protein structure prediction.pptx
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Protein Structure prediction concept i.e. how exact predict our protein.and it's types are included in detailed.
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Threading modeling methods
This document discusses protein threading modeling methods. Protein threading, also called fold recognition, is used to model proteins that have the same fold as proteins with known structures but no homologous sequences. It differs from homology modeling which is used for proteins that have homologous sequences. Protein threading works by using statistical knowledge of relationships between structures in the Protein Data Bank and the sequence of the protein being modeled. It is based on observations that there are a limited number of folds in nature and most new structures have similar folds to ones already in the PDB. The document then describes the general steps of the protein threading method.
Methods of Protein structure determination
This document summarizes several methods for determining protein structure: X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. X-ray crystallography involves growing protein crystals, exposing them to X-rays to generate diffraction patterns, and using the patterns to build 3D electron density maps of the protein. Nuclear magnetic resonance spectroscopy measures distances between atomic nuclei in soluble proteins by analyzing spectra from radiofrequency pulses applied in strong magnetic fields. Cryo-electron microscopy images frozen, hydrated protein samples with an electron microscope to determine large protein structures without the need for crystallization.
NMR of protein
Protein NMR spectroscopy is a technique that uses the magnetic properties of atomic nuclei to determine the physical and chemical properties of molecules. It provides detailed information about molecular structure, dynamics, reaction state, and chemical environment. NMR spectroscopy is commonly used by chemists and biochemists to investigate organic molecules. It involves placing a sample in a strong magnetic field and exposing it to radiofrequency pulses to determine the number and types of atoms in the molecule. NMR has applications in chemistry for studying chemical bonds and in medicine for imaging tissues and detecting diseases.
methods for protein structure prediction
protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
Scop database
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.
Protein Threading
Protein threading is a protein structure prediction method that involves "threading" or placing an amino acid sequence into known protein structure templates to find the best matching fold. The key steps are:
1) A query sequence is threaded into structural positions of templates from a structure library to find sequence-structure alignments
2) Alignments are scored and optimized using an objective function accounting for residue interactions and preferences
3) The highest scoring template is selected as the predicted structure, though loop regions are often not accurately predicted
Protein Structure Determination
1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
Express sequence tags
ESTs are short sequences of DNA derived from cDNA clones that represent gene expression in particular cells or tissues. They provide a simple and inexpensive way to discover new genes and map their positions in genomes. To create an EST, mRNA is converted to cDNA and then sequenced, yielding short expressed DNA sequences. ESTs are deposited in public databases like NCBI's dbEST and can help identify genes, construct genome maps, and characterize expressed genes through clustering, assembly, and mapping to genomic sequences. However, isolating mRNA from some tissues can be difficult and ESTs alone do not indicate the genes they were derived from.
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Threading modeling methods
This document discusses protein threading modeling methods. Protein threading, also called fold recognition, is used to model proteins that have the same fold as proteins with known structures but no homologous sequences. It differs from homology modeling which is used for proteins that have homologous sequences. Protein threading works by using statistical knowledge of relationships between structures in the Protein Data Bank and the sequence of the protein being modeled. It is based on observations that there are a limited number of folds in nature and most new structures have similar folds to ones already in the PDB. The document then describes the general steps of the protein threading method.
Methods of Protein structure determination
This document summarizes several methods for determining protein structure: X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. X-ray crystallography involves growing protein crystals, exposing them to X-rays to generate diffraction patterns, and using the patterns to build 3D electron density maps of the protein. Nuclear magnetic resonance spectroscopy measures distances between atomic nuclei in soluble proteins by analyzing spectra from radiofrequency pulses applied in strong magnetic fields. Cryo-electron microscopy images frozen, hydrated protein samples with an electron microscope to determine large protein structures without the need for crystallization.
NMR of protein
Protein NMR spectroscopy is a technique that uses the magnetic properties of atomic nuclei to determine the physical and chemical properties of molecules. It provides detailed information about molecular structure, dynamics, reaction state, and chemical environment. NMR spectroscopy is commonly used by chemists and biochemists to investigate organic molecules. It involves placing a sample in a strong magnetic field and exposing it to radiofrequency pulses to determine the number and types of atoms in the molecule. NMR has applications in chemistry for studying chemical bonds and in medicine for imaging tissues and detecting diseases.
methods for protein structure prediction
protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
Scop database
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.
Protein Threading
Protein threading is a protein structure prediction method that involves "threading" or placing an amino acid sequence into known protein structure templates to find the best matching fold. The key steps are:
1) A query sequence is threaded into structural positions of templates from a structure library to find sequence-structure alignments
2) Alignments are scored and optimized using an objective function accounting for residue interactions and preferences
3) The highest scoring template is selected as the predicted structure, though loop regions are often not accurately predicted
Protein Structure Determination
1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
Express sequence tags
ESTs are short sequences of DNA derived from cDNA clones that represent gene expression in particular cells or tissues. They provide a simple and inexpensive way to discover new genes and map their positions in genomes. To create an EST, mRNA is converted to cDNA and then sequenced, yielding short expressed DNA sequences. ESTs are deposited in public databases like NCBI's dbEST and can help identify genes, construct genome maps, and characterize expressed genes through clustering, assembly, and mapping to genomic sequences. However, isolating mRNA from some tissues can be difficult and ESTs alone do not indicate the genes they were derived from.
threading and homology modelling methods
The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
Protein 3 d structure prediction
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.
RNA secondary structure prediction
RNA plays an important role in protein synthesis. It has a primary sequence made of A, C, G, and U nucleotides that can fold into a secondary structure through base pairing. The secondary structure is predicted using either maximizing the number of base pairs or minimizing the free energy. Dynamic programming is commonly used to predict the optimal secondary structure. Predicting RNA structure is important for understanding its function and evolution.
Homology modelling
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
MULTIPLE SEQUENCE ALIGNMENT
This document discusses multiple sequence alignment techniques. It begins with definitions of key terms like homology, similarity, and conservation. It then describes pairwise alignment and its applications. The rest of the document focuses on multiple sequence alignment methods like progressive alignment, iterative refinement, tree alignment, star alignment, and using genetic algorithms. It provides examples and explanations of popular multiple sequence alignment tools like Clustal W and T-Coffee.
Homology modeling
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
X-ray Crystallography & Its Applications in Proteomics
X-ray crystallography is a technique that uses X-rays to determine the atomic structure of crystals. It involves crystallizing molecules and bombarding them with X-rays, which produce a diffraction pattern. This pattern is used to deduce the molecular structure. X-ray crystallography has many applications in proteomics, including determining protein structures, studying protein interactions, and elucidating enzyme catalysis mechanisms. It provides atomic-level insights that advance understanding of protein function.
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Motif & Domain
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.
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Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Motifs and domains
This document discusses protein motifs and domains. It defines a motif as a recurring arrangement of secondary structure found in multiple proteins, such as the HTH, HLH, and hairpin motifs. A domain contains one or more well-characterized motifs and has an independent function. Two common motifs are described: the HTH motif, which contains two antiparallel alpha helices connected by a beta turn for DNA binding; and the HLH motif, which contains two helices connected by a loop, with the larger helix binding DNA and the smaller helix aiding folding. Domains are defined as distinct functional units that are evolutionarily conserved and can exist independently; they are classified based on secondary structure composition.
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Ab Initio Protein Structure Prediction
Ab initio protein structure prediction uses computational methods to predict a protein's 3D structure from its amino acid sequence. It relies on conformational searching to generate structure decoys and selecting native-like models. The key factors for success are an accurate energy function, efficient search methods like molecular dynamics or genetic algorithms, and effective selection of models close to the native structure. Model selection approaches include energy evaluations, compatibility scores, clustering of similar decoys, and identifying the lowest energy conformations.
Clustal
Clustal Omega is a fast and scalable program for multiple sequence alignment. It begins by producing pairwise alignments using a word-based heuristic method. It then clusters the sequences using a modified mBed distance method and k-means clustering. Finally, it generates the multiple sequence alignment using the HHAlign package, which aligns profile HMMs built from the sequences. Clustal Omega is widely considered one of the fastest online multiple sequence alignment tools.
Protein Structure Prediction
The document discusses protein structure prediction. It begins by reviewing protein structure, including primary, secondary, tertiary, and quaternary structure. It then describes the building blocks of proteins, amino acids, and how their properties allow formation of regular secondary structures like alpha helices and beta sheets. The document outlines different types of secondary structure and how their patterns of hydrogen bonding influence 3D structure. It concludes by describing six classes of protein structure defined by their arrangements of alpha helices and beta sheets.
Structure based and ligand based drug designing
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
Structural genomics
Structural genomics aims to determine the 3D structure of all proteins in a genome. It uses high-throughput methods like X-ray crystallography and NMR on a genomic scale. This allows determination of protein structures for entire proteomes. It provides insights into protein function and can aid drug discovery by identifying potential drug targets like in Mycobacterium tuberculosis. Structural genomics leverages completed genome sequences to clone and express all encoded proteins for structural characterization.
Pairwise sequence alignment
1) Pairwise sequence alignment is a method to compare two biological sequences like DNA, RNA, or proteins. It involves arranging the sequences in columns to highlight their similarities and differences.
2) There are many possible alignments between two sequences, but most imply too many mutations. The best alignment minimizes the number of mutations needed to explain the differences between the sequences.
3) For short protein sequences like "QKGSYPVRSTC" and "QKGSGPVRSTC", the optimal alignment implies one single mutation occurred since the sequences diverged from a common ancestor.
Genomics and proteomics (Bioinformatics)
Genomics is the study of genomes, including sequencing genomes and determining the complete set of proteins and genes in an organism. The first genomes sequenced included Haemophilus influenzae in 1995 and the human genome was completed in 2003, taking 13 years. Genomics provides information on genes, metabolic pathways, and the functioning of organisms through approaches like genome sequencing, structural genomics, functional genomics, comparative genomics, and proteomics.
Functional genomics
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
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The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
Protein 3 d structure prediction
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.
RNA secondary structure prediction
RNA plays an important role in protein synthesis. It has a primary sequence made of A, C, G, and U nucleotides that can fold into a secondary structure through base pairing. The secondary structure is predicted using either maximizing the number of base pairs or minimizing the free energy. Dynamic programming is commonly used to predict the optimal secondary structure. Predicting RNA structure is important for understanding its function and evolution.
Homology modelling
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
MULTIPLE SEQUENCE ALIGNMENT
This document discusses multiple sequence alignment techniques. It begins with definitions of key terms like homology, similarity, and conservation. It then describes pairwise alignment and its applications. The rest of the document focuses on multiple sequence alignment methods like progressive alignment, iterative refinement, tree alignment, star alignment, and using genetic algorithms. It provides examples and explanations of popular multiple sequence alignment tools like Clustal W and T-Coffee.
Homology modeling
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
X-ray Crystallography & Its Applications in Proteomics
X-ray crystallography is a technique that uses X-rays to determine the atomic structure of crystals. It involves crystallizing molecules and bombarding them with X-rays, which produce a diffraction pattern. This pattern is used to deduce the molecular structure. X-ray crystallography has many applications in proteomics, including determining protein structures, studying protein interactions, and elucidating enzyme catalysis mechanisms. It provides atomic-level insights that advance understanding of protein function.
BITS: UCSC genome browser - Part 1
These are the first lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
Motif & Domain
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.
Functional genomics, and tools
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Motifs and domains
This document discusses protein motifs and domains. It defines a motif as a recurring arrangement of secondary structure found in multiple proteins, such as the HTH, HLH, and hairpin motifs. A domain contains one or more well-characterized motifs and has an independent function. Two common motifs are described: the HTH motif, which contains two antiparallel alpha helices connected by a beta turn for DNA binding; and the HLH motif, which contains two helices connected by a loop, with the larger helix binding DNA and the smaller helix aiding folding. Domains are defined as distinct functional units that are evolutionarily conserved and can exist independently; they are classified based on secondary structure composition.
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The above presentation consist of the definition of microarray, brief history, general principle of the same, the type of scanner that are used to read or to scan the microarray , type of DNA microarray and finally its various apliccation including the role of DNA microaarray in drug discovery.
Ab Initio Protein Structure Prediction
Ab initio protein structure prediction uses computational methods to predict a protein's 3D structure from its amino acid sequence. It relies on conformational searching to generate structure decoys and selecting native-like models. The key factors for success are an accurate energy function, efficient search methods like molecular dynamics or genetic algorithms, and effective selection of models close to the native structure. Model selection approaches include energy evaluations, compatibility scores, clustering of similar decoys, and identifying the lowest energy conformations.
Clustal
Clustal Omega is a fast and scalable program for multiple sequence alignment. It begins by producing pairwise alignments using a word-based heuristic method. It then clusters the sequences using a modified mBed distance method and k-means clustering. Finally, it generates the multiple sequence alignment using the HHAlign package, which aligns profile HMMs built from the sequences. Clustal Omega is widely considered one of the fastest online multiple sequence alignment tools.
Protein Structure Prediction
The document discusses protein structure prediction. It begins by reviewing protein structure, including primary, secondary, tertiary, and quaternary structure. It then describes the building blocks of proteins, amino acids, and how their properties allow formation of regular secondary structures like alpha helices and beta sheets. The document outlines different types of secondary structure and how their patterns of hydrogen bonding influence 3D structure. It concludes by describing six classes of protein structure defined by their arrangements of alpha helices and beta sheets.
Structure based and ligand based drug designing
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
Structural genomics
Structural genomics aims to determine the 3D structure of all proteins in a genome. It uses high-throughput methods like X-ray crystallography and NMR on a genomic scale. This allows determination of protein structures for entire proteomes. It provides insights into protein function and can aid drug discovery by identifying potential drug targets like in Mycobacterium tuberculosis. Structural genomics leverages completed genome sequences to clone and express all encoded proteins for structural characterization.
Pairwise sequence alignment
1) Pairwise sequence alignment is a method to compare two biological sequences like DNA, RNA, or proteins. It involves arranging the sequences in columns to highlight their similarities and differences.
2) There are many possible alignments between two sequences, but most imply too many mutations. The best alignment minimizes the number of mutations needed to explain the differences between the sequences.
3) For short protein sequences like "QKGSYPVRSTC" and "QKGSGPVRSTC", the optimal alignment implies one single mutation occurred since the sequences diverged from a common ancestor.
Genomics and proteomics (Bioinformatics)
Genomics is the study of genomes, including sequencing genomes and determining the complete set of proteins and genes in an organism. The first genomes sequenced included Haemophilus influenzae in 1995 and the human genome was completed in 2003, taking 13 years. Genomics provides information on genes, metabolic pathways, and the functioning of organisms through approaches like genome sequencing, structural genomics, functional genomics, comparative genomics, and proteomics.
Functional genomics
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
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Homology modeling is a computational method to predict the 3D structure of a protein based on the known structure of homologous proteins. It involves 7 main steps: 1) selecting a template protein with high sequence similarity, 2) aligning the sequences, 3) building the protein backbone, 4) modeling loops and insertions/deletions, 5) refining side chains, 6) refining the overall structure using energy minimization, and 7) evaluating the model. Homology models can accurately predict protein structure when the sequence identity between the target and template is above 30%. Models are useful for studying protein function and designing drugs.
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Homology modeling uses the amino acid sequence of a target protein and the 3D structure of a related template protein to generate a 3D model of the target. It involves aligning the target sequence to the template sequence, building the backbone of the target based on the template structure, modeling loops and side chains, optimizing the model structure, and validating the model. Homology modeling is most accurate when the sequence identity between the target and template is above 30%. It provides information about conserved regions and residues but is limited in modeling insertions, deletions, and side chains.
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protein structure prediction.pptx
- 1. PROTEIN STRUCTURE PREDICTION Presented by – Siddika Arif Attar M.SC BIOTECHNOLOGY
- 2. Introduction Methods for protein structure prediction Basic concept Homology modeling Folding recognition Ab –intio method INDEX
- 3. Introduction : protein structure prediction- protein structure prediction is the inference of the 3-D structure of protein From its amino acid. i.e. The predicton of its secondory & tertiory structure from primary structure.
- 4. Methods for protein structure prediction 1. Experimental method 2. Computational method X-Ray Crystallography NMR Spectroscopy Electron Microscopy Homology modeling Fold Recognition Ab – Intio method
- 6. 1. Homology modeling Knowledge based modeling. Based on sequence similarity with a protein. For which a structure has been predicted .
- 7. Steps in homology modeling 1. Target recognition : Search related protein sequence in any strucrural database. FASTA &BLAST from EMBL-EBI & NCBI can be used ‘ 2. Template selection : Slection on the basis of higher similarity ,close- subfamily phylogenetic tree & purpose of modeling 3. Alignment sequence : The sequence similarity is too high then global alignment used. The best alignment methods are Cludtal X ,MUSCLE, T-Coffe & MAFFT .
- 8. 4. Model Building : Use MODELLER of model building i.e. Max Mod , PY Mod , PRIMO. 5. Model Evaluation : Accuracy of the model depend upon its sequence identity with the template. Fig .Homology Modeling
- 9. 2. Folding recognition Two Algorithms : Pairwise energy based method [ threading] Profile based method [fold recognition] 1. Pairwise energy based method : Searach for structural fold database by using energy based criteria. Using a dynamic programming & heuristic approaches . Calculate energy for raw model . Lowest energy fold is the best model .
- 10. 2. Profile based method A profile is constructed for related protein structure Generated by superimposition if structures to expose corresponding residues. Similarity depend upon the secondary structure ,polarity, hydrophobicity.
- 11. 3. Ab – Intio Method Ab – Intio method predict the proteins structure based on the physical methods . These method build protein 3-D structures . In Ab – Intio method ,Rosetta tool can be used . Rosetta Breaks down the query sequence into many short segments Predict the secondary structure of small segments using HMMSTR. Segments with assigned secondary structure & assembled into 3D configuration. A large number of models are build & their over all energy potential calculated . Conformation with lowest energy is choosen as the best model.
- 12. THANK YOU !