Introduction to protein structure and structural biology techniques to study structure/function relationships, with an emphasis on x-ray crystallography.
This document provides an overview of structural biology, including its focus on determining the molecular structures of biological macromolecules like proteins and nucleic acids. It discusses key developments in the field, such as Watson and Crick's discovery of DNA's structure, and the two main experimental approaches used today: X-ray crystallography and nuclear magnetic resonance spectroscopy. X-ray crystallography involves crystallizing a protein and analyzing its diffraction pattern to determine structure, while NMR studies protein structures in solution using magnetic fields. Both techniques have benefits and limitations related to the size of molecule that can be studied and experimental requirements.
structural biology-Protein structure function relationshipMSCW Mysore
Structural biology determines protein structures using x-ray crystallography. X-rays are diffracted by regular arrays of atoms in protein crystals to produce patterns that reveal atomic structures. Protein structures determine their functions, such as catalytic activity. Understanding protein structures is essential to elucidating their roles in cellular processes.
The document discusses experimental and computational methods for protein structure prediction. Experimental methods like NMR, X-ray crystallography, and cryo-EM can accurately determine protein structure but require isolating and crystallizing the protein. Computational methods like homology modeling, ab initio modeling, and threading/folding predict structure from sequence alone and are less accurate but do not require crystallization. Computational methods work best when a template structure is available from experimental data. While experimental methods are very accurate, they are also costly and difficult for large numbers of proteins, making computational methods a useful complement despite being less accurate.
The document discusses protein-protein interactions (PPIs) and methods used to study them. It defines PPIs as physical contacts between two or more proteins through biochemical or electrostatic forces. It describes different types of PPIs including homo-oligomers, hetero-oligomers, covalent and non-covalent interactions. Common methods to study PPIs are also summarized, such as yeast two-hybrid systems, co-immunoprecipitation, and protein interaction databases. The applications and importance of PPI research are mentioned including roles in various cellular processes and diseases.
Yeast two-hybrid is based on the reconstitution of a functional transcription factor (TF) when two proteins or polypeptides of interest interact. Upon interaction between the bait and the prey, the DBD and AD are brought in close proximity and a functional TF is reconstituted upstream of the reporter gene.
The document discusses various computational methods for predicting the three-dimensional structure of proteins from their amino acid sequences. It describes homology modeling, which predicts structures based on known protein structural templates that share sequence homology. It also covers threading/fold recognition and ab initio modeling, which predict structures without templates by using physicochemical principles or energy minimization approaches. Key steps and programs used in each method are outlined.
The key forces stabilizing nucleic acid structure are hydrogen bonding, base stacking, hydrophobic interactions, and ionic bonding. Hydrogen bonding occurs between complementary nucleotide bases on opposite strands. Base stacking involves hydrophobic interactions between stacked aromatic nucleotide bases within each strand. Hydrophobic interactions bury hydrophobic bases in the core of the double helix, increasing stability. Ionic interactions between phosphate groups and counterions in solution also stabilize the structure.
This document provides an overview of structural biology, including its focus on determining the molecular structures of biological macromolecules like proteins and nucleic acids. It discusses key developments in the field, such as Watson and Crick's discovery of DNA's structure, and the two main experimental approaches used today: X-ray crystallography and nuclear magnetic resonance spectroscopy. X-ray crystallography involves crystallizing a protein and analyzing its diffraction pattern to determine structure, while NMR studies protein structures in solution using magnetic fields. Both techniques have benefits and limitations related to the size of molecule that can be studied and experimental requirements.
structural biology-Protein structure function relationshipMSCW Mysore
Structural biology determines protein structures using x-ray crystallography. X-rays are diffracted by regular arrays of atoms in protein crystals to produce patterns that reveal atomic structures. Protein structures determine their functions, such as catalytic activity. Understanding protein structures is essential to elucidating their roles in cellular processes.
The document discusses experimental and computational methods for protein structure prediction. Experimental methods like NMR, X-ray crystallography, and cryo-EM can accurately determine protein structure but require isolating and crystallizing the protein. Computational methods like homology modeling, ab initio modeling, and threading/folding predict structure from sequence alone and are less accurate but do not require crystallization. Computational methods work best when a template structure is available from experimental data. While experimental methods are very accurate, they are also costly and difficult for large numbers of proteins, making computational methods a useful complement despite being less accurate.
The document discusses protein-protein interactions (PPIs) and methods used to study them. It defines PPIs as physical contacts between two or more proteins through biochemical or electrostatic forces. It describes different types of PPIs including homo-oligomers, hetero-oligomers, covalent and non-covalent interactions. Common methods to study PPIs are also summarized, such as yeast two-hybrid systems, co-immunoprecipitation, and protein interaction databases. The applications and importance of PPI research are mentioned including roles in various cellular processes and diseases.
Yeast two-hybrid is based on the reconstitution of a functional transcription factor (TF) when two proteins or polypeptides of interest interact. Upon interaction between the bait and the prey, the DBD and AD are brought in close proximity and a functional TF is reconstituted upstream of the reporter gene.
The document discusses various computational methods for predicting the three-dimensional structure of proteins from their amino acid sequences. It describes homology modeling, which predicts structures based on known protein structural templates that share sequence homology. It also covers threading/fold recognition and ab initio modeling, which predict structures without templates by using physicochemical principles or energy minimization approaches. Key steps and programs used in each method are outlined.
The key forces stabilizing nucleic acid structure are hydrogen bonding, base stacking, hydrophobic interactions, and ionic bonding. Hydrogen bonding occurs between complementary nucleotide bases on opposite strands. Base stacking involves hydrophobic interactions between stacked aromatic nucleotide bases within each strand. Hydrophobic interactions bury hydrophobic bases in the core of the double helix, increasing stability. Ionic interactions between phosphate groups and counterions in solution also stabilize the structure.
This document provides an overview of structural bioinformatics and protein structure. It discusses the basics of protein structure including the primary, secondary, tertiary, and quaternary levels. Methods for determining three-dimensional protein structure such as X-ray crystallography and NMR spectroscopy are described. The document also covers protein structure visualization tools, comparison of protein structures to determine evolutionary relationships, and classification databases like SCOP and CATH. The key applications of structural bioinformatics are structure-based function prediction and aiding structural genomics projects.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
The STRING database aims to provide a comprehensive global protein-protein interaction network. The latest version covers over 5000 organisms and allows users to upload entire genome-wide datasets. It implements classification systems like Gene Ontology and KEGG for gene-set enrichment analysis. STRING collects and integrates data from various sources, including experimental repositories, text mining, and predicted interactions based on genomic features. Users can access and visualize the interaction data through a web interface or API.
The Molecular Modeling Database (MMDB) is a database hosted by the National Center for Biotechnology Information that contains over 28,000 experimentally determined 3D structures of biomolecules including proteins and nucleic acids derived from the Protein Data Bank, excluding theoretical models. It facilitates computation and links structures to other data types. Each record cross-references its source PDB file. The database contains molecular structures, biological activity data, experimental data, chemical properties, and annotations to aid researchers. Examples of widely used molecular modeling databases discussed are the Protein Data Bank, PubChem, and RCSB Ligand Explorer.
The document describes several key databases within the KEGG resource, including:
- The PATHWAY database containing molecular network maps of metabolic and genetic pathways.
- The BRITE database providing hierarchical classifications of biological systems beyond what is shown in pathways.
- The LIGAND database consisting of chemical compounds, carbohydrates, reactions, and enzyme information.
KEGG aims to comprehensively capture biological knowledge through integrated databases covering genomes, pathways, diseases and drugs.
Protein structure prediction involves computational methods to determine a protein's 3D structure from its amino acid sequence. Ab initio methods use physics-based calculations of potential energy to predict the most stable conformation. Comparative methods leverage databases of known protein structures, searching for sequences with similar folds. Homology modeling relies on the assumption that related proteins share similar folds, allowing prediction based on matches to distant evolutionary relatives. Protein threading compares local segments of the sequence to structural fragments in databases.
The SCOP database classifies protein structures hierarchically and describes evolutionary relationships between proteins. It was created in 1994 at the Centre for Protein Engineering and is maintained manually. SCOP links to the Protein Data Bank to obtain structural classifications for each protein structure directly and can also be searched to find a protein's structural class, fold, and domain information.
The document discusses various methods for structurally aligning proteins, including combinatorial extension, VAST, DALI, SSAP, and TM-align. It also describes Ramachandran plots, which show allowed and favored phi/psi dihedral angle combinations for protein backbone chains based on steric constraints. Structural alignment methods are useful for detecting evolutionary relationships between proteins with low sequence similarity. Ramachandran plots help validate protein structures by identifying conformations not allowed by steric hindrance.
Recognizable folding pattern of proteins involved two or more elements of its secondary structure.
In a motif two elements of secondary structure folded against each other.
Because it is falling between secondary and tertiary structure and describe small part of protein or entire polypeptide chain.
The document discusses protein folding, which is the process by which a polypeptide chain folds into its characteristic and functional three-dimensional structure. It describes the four levels of protein structure: primary, secondary, tertiary, and quaternary. Key drivers of folding are the hydrophobic effect and formation of hydrogen bonds. Chaperone proteins assist in protein folding in vivo. Factors such as mutations, errors in synthesis, environmental stresses, and aging can cause proteins to misfold and aggregate, which is associated with various diseases. Cells use molecular chaperones and protein degradation systems to prevent aggregation, but these become less effective with age.
This document discusses site-specific recombination, including the structures and mechanisms involved. It describes two classes of recombinases - tyrosine recombinases and serine recombinases. Tyrosine recombinases involve cleavage of DNA through formation of a protein-DNA bond using a tyrosine residue. Serine recombinases utilize a phosphoserine bond between DNA and a conserved serine residue. The document provides examples of applications for site-specific recombination such as tracking cell lineage, altering gene expression, and targeted gene knockout.
Homology modeling is a technique used to predict the 3D structure of a protein based on the alignment of its amino acid sequence to known protein structures. It relies on the observation that structure is more conserved than sequence during evolution. The key steps in homology modeling include: 1) identifying a template structure through sequence alignment tools like BLAST, 2) correcting any errors in the initial alignment, 3) generating the protein backbone based on the template structure, 4) modeling any loops or missing regions, 5) adding side chains, 6) optimizing the model structure energetically, and 7) validating that the final model matches the template structure and has correct stereochemistry. Homology modeling is useful for applications like structure-based drug design
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
METHODS TO DETERMINE PROTEIN STRUCTURE Sabahat Ali
This document discusses several methods for determining protein structure: Edman degradation, X-ray crystallography, Western blotting, SDS-PAGE, 2D gel electrophoresis, and isoelectric focusing. Edman degradation involves chemically removing amino acids from the N-terminus of a protein one by one to determine the sequence. X-ray crystallography provides high-resolution 3D structures of proteins. Western blotting identifies specific proteins in a sample using antibodies. SDS-PAGE and 2D gels separate proteins by size and electric charge properties. Isoelectric focusing separates proteins based on their isoelectric points.
Gene expression in eukaryotes is regulated through multiple mechanisms at the transcriptional and post-transcriptional levels. These mechanisms allow for adaptation, tissue specificity, and development. Regulation occurs through chromatin remodeling, enhancers/repressors, locus control regions, gene amplification, rearrangement, and alternative RNA processing. Key differences between prokaryotic and eukaryotic gene expression include larger eukaryotic genomes, different cell types, lack of operons, chromatin structure, and uncoupled transcription/translation.
DNA
INTRODUCTION
CHEMICAL COMPOSITION
NUCLEOSIDES & NUCLEOTIDES
DNA REPAIR
INTRODUCTION
TYPES OF DNA REPAIR
I)DIRECT REPAIR SYSTEM,
II)BASE EXCISION REPAIR,
III)NUCLEOTIDE EXCISION REPAIR,
IV)MISMATCH REPAIR,
V)RECOMBINATION REPAIR,
DEFECTS IN DNA REPAIR UNDERLIE HUMAN DISEASE
DNA RECOMBINATION
INTRODUCTION
MECHANISM OF DNA RECOMBINATION
TYPES OF RECOMBINATION
I) HOMOLOGOUS RECOMBINATION
MODELS FOR HOMOLOGOUS RECOMBINATION:-
I)HOLLIDAY MODEL,
II)MESSELSON AND RADDING MODEL,
III)DOUBLE STRAND BREAK MODEL,
GENE CONVERSION
II) NON-HOMOLOGOUS RECOMBINATION,
i) SITE SPECIFIC RECOMBINATION,
ii)TRANSPOSITIONAL RECOMBINATION.,
Protein Folding-biophysical and cellular aspects, protein denaturationAnishaMukherjee5
Protein folding is the physical process by which a protein chain acquires its native 3-dimensional structure, a conformation that is usually biologically functional, in an expeditious and reproducible manner.
This document discusses restriction mapping and primer design. It describes restriction mapping as a way to characterize unknown DNA using restriction enzymes that cut DNA at specific sequences. It outlines criteria for designing effective primers for applications like PCR, including length, GC content, specificity, and melting temperature. Computer programs can help design primers and generate in silico restriction maps from DNA sequences. Degenerate primers allow amplification of related gene sequences.
X-ray crystallography is a technique used to determine the atomic and molecular structure of crystals. It works by firing X-rays at a crystal and analyzing the diffracted rays. This allows researchers to construct a 3D model of the density of electrons within the crystal, revealing where atoms are located. Well-ordered protein crystals are required, as X-ray scattering from a single molecule would be too weak. Researchers grow crystals and collect diffraction data, which are then used to calculate atomic positions via Fourier transforms. This technique has determined over 85% of known protein structures and is invaluable for understanding functions at the molecular level.
This document discusses motifs and domains in proteins. It defines motifs as short conserved regions related to function, such as binding sites, that are not detectable by sequence searches. There are sequence motifs consisting of nucleotide or amino acid patterns, and structural motifs formed by amino acid spatial arrangements. Domains are stable, independently folding units of proteins that determine structure and function. Both motifs and domains are useful for classifying protein families and have structural and functional roles, though domains are more stable independently. Motifs and domains form through interactions of alpha helices and beta sheets and have similarities, but domains mainly determine unique functions while motifs mainly provide structural roles within families.
This document discusses classifying and characterizing proteins of unknown function in the fission yeast Schizosaccharomyces pombe. It begins by showing the progress that has been made in characterizing known proteins, but that the number of unknown proteins is decreasing only gradually. It then discusses classifying proteins as unknown if their broad cellular role is not known. The rest of the document proposes different methods for predicting the potential functions of unknown proteins, such as identifying informative features, clustering unknown proteins based on similar features, and predicting potential functions by finding the best matching known proteins. It emphasizes the need for high-quality curation and experimental data to make accurate predictions.
Computational Prediction Of Protein-1.pptxashharnomani
This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
This document provides an overview of structural bioinformatics and protein structure. It discusses the basics of protein structure including the primary, secondary, tertiary, and quaternary levels. Methods for determining three-dimensional protein structure such as X-ray crystallography and NMR spectroscopy are described. The document also covers protein structure visualization tools, comparison of protein structures to determine evolutionary relationships, and classification databases like SCOP and CATH. The key applications of structural bioinformatics are structure-based function prediction and aiding structural genomics projects.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
The STRING database aims to provide a comprehensive global protein-protein interaction network. The latest version covers over 5000 organisms and allows users to upload entire genome-wide datasets. It implements classification systems like Gene Ontology and KEGG for gene-set enrichment analysis. STRING collects and integrates data from various sources, including experimental repositories, text mining, and predicted interactions based on genomic features. Users can access and visualize the interaction data through a web interface or API.
The Molecular Modeling Database (MMDB) is a database hosted by the National Center for Biotechnology Information that contains over 28,000 experimentally determined 3D structures of biomolecules including proteins and nucleic acids derived from the Protein Data Bank, excluding theoretical models. It facilitates computation and links structures to other data types. Each record cross-references its source PDB file. The database contains molecular structures, biological activity data, experimental data, chemical properties, and annotations to aid researchers. Examples of widely used molecular modeling databases discussed are the Protein Data Bank, PubChem, and RCSB Ligand Explorer.
The document describes several key databases within the KEGG resource, including:
- The PATHWAY database containing molecular network maps of metabolic and genetic pathways.
- The BRITE database providing hierarchical classifications of biological systems beyond what is shown in pathways.
- The LIGAND database consisting of chemical compounds, carbohydrates, reactions, and enzyme information.
KEGG aims to comprehensively capture biological knowledge through integrated databases covering genomes, pathways, diseases and drugs.
Protein structure prediction involves computational methods to determine a protein's 3D structure from its amino acid sequence. Ab initio methods use physics-based calculations of potential energy to predict the most stable conformation. Comparative methods leverage databases of known protein structures, searching for sequences with similar folds. Homology modeling relies on the assumption that related proteins share similar folds, allowing prediction based on matches to distant evolutionary relatives. Protein threading compares local segments of the sequence to structural fragments in databases.
The SCOP database classifies protein structures hierarchically and describes evolutionary relationships between proteins. It was created in 1994 at the Centre for Protein Engineering and is maintained manually. SCOP links to the Protein Data Bank to obtain structural classifications for each protein structure directly and can also be searched to find a protein's structural class, fold, and domain information.
The document discusses various methods for structurally aligning proteins, including combinatorial extension, VAST, DALI, SSAP, and TM-align. It also describes Ramachandran plots, which show allowed and favored phi/psi dihedral angle combinations for protein backbone chains based on steric constraints. Structural alignment methods are useful for detecting evolutionary relationships between proteins with low sequence similarity. Ramachandran plots help validate protein structures by identifying conformations not allowed by steric hindrance.
Recognizable folding pattern of proteins involved two or more elements of its secondary structure.
In a motif two elements of secondary structure folded against each other.
Because it is falling between secondary and tertiary structure and describe small part of protein or entire polypeptide chain.
The document discusses protein folding, which is the process by which a polypeptide chain folds into its characteristic and functional three-dimensional structure. It describes the four levels of protein structure: primary, secondary, tertiary, and quaternary. Key drivers of folding are the hydrophobic effect and formation of hydrogen bonds. Chaperone proteins assist in protein folding in vivo. Factors such as mutations, errors in synthesis, environmental stresses, and aging can cause proteins to misfold and aggregate, which is associated with various diseases. Cells use molecular chaperones and protein degradation systems to prevent aggregation, but these become less effective with age.
This document discusses site-specific recombination, including the structures and mechanisms involved. It describes two classes of recombinases - tyrosine recombinases and serine recombinases. Tyrosine recombinases involve cleavage of DNA through formation of a protein-DNA bond using a tyrosine residue. Serine recombinases utilize a phosphoserine bond between DNA and a conserved serine residue. The document provides examples of applications for site-specific recombination such as tracking cell lineage, altering gene expression, and targeted gene knockout.
Homology modeling is a technique used to predict the 3D structure of a protein based on the alignment of its amino acid sequence to known protein structures. It relies on the observation that structure is more conserved than sequence during evolution. The key steps in homology modeling include: 1) identifying a template structure through sequence alignment tools like BLAST, 2) correcting any errors in the initial alignment, 3) generating the protein backbone based on the template structure, 4) modeling any loops or missing regions, 5) adding side chains, 6) optimizing the model structure energetically, and 7) validating that the final model matches the template structure and has correct stereochemistry. Homology modeling is useful for applications like structure-based drug design
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
METHODS TO DETERMINE PROTEIN STRUCTURE Sabahat Ali
This document discusses several methods for determining protein structure: Edman degradation, X-ray crystallography, Western blotting, SDS-PAGE, 2D gel electrophoresis, and isoelectric focusing. Edman degradation involves chemically removing amino acids from the N-terminus of a protein one by one to determine the sequence. X-ray crystallography provides high-resolution 3D structures of proteins. Western blotting identifies specific proteins in a sample using antibodies. SDS-PAGE and 2D gels separate proteins by size and electric charge properties. Isoelectric focusing separates proteins based on their isoelectric points.
Gene expression in eukaryotes is regulated through multiple mechanisms at the transcriptional and post-transcriptional levels. These mechanisms allow for adaptation, tissue specificity, and development. Regulation occurs through chromatin remodeling, enhancers/repressors, locus control regions, gene amplification, rearrangement, and alternative RNA processing. Key differences between prokaryotic and eukaryotic gene expression include larger eukaryotic genomes, different cell types, lack of operons, chromatin structure, and uncoupled transcription/translation.
DNA
INTRODUCTION
CHEMICAL COMPOSITION
NUCLEOSIDES & NUCLEOTIDES
DNA REPAIR
INTRODUCTION
TYPES OF DNA REPAIR
I)DIRECT REPAIR SYSTEM,
II)BASE EXCISION REPAIR,
III)NUCLEOTIDE EXCISION REPAIR,
IV)MISMATCH REPAIR,
V)RECOMBINATION REPAIR,
DEFECTS IN DNA REPAIR UNDERLIE HUMAN DISEASE
DNA RECOMBINATION
INTRODUCTION
MECHANISM OF DNA RECOMBINATION
TYPES OF RECOMBINATION
I) HOMOLOGOUS RECOMBINATION
MODELS FOR HOMOLOGOUS RECOMBINATION:-
I)HOLLIDAY MODEL,
II)MESSELSON AND RADDING MODEL,
III)DOUBLE STRAND BREAK MODEL,
GENE CONVERSION
II) NON-HOMOLOGOUS RECOMBINATION,
i) SITE SPECIFIC RECOMBINATION,
ii)TRANSPOSITIONAL RECOMBINATION.,
Protein Folding-biophysical and cellular aspects, protein denaturationAnishaMukherjee5
Protein folding is the physical process by which a protein chain acquires its native 3-dimensional structure, a conformation that is usually biologically functional, in an expeditious and reproducible manner.
This document discusses restriction mapping and primer design. It describes restriction mapping as a way to characterize unknown DNA using restriction enzymes that cut DNA at specific sequences. It outlines criteria for designing effective primers for applications like PCR, including length, GC content, specificity, and melting temperature. Computer programs can help design primers and generate in silico restriction maps from DNA sequences. Degenerate primers allow amplification of related gene sequences.
X-ray crystallography is a technique used to determine the atomic and molecular structure of crystals. It works by firing X-rays at a crystal and analyzing the diffracted rays. This allows researchers to construct a 3D model of the density of electrons within the crystal, revealing where atoms are located. Well-ordered protein crystals are required, as X-ray scattering from a single molecule would be too weak. Researchers grow crystals and collect diffraction data, which are then used to calculate atomic positions via Fourier transforms. This technique has determined over 85% of known protein structures and is invaluable for understanding functions at the molecular level.
This document discusses motifs and domains in proteins. It defines motifs as short conserved regions related to function, such as binding sites, that are not detectable by sequence searches. There are sequence motifs consisting of nucleotide or amino acid patterns, and structural motifs formed by amino acid spatial arrangements. Domains are stable, independently folding units of proteins that determine structure and function. Both motifs and domains are useful for classifying protein families and have structural and functional roles, though domains are more stable independently. Motifs and domains form through interactions of alpha helices and beta sheets and have similarities, but domains mainly determine unique functions while motifs mainly provide structural roles within families.
This document discusses classifying and characterizing proteins of unknown function in the fission yeast Schizosaccharomyces pombe. It begins by showing the progress that has been made in characterizing known proteins, but that the number of unknown proteins is decreasing only gradually. It then discusses classifying proteins as unknown if their broad cellular role is not known. The rest of the document proposes different methods for predicting the potential functions of unknown proteins, such as identifying informative features, clustering unknown proteins based on similar features, and predicting potential functions by finding the best matching known proteins. It emphasizes the need for high-quality curation and experimental data to make accurate predictions.
Computational Prediction Of Protein-1.pptxashharnomani
This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
The Past, Present and Future of Knowledge in Biologyrobertstevens65
This document discusses the past, present, and future of knowledge representation in biology. It covers how ontologies have grown significantly in use over time for organizing biological facts and data. However, ontologies only represent part of biological knowledge, and there is potential to do more by connecting different types of knowledge, generating natural language descriptions, and representing knowledge about experiments and workflows in addition to entities and relationships. The document argues that biological knowledge representation has advanced beyond ontologies alone and could benefit from additional types of knowledge representation and reasoning.
Cell structure and function / dental implant courses by Indian dental academy Indian dental academy
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Microtubules are an essential component of the cytoskeleton that help maintain cellular structure and enable various cellular movements and transport processes. They are composed of alpha and beta tubulin subunits that polymerize to form protofilaments which assemble into microtubules. Microtubules are dynamic structures that undergo growth and shrinkage. Recent research has provided new insights into how microtubules and motor proteins self-organize to form larger cellular structures like spindles during cell division. Additionally, a protein called DRG1 has been found to promote microtubule polymerization and bundling, helping to regulate spindle assembly and dynamics.
The document discusses two scientific articles about cellular switches and their relevance to neurodegenerative diseases. It describes a study that found a protein that acts as an effector for the G protein FIhF in bacteria and compares this to the complex formed by SRP54 and FtsY. It also outlines research that resolved the 3D structure of the elongator protein complex which is implicated in certain neurological conditions.
In this talk we will go back to basics with ontologies and from that project forwards to their future. I’ll base most of what I talk about on my experience in bio-ontologies, but most experience will be applicable in many domains; most domains are not as special as they think.
When it comes down to the basics, we need to know what our data represent or mean; that is where ontologies come into play; We need to know what we’re talking about. Once we have that clear we can proceed. There is much that one can do with data once we know what it means.
We can exploit those data through knowing what it represents. We can exploit these data better if our ontologies are also better. In taking this simple point of view forwards, I will use this talk to establish a set of principles for ontologists.
Gene expression in eukaryotes is regulated at multiple stages including transcription, RNA processing, translation, and protein modification. Regulation allows different genes to be expressed in different cell types through the actions of transcription factors and regulatory RNAs. Differences in gene regulation between species help explain variations in form and function even when genomes are highly similar.
Gene expression in eukaryotes is regulated at multiple stages including transcription, RNA processing, translation, and protein modification. Regulation allows different genes to be expressed in different cell types through the actions of transcription factors and other proteins that control if and how genes are expressed. Differences in gene regulation between species help explain variations in form and function even when genomes are highly similar at the DNA level.
Module 2 OverviewThe Cell and EnergyEvery tissue in every body.docxannandleola
Module 2 Overview
The Cell and Energy
Every tissue in every body of every organism is made of cells. The complexity of life ranges from the single-celled amoeba to large mammals containing innumerable cells, with many distinct types performing specific functions. This module will introduce you to the structure and function of cells and will describe the two major types—prokaryotic and eukaryotic cells. You will then be introduced to the various types and functions of specific protein molecules called enzymes, which facilitate cellular processes.
All organisms require energy to perform the functions that sustain their lives. The chemical reactions at the heart of these functions are referred to as an organism's metabolism. The biochemical or metabolic pathways that these reactions take are a series of linked reactions that transform energy into a usable form.
Learning Objectives
Upon completion of this module, you should be able to:
2A
Identify the typical organelles associated with eukaryotic cells.
2B
Examine the differences in organelles found in prokaryotic and eukaryotic cells.
2C
Describe the function of each of the organelles associated with eukaryotic cells.
2D
Name examples of organisms composed of prokaryotic and eukaryotic cells.
3A
State the controlled methods by which materials can be transported through a cell membrane.
3B
Contrast diffusion, osmosis, and dialysis.
3C
Classify the components and molecular parts of a typical cell membrane.
3D
Explain why cells are small.
3E
State what environmental factors are able to alter enzyme activity.
3F
Describe to which group of organic molecules enzymes belong.
3G
Explain why enzymes are so important to all organisms.
3H
Describe what happens when an enzyme and a substrate combine.
3I
Contrast active site and binding site.
3J
Define the term, denature, and provide negative and positive feedback.
3K
Describe enzymatic competition.
3L
Relate the shape of an enzyme to its ability to help in chemical reaction.
3M
Describe why enzymes work in some situations and not in others.
3N
Contrast cofactors, vitamins, and coenzymes.
3O
Explain the importance of ATP.
3P
Describe how the proton pump mechanism generates ATP.
Module 2 Reading Assignment
Enger, E. D., Ross, F. C., & Bailey, D. B. (2012). Concepts in biology (14th ed.). New York: McGraw-Hill. Chapters 4 and 5.
Lecture Notes
The Cell and Energy
Prokaryotic cells are smaller than eukaryotic cells. While eukaryotic cells can be much larger, they are still small. Cell size is important for a couple different reasons. Logistically, small cells are easier to replace. This is why they replicate and split. When cells become too large, it is more beneficial to split off and remain small and effective than to become too large, become less effective, and become harder to replace. The effectiveness of absorption and expulsion through the plasma membrane is another reason why cells are small. Absorption and expulsion beco ...
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
Intro to in silico drug discovery 2014Lee Larcombe
This document provides an overview of in silico drug design with a focus on safety and efficacy. It discusses using computational approaches to model molecular interactions for small molecule drugs and biologics. For small molecules, it describes obtaining protein structures, simulating binding via docking, and considering absorption and toxicity. For biologics like antibodies, it discusses engineering for reduced immunogenicity and improved half-life via Fc modifications. The goal is developing safer, more effective therapies through computational analysis and protein design.
This document discusses using metabolic network data to develop a framework for understanding cellular networks. It begins by stating that while biological data is being generated faster than ever, it needs to be converted into usable knowledge. The author aims to create a generalizable framework for understanding cellular networks using metabolism as a test case. Metabolic networks are constructed from KEGG databases by connecting substrates to products. Predictions of new reactions are made by comparing organism protein sequences to enzyme sequences for each reaction. Predicted changes are validated by comparing them to updates made to the KEGG databases over time.
The document discusses recent findings about RNA structure and function. It summarizes that scientists have found that unlike previous beliefs, single strands of RNA do not form knots in their structure but rather take on geometric configurations. This facilitates protein synthesis and faithful reproduction of genetic information. The document also discusses how RNA itself can proofread during translation to detect mistakes and signal to enzymes to fix or discard errors, demonstrating an auto-regulation system. Understanding RNA and its role is important for medicine to better understand cellular processes and identify solutions to diseases involving protein synthesis and translation errors.
Why Proteins Are Essential For Cellular FunctionBeth Salazar
Here are the key ways a cell membrane is suited to its functions:
- The fluid mosaic structure allows for flexibility and permeability while maintaining integrity. The phospholipid bilayer provides a barrier to control what enters and exits the cell, while still allowing movement of some substances.
- Integral and peripheral proteins embedded in the phospholipid bilayer carry out important functions like transporting molecules, signaling, and identity. Transport proteins allow selective passage of nutrients, waste, and signals across the membrane.
- The phospholipid tails are nonpolar to form a hydrophobic barrier, preventing everything from freely diffusing across. The polar heads face the aqueous cytosol and extracellular environments. This structure prevents unwanted substances from entering while enabling transport.
- Ch
The document provides details about a three day lesson plan for middle school EL students on cells. Day one involves introducing cell vocabulary through a PowerPoint presentation and having students label a graphic organizer and cell model. Day two has students making edible cell models using candy to represent organelles. Day three involves students describing the structures and functions of organelles to peers and explaining similarities and differences between plant and animal cells. The lesson aims to help students understand cell parts and their functions.
Cell fractionation is a technique used to isolate cell organelles. It involves homogenizing cells and separating the components via differential centrifugation or density gradient centrifugation. This allows organelles to be identified and studied individually using marker enzymes that are specific to each organelle. Cell fractionation has provided insights into the structure and function of organelles and their role in cellular processes.
Protein folding is the process by which a protein goes from an unfolded state to its biologically active three-dimensional structure. It is important to understand protein folding to help predict protein structures from sequence alone and to understand diseases caused by protein misfolding. Proteins typically fold through progressive formation of native-like structures rather than through a random search. Molecular chaperones help other proteins fold within cells. Misfolded proteins can form amyloid fibrils associated with diseases. Computational methods aim to predict protein structures from sequence using fragment libraries and modeling protein energy landscapes. Protein design techniques aim to computationally modify protein sequences to achieve desired stabilities, functions, and binding properties.
Personal notes:
- Section 1 : Cell
-- What is a Cell?
-- What is DNA?
-- What is mitochondrial DNA?
-- What is a gene?
-- What is a chromosome?
-- How many chromosomes do people have?
- Section 2 : Proteins
-- What are proteins and what do they do?
-- How do genes direct the production of proteins?
-- Can genes be turned on and off in cells?
-- What is epigenome?
-- How do cells divide?
-- How do genes control the growth and division of cells?
-- How do genetics indicate the location of a gene?
- Section 3: Genetic Mapping
-- What is genetic mapping?
-- How do researchers create a genetic map?
-- What are genetic markers?
This presentation was created by Ioanna Leontiou and it is intended as a creative and flexible tool for students on Biological sciences who focus on the chromosome segregation. It is created to facilitate students performing research projects in our lab (especially during Covid restrictions), but it is suitable for every student who wants to learn more about chromosomes and the molecular mechanism controlling chromosome segregation. The presentation includes a generic overview of the cell division, illustrates the chromosome structure and provides molecular details of the spindle assembly checkpoint, an important pathway that ensures high fedility of chromosome segregation through mitosis. It also includes an introduction to some of the molecular biology techniques used in a yeast lab and incoporates some fluorescent microscopy images/videos. At the end of the presentantion there is a list of open access scientific publications for further reading on the the molecular mechanism of spindle checkpoint and some links of some very interesting sites, which include a range of videos on laboratory molecular biology techniques, research talks and guided papers. The purpose of this presentantion is to create a piece of work that students could return to when needed. Diagramms and illustrations are also encouranged to be used by scientists, science communicators and educators.
This presentation is licensed under a Creative Common Attribution-ShareAlike 4.0 (CC BY-SA 4.0), unless otherwise stated on the specific slide.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
2. • what is structural biology?
• why is it important?
• how do we do it?
3. Structural biology
• THE WHAT: A scientific discipline that looks at the molecular structure of biological
macromolecules and how that STRUCTURE relates to its FUNCTION
• THE WHY: Answers questions like:
• Why do molecules work the way they do?
• What specifically makes one (or a group of them) well-suited for a particular task?
• Can we manipulate them to work even better or do other things?
• THE HOW:
• incorporates principles and techniques of:
MOLECULAR
BIOLOGY
BIOCHEMISTRY BIOPHYSICS
4. • what is structural biology?
• why is it important?
• how do we do it?
5. Structure & function are
intimately connected
We can exploit this relationship to learn about
function from structure and structure from
function
6. What do we mean by structure?
Primary structure
secondary structure
tertiary structure
quaternary structure
Proteins have multiple
layers of structure
underlying their final 3D
shape
7. But where does this structure
come from…
DNA contains the instructions to make amino acids, which
are a protein’s building blocks
R
8. AMINO ACIDS
Amino acids are the
building blocks of
proteins & there are 20
common ones
They all have the same
generic backbone
HYDROGEN
NITROGEN
CARBON
OXYGEN
9. • But they have unique
side chains (aka R
groups)
R
AMINO ACIDS
10. AMINO ACIDS
Different side chains
have different
properties
SMALL &
FLEXIBLE
BIG &
BULKY
POSITIVELY
CHARGED
NEGATIVELY
CHARGED
RWATER-LOVING
HYDROPHILIC WATER-AVOIDING
HYDROPHOBIC
11. AMINO ACIDS
The side chains’ properties influence how the
proteins fold
put us next to
each other
put me at the
surface
hide me in the
middle
I don’t bend that
way…
Don’t expect me
to stay still! SMALL &
FLEXIBLE
BIG &
BULKY POSITIVELY
CHARGED
NEGATIVELY
CHARGED
WATER-LOVING
HYDROPHILIC
WATER-AVOIDING
HYDROPHOBIC
12. PRIMARY
STRUCTUREAmino acids link together to form polypeptide chains - this
is your PRIMARY STRUCTURE
A protein’s gene contains the
instructions for what order to put
them in
13. SECONDARY
STRUCTUREInteractions between the backbone leads to SECONDARY STRUCT
can maximize favorable interactions by
folding into a couple common motifs
alpha helix
(α helix)
beta strands
15. QUARTERNARY
STRUCTURE
Some proteins are made up of multiple
chains, and interactions between the side
chains of different chains lead to
QUATERNARY STRUCTURE
17. x-ray crystallography
nuclear magnetic resonance (NMR)
electron microscopy
LOOK AT ITS
STRUCTURE
CHANGE IT
TEST ITS
FUNCTION
binding assays
activity assays
site-directed mutagenesis; truncations
What’s it “supposed” to do?
How can we measure that?
Can it still do what it’s “supposed to” do? Can it do new things?
18. Thought exercise
• Suppose I tell you what an object does and ask you how it
works
• a structural biologist will want to know what it looks like.
Why?
• Consider the converse: I show you an object and ask you
what it does
19. Specific functions are often
carried out by specific parts
DOMAINS
open a beer bottle
uncork a bottle of wine
slice open an envelope
20. This is true for proteins too
P
O
H
3’ 5’
O
H
P
3’ 5’
open a beer bottle
uncork a bottle of wine
slice open an envelope
But they often face more difficult problems…
PNKP
DNA Ligase
21. What parts do what?
P
O
H
O
H
P
3’ 5’
3’ 5’
Polynucleotide kinase phosphatase (PNKP)
Bernstein et al., Molecular Cell, 2005
23. I wonder what that does…
Structural biologists use mutations to examine function
24. Mutations to different parts can have different
effects that can tell us about what that part
does
25. We can do something similar
with proteins
• But we need protein…
• We can introduce the gene for the protein into bacteria or
insect cells
• We grow those cells & those cells make lots of the protein,
which we can purify
26. Since we’re introducing the gene, we have the opportunity to
make changes to it…
Control the gene, control the
protein…
Changes to the gene change the primary structure, which can then
affect the higher structural levels & perhaps the function
27. We can add a specific sequence of amino acids to act as a
“tag” so we can purify it more easily
Control the gene, control the
protein…
28. Control the gene, control the
protein…
We can mutate specific amino acids to test for function
SITE-DIRECTED
MUTAGENESIS
This can identify “active sites” where the action happens and/or
binding sites for other molecules
29. Control the gene, control the
protein…
We can truncate, or shorten, the ends, or delete pieces from
the middle
This can help with crystallization, as we’ll see later…
30. A more biological example
P
O
H
P P
3’ 5’
3’ 5’
Polynucleotide kinase phosphatase
(PNKP)
31. A more biological example
P
O
H
O
H
O
H
3’ 5’
3’ 5’
Polynucleotide kinase phosphatase (PNKP)
32. A more biological example
P
O
H
O
H
P
3’ 5’
3’ 5’
Polynucleotide kinase phosphatase (PNKP)
34. If we know what parts do what, we
can use structure-guided design…
• What if you want to open your bottles without worrying
about cutting your finger?
35. If we know what parts do what, we
can use structure-guided design…
• used to develop protease inhibitors for HIV
https://www.sciencedirect.com/science/article/pii/S0022283617303157
36. In addition to what does what, you
can figure out “how” it does it
Location, location, location
37. In addition to what does what, you
can figure out “how” it does it
Not all mutations are created equal
39. Changing existing molecules
• you can buy mutants of T4
PNK that have kinase activity,
but no phosphatase activity
• great for radiolabeling RNA so
you can track it!
40. In addition to what does what, you
can figure out “how” it does it
Integrate information from different types of experiments
48. But getting crystals is rarely
easy…
• to crystallize, proteins must
“freeze” in a precisely
ordered manner
• but what if there’s a “loose
screw”?
Proteins move around and can exist in different
conformations
Not all of which are equally informative…
52. Look at the pieces separately
Bernstein et al., Molecular Cell, 2005
53. Get help from a homolog!
• sometimes similar proteins from the same species or other
species crystallize more easily
this is actually the murine (mouse) version
can be closely related
can be more distantly
related
they can have very different
sequences but similar
structures
54. Try another method
cryo-electron microscopy (cryo-
EM)
https://cryoem.slac.stanford.edu/what-is-cryo-em
instead of trying to capture them in a
single conformation, let them move
around, then take a snapshot and pick
out the most prominent ones
group together & average the ones that look similar
good for BIG things
55. nuclear magnetic resonance
(NMR)
Let it move & look at it all while it moves!
Good for small, flexible, things
use a strong magnet to alter the magnetic
field and see how the nuclei of the atoms in
the proteins respond
gives you an “ensemble” of images
https://slideplayer.com/slide/6420286/
Editor's Notes
Structural biology can be one of those arcane fields that's difficult to describe and I found myself having difficulty explaining what I was doing and why to family and friends. So I set out on a mission to share the wonders of structural biology and biochemistry in a fun way on social media and a website through a not-always-super "scicomm superhero" alter ego the bumbling biochemist (lab coat cape and all), with the underlying personal mission of teaching myself how to communicate science more effectively. In this talk, I introduce some of the fundamentals of structural biology.
Structural biology
At its core, structural biology is about structure, function, and the relationship between the 2
The “macromolecules” we look at are usually looking at proteins or protein/nucleic acid (RNA &/or DNA) complexes.
When we talk about “structure,” we’re usually referring to the overall 3D structure, but proteins have layers of structure underneath the final product we “see”. This starts with the primary structure, which is the sequence of amino acid “building blocks” whose chemical characteristics influence how the protein folds.
Structural biologists often start off knowing (at least some of) what a molecule does (thanks to cell biologists, etc.) but we often have to figure out how best to measure those functions in a test tube (in vitro)
Radiation can cause breaks in DNA. These ends can be stitched back together by DNA ligase, but only if they have the right “caps” - a molecule called polynucleotide kinase phosphatase (PNK) can convert “wrong” caps but it needs to be able to do 3 things - find the site, phosphorylate 5’ ends and dephosphorylate 3’ ends
PNKP splits these 3 functions among it’s 3 domains. The kinase domain adds phosphate, the phosphatase domain removes that phosphate, and the FHA domain binds to proteins that are part of the DNA repair pathway that have already “scouted out” the break site
We can use site-directed mutagenesis to change specific parts of the primary structure, which can then affect the higher structural levels & perhaps the function
We can use site-directed mutagenesis to change specific parts of the primary structure, which can then affect the higher structural levels & perhaps the function
We can use site-directed mutagenesis to change specific parts of the primary structure, which can then affect the higher structural levels & perhaps the function
We can use site-directed mutagenesis to change specific parts of the primary structure, which can then affect the higher structural levels & perhaps the function
If you know the shape of the blade, you can design a shield to cover it
Structure-guided design is also often used to “fine-tune” hit compounds - see where they bind and how they might be made to bind better (i.e. are there other potential interactions if you added something?)
You can use site-directed mutagenesis to find out what specific part within a domain is important for different things. If you don’t “hit” a part that’s important for a particular function, that function won’t be affected, even if it’s in the responsible domain
Different mutations have different effects. Instead of losing function or being “neutral” mutations can modify a molecule’s functional preferences
Get information however you can, then combine that information to come up with a mechanistic theory
Frances Arnold won the 2018 Nobel Prize in Chemistry for research on directed evolution, but her lab also studies how to combine pieces of different proteins together to make “chimeras” with cool new functions. She uses knowledge of the structures of the “parent” molecules to know where are good places to cut & paste so that the parts remain functional
I’m going to focus mainly on crystallography, because it’s what I use
In x-ray crystallography, you freeze your molecules in an ordered lattice and shoot x-ray beams at it. The beams will scatter when they hit the molecules, producing a pattern of spots that you can then work backwards from to deduce the underlying structure
You want the protein to come out of solution but in a very orderly fashion… A technique I commonly use is hanging-drop diffusion. Since the concentration of “magic” in the liquid is higher in the reservoir than in the drop with protein, water will evaporate out of the drop, promoting crystal formation
Flexibility’s great for a lot of things, like linking together domains that need to move independently, but squirming around and crystallizing don’t mix well…
Of course, you’re not getting the full picture, but it’s better than no picture at all! And it’s only 1 piece of the puzzle - you combine whatever limited data you get with data from activity tests, etc.
They were able to get a better look at the floppy FHA domain by crystallizing it apart from the other domains (but with a piece of one of its binding partners so we also get information about how it recognizes the target site)