Proteins are polymers of amino acids existing as a structural and functional unit. here we look at various bioinformatics tools that can be used to predict these protein structures from its known amino acid sequence.
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
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.
1) This document introduces methods for detecting sequence similarity, which is a fundamental analysis in bioinformatics.
2) It describes how to search databases for similar sequences using BLAST or FASTA, and how to compare two sequences using dynamic programming algorithms like Needleman-Wunsch or Smith-Waterman.
3) Substitution matrices like BLOSUM62 are used to score alignments and measure sequence similarity based on amino acid properties.
The document discusses various methods for predicting protein function, including homology-based transfer of annotation and prediction of functional motifs and domains. Homology-based transfer can infer molecular function from sequence similarity, but biological process is only transferable between orthologs. Orthologs can be detected through phylogenetic trees or automated methods like InParanoid. Each protein domain contributes to molecular function, while short motifs like phosphorylation sites are also important. Functional annotation involves describing proteins at the molecular, biological process, and cellular component levels.
This document summarizes different computational methods for protein structure prediction, including homology modeling, fold recognition, threading, and ab initio modeling. Homology modeling relies on identifying proteins with similar sequences and known structures. Fold recognition and threading can be used when there are no homologs, to identify proteins with the same overall fold but different sequences. Ab initio modeling uses physics-based modeling and protein fragments to predict structure from sequence alone, and has challenges due to the vast number of possible conformations.
Protein Sequence, Structure, and Functional Databases: UniProtKB, Swiss-Prot, TrEMBL, PIR, MIPS, PROSITE, PRINTS, BLOCKS, Pfam, NDRB, OWL, PDB, SCOP, CATH, NDB, PQS, SYSTERS, and Motif. Presented at UGC Sponsored National Workshop on Bioinformatics and Sequence Analysis conducted by Nesamony Memorial Christian College, Marthandam on 9th and 10th October, 2017 by Prof. T. Ashok Kumar
sequence of file formats in bioinformaticsnadeem akhter
This document discusses different biological database file formats for storing sequence and molecular data. It describes several common formats including FASTA, Multi-FASTA, GeneBank flat-file format, GCG, EMBL, Clustal, and SWISS PROT. It provides details on the header, feature, and sequence sections of each format and how they represent genetic and protein information.
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
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
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.
1) This document introduces methods for detecting sequence similarity, which is a fundamental analysis in bioinformatics.
2) It describes how to search databases for similar sequences using BLAST or FASTA, and how to compare two sequences using dynamic programming algorithms like Needleman-Wunsch or Smith-Waterman.
3) Substitution matrices like BLOSUM62 are used to score alignments and measure sequence similarity based on amino acid properties.
The document discusses various methods for predicting protein function, including homology-based transfer of annotation and prediction of functional motifs and domains. Homology-based transfer can infer molecular function from sequence similarity, but biological process is only transferable between orthologs. Orthologs can be detected through phylogenetic trees or automated methods like InParanoid. Each protein domain contributes to molecular function, while short motifs like phosphorylation sites are also important. Functional annotation involves describing proteins at the molecular, biological process, and cellular component levels.
This document summarizes different computational methods for protein structure prediction, including homology modeling, fold recognition, threading, and ab initio modeling. Homology modeling relies on identifying proteins with similar sequences and known structures. Fold recognition and threading can be used when there are no homologs, to identify proteins with the same overall fold but different sequences. Ab initio modeling uses physics-based modeling and protein fragments to predict structure from sequence alone, and has challenges due to the vast number of possible conformations.
Protein Sequence, Structure, and Functional Databases: UniProtKB, Swiss-Prot, TrEMBL, PIR, MIPS, PROSITE, PRINTS, BLOCKS, Pfam, NDRB, OWL, PDB, SCOP, CATH, NDB, PQS, SYSTERS, and Motif. Presented at UGC Sponsored National Workshop on Bioinformatics and Sequence Analysis conducted by Nesamony Memorial Christian College, Marthandam on 9th and 10th October, 2017 by Prof. T. Ashok Kumar
sequence of file formats in bioinformaticsnadeem akhter
This document discusses different biological database file formats for storing sequence and molecular data. It describes several common formats including FASTA, Multi-FASTA, GeneBank flat-file format, GCG, EMBL, Clustal, and SWISS PROT. It provides details on the header, feature, and sequence sections of each format and how they represent genetic and protein information.
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
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.
The document discusses Prosite, a database of protein family signatures that can be used to determine the function of uncharacterized proteins. It contains patterns and profiles formulated to identify which known protein family a new sequence belongs to. The Prosite database consists of two files - a data file containing information for scanning sequences, and a documentation file describing each pattern and profile. New Prosite entries are mainly profiles developed by collaborators at the SIB Swiss Institute of Bioinformatics to identify distantly related proteins based on conserved residues.
Modeller is an automated protein homology modelling tool that takes a target protein sequence and known template protein structure as input. It first aligns the target and template sequences and then uses spatial restraints to build a 3D model of the target protein's structure. The key steps in Modeller's methodology are sequence alignment, satisfaction of spatial restraints from the template, and building a model of the target protein structure.
In the era of computers life sciences databases are still understated. Here is my presentation on biological databases. Complete classification of different databases.
For more presentations and work come and visit
https://www.linkedin.com/in/shradheya-r-r-gupta-54492984/
The ZINC database was developed by John Irwin as a curated collection of commercially available small molecules for virtual screening, containing data on commercially available and annotated small molecules with their 3D structures. Investigators in pharmaceutical companies, biotech companies, and research universities use the ZINC database for virtual screening as it aims to represent molecules in their biologically relevant 3D form, and is continuously updated while also releasing static subsets quarterly.
The document discusses various types of biological databases including nucleotide databases, genomic databases, protein databases, and metabolic databases. It provides examples of several specific databases, such as Nucleotide databases like GenBank, genomic databases like Entrez Genome, protein databases like UniProt, and metabolic databases like KEGG. It also discusses the different levels of data in biological databases from primary data directly from experiments to secondary data that is analyzed and derived from primary data.
The OMIM database provides structured summaries of the relationship between human genotypes and phenotypes by reviewing the biomedical literature. It was initiated in the 1960s as MIM and became an online database called OMIM in 1985. OMIM contains over 24,600 entries describing more than 16,000 genes and 8,600 phenotypes. The entries are updated nightly and provide a structured format to describe genotype-phenotype relationships along with interactive tools like genome coordinate searching and phenotypic series.
Proteins play crucial roles in nearly all biological processes. These many functions of proteins are a result of the folding of proteins into many distinct 3D structures.
Protein analysis tries to explore how amino acid sequences specify the structure of proteins and how these proteins bind to substrates and other molecules to perform their functions.
Protein analysis allows us to understand the function of the protein based on its structure.
Protein microarrays are high-throughput methods that allow researchers to study protein interactions and functions on a large scale. There are three main types of protein microarrays: analytical microarrays use antibodies to detect specific proteins in samples; functional microarrays examine protein-protein and other molecular interactions; and reverse-phase protein microarrays profile protein expression levels and post-translational modifications by immobilizing cell or tissue lysates. Protein microarrays have applications in diagnostics, proteomics, studying protein functions, and analyzing antibodies.
This document discusses databases in bioinformatics. It begins by noting the rapid increase in biological data from sources like gene sequences, protein sequences, structural data, and gene expression data. It then defines biological databases as structured, searchable collections of data that are periodically updated and cross-referenced. The major purposes of databases are to make biological data available, systematize the data, and allow analysis of computed biological data. The document provides a brief history of biological databases and sequencing efforts. It also classifies biological databases based on data type, maintenance status, data access, data sources, database design, and organism. Specific databases discussed include DDBJ, EMBL, GenBank, Swiss-Prot, and NCB
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
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.
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
This document provides information about UniProt, a hub for protein knowledge that includes several databases. It summarizes the main UniProt databases: UniProtKB contains manually annotated Swiss-Prot and automatically annotated TrEMBL sections; UniParc is an archive of all protein sequences and UniRef clusters similar sequences. The document outlines the process of automatic annotation in TrEMBL and manual annotation in Swiss-Prot. It also describes search, alignment and retrieval tools on the UniProt website and options for downloading protein data.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
SWISS-PROT- Protein Database- The Universal Protein Resource Knowledgebase (UniProtKB) is the central hub for the collection of functional information on proteins.
Protein structure determination of insulin of zebra fish (Danio rerio) by hom...IOSR Journals
The protein sequence of insulin of zebra fish is obtained from UniProt. Due to lack of their structure, structure prediction is necessary, because the structure of protein plays an important role in their function. Our work is based on the production of two protein structure, from the same sequence, by computational approach and finally validates these generated structures. In this work two different widely acceptable online web tool are used for generating structure from the protein sequences of insulin of zebra fish. These are Swiss Model web server and ESyPred3D web server. After getting structure from this two web tool, the structures are passed by a series of quality tests. ProQ web software is used for checking quality of these generated structures. 3d-ss web tool is used for superimposition between two generated structures. It can compare between two structures. The Ramachandran plot is calculated by using VegaZZ software. CASTp (Computer Atlas of Surface Topology of protein) is a web tool, used to predict active sides with their respective volume and area. Finally ProFunc tool is used for analysis of two structures
This document describes a study that used computational methods to predict the 3D protein structure of insulin from zebrafish. The researchers obtained the insulin sequence from UniProt and used Swiss Model and ESyPred3D to generate 3D structures. They then validated the structures using ProQ, analyzed them using tools like 3d-ss, VegaZZ, CASTp and ProFunc. Both generated structures passed quality tests, had good Ramachandran plots and active sites were identified. This suggests the computational approach was able to predict reasonable insulin structures when experimental data was unavailable.
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.
The document discusses Prosite, a database of protein family signatures that can be used to determine the function of uncharacterized proteins. It contains patterns and profiles formulated to identify which known protein family a new sequence belongs to. The Prosite database consists of two files - a data file containing information for scanning sequences, and a documentation file describing each pattern and profile. New Prosite entries are mainly profiles developed by collaborators at the SIB Swiss Institute of Bioinformatics to identify distantly related proteins based on conserved residues.
Modeller is an automated protein homology modelling tool that takes a target protein sequence and known template protein structure as input. It first aligns the target and template sequences and then uses spatial restraints to build a 3D model of the target protein's structure. The key steps in Modeller's methodology are sequence alignment, satisfaction of spatial restraints from the template, and building a model of the target protein structure.
In the era of computers life sciences databases are still understated. Here is my presentation on biological databases. Complete classification of different databases.
For more presentations and work come and visit
https://www.linkedin.com/in/shradheya-r-r-gupta-54492984/
The ZINC database was developed by John Irwin as a curated collection of commercially available small molecules for virtual screening, containing data on commercially available and annotated small molecules with their 3D structures. Investigators in pharmaceutical companies, biotech companies, and research universities use the ZINC database for virtual screening as it aims to represent molecules in their biologically relevant 3D form, and is continuously updated while also releasing static subsets quarterly.
The document discusses various types of biological databases including nucleotide databases, genomic databases, protein databases, and metabolic databases. It provides examples of several specific databases, such as Nucleotide databases like GenBank, genomic databases like Entrez Genome, protein databases like UniProt, and metabolic databases like KEGG. It also discusses the different levels of data in biological databases from primary data directly from experiments to secondary data that is analyzed and derived from primary data.
The OMIM database provides structured summaries of the relationship between human genotypes and phenotypes by reviewing the biomedical literature. It was initiated in the 1960s as MIM and became an online database called OMIM in 1985. OMIM contains over 24,600 entries describing more than 16,000 genes and 8,600 phenotypes. The entries are updated nightly and provide a structured format to describe genotype-phenotype relationships along with interactive tools like genome coordinate searching and phenotypic series.
Proteins play crucial roles in nearly all biological processes. These many functions of proteins are a result of the folding of proteins into many distinct 3D structures.
Protein analysis tries to explore how amino acid sequences specify the structure of proteins and how these proteins bind to substrates and other molecules to perform their functions.
Protein analysis allows us to understand the function of the protein based on its structure.
Protein microarrays are high-throughput methods that allow researchers to study protein interactions and functions on a large scale. There are three main types of protein microarrays: analytical microarrays use antibodies to detect specific proteins in samples; functional microarrays examine protein-protein and other molecular interactions; and reverse-phase protein microarrays profile protein expression levels and post-translational modifications by immobilizing cell or tissue lysates. Protein microarrays have applications in diagnostics, proteomics, studying protein functions, and analyzing antibodies.
This document discusses databases in bioinformatics. It begins by noting the rapid increase in biological data from sources like gene sequences, protein sequences, structural data, and gene expression data. It then defines biological databases as structured, searchable collections of data that are periodically updated and cross-referenced. The major purposes of databases are to make biological data available, systematize the data, and allow analysis of computed biological data. The document provides a brief history of biological databases and sequencing efforts. It also classifies biological databases based on data type, maintenance status, data access, data sources, database design, and organism. Specific databases discussed include DDBJ, EMBL, GenBank, Swiss-Prot, and NCB
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
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.
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
This document provides information about UniProt, a hub for protein knowledge that includes several databases. It summarizes the main UniProt databases: UniProtKB contains manually annotated Swiss-Prot and automatically annotated TrEMBL sections; UniParc is an archive of all protein sequences and UniRef clusters similar sequences. The document outlines the process of automatic annotation in TrEMBL and manual annotation in Swiss-Prot. It also describes search, alignment and retrieval tools on the UniProt website and options for downloading protein data.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
SWISS-PROT- Protein Database- The Universal Protein Resource Knowledgebase (UniProtKB) is the central hub for the collection of functional information on proteins.
Protein structure determination of insulin of zebra fish (Danio rerio) by hom...IOSR Journals
The protein sequence of insulin of zebra fish is obtained from UniProt. Due to lack of their structure, structure prediction is necessary, because the structure of protein plays an important role in their function. Our work is based on the production of two protein structure, from the same sequence, by computational approach and finally validates these generated structures. In this work two different widely acceptable online web tool are used for generating structure from the protein sequences of insulin of zebra fish. These are Swiss Model web server and ESyPred3D web server. After getting structure from this two web tool, the structures are passed by a series of quality tests. ProQ web software is used for checking quality of these generated structures. 3d-ss web tool is used for superimposition between two generated structures. It can compare between two structures. The Ramachandran plot is calculated by using VegaZZ software. CASTp (Computer Atlas of Surface Topology of protein) is a web tool, used to predict active sides with their respective volume and area. Finally ProFunc tool is used for analysis of two structures
This document describes a study that used computational methods to predict the 3D protein structure of insulin from zebrafish. The researchers obtained the insulin sequence from UniProt and used Swiss Model and ESyPred3D to generate 3D structures. They then validated the structures using ProQ, analyzed them using tools like 3d-ss, VegaZZ, CASTp and ProFunc. Both generated structures passed quality tests, had good Ramachandran plots and active sites were identified. This suggests the computational approach was able to predict reasonable insulin structures when experimental data was unavailable.
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is an open access international journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document provides instructions for a lab exercise on protein analysis using various bioinformatics databases and tools. It includes steps to retrieve protein sequences from UniProtKB and protein structures from RCSB PDB for specified proteins. It also describes using the MolStar viewer to view protein structures and the Swiss-Model tool to build homology models for proteins based on their sequences. Students are asked to complete exercises retrieving sequence and structure data and generating models for several human proteins to analyze and visualize.
protein design, principles and examples.pptxGopiChand121
Protein design uses structural biology knowledge to predict amino acid sequences that produce proteins with targeted properties, allowing hypotheses to be tested. Computational methods now design proteins very different from known ones. Protein design remains an important problem, and current methods largely use physics-based approaches relying on single structures, despite multiple structures being available. A new method called FlexiBaL-GP uses machine learning to learn lower dimensional representations of backbone movements from multiple structures.
The document describes using systems biology modeling tools to model the metabolic network of MRSA acetate kinase. It outlines conducting protein homology modeling using PMP to generate structural models of acetate kinase, evaluating models using ERRAT, chemical modeling of protein kinase inhibitors using OCHEM and PubChem, and metabolic network modeling using BioModels and Virtual Cell. The aim is to understand how protein, chemical, and metabolic network modeling can provide more information for research into new antibiotic targets. Key steps include selecting templates for homology modeling, evaluating models with ERRAT, extracting chemical data from OCHEM and PubChem, importing an acetate kinase model from BioModels into Virtual Cell, validating the model, and running simulations in Virtual Cell.
Pharmacophore based ligand-designing_using_substructure_searching_to_explore_...Prasanthperceptron
The document describes a protocol for identifying new chemical entities (NCEs) using pharmacophore-based ligand design and substructure searching. It involves taking a known biologically active "pivot" molecule, searching for similar structures in PubChem based on smiles or InChi key, uploading matched structures to PharmaGist for pharmacophore analysis, and interpreting the results to identify potential new ligands that maintain essential pharmacophore features. The goal is to discover novel molecules for patenting while leveraging knowledge of established pharmacophores.
This document provides a manual for using the Protein Stability Program developed by S. Prasanth Kumar. The program takes a protein sequence as input and outputs kinetic and thermodynamic parameters to analyze protein stability. It serves to help understand protein stability from sequence alone without 3D structure. Key steps include downloading the program, preparing an input sequence file, running the program from the command line, and interpreting the results to identify amino acids contributing most to stability.
1
Phylogenetic Analysis Homework assignment
This assignment will be completed on your own and turned in the week of 11/8-11/10.
Introduction
Molecular evolution is the study of how proteins and nucleic acids evolve. Included in this
field are studies of mutations and chromosomal rearrangements, the evolutionary process,
the identification of sequence patterns conferring function in proteins and nucleic acids,
and the reconstruction of the evolutionary history of organisms and the molecules that
they make. All of these studies rely on comparisons of nucleotide or amino acid sequences.
In this tutorial, you will be introduced to some of the fundamental principles of molecular
evolution and the types of bioinformatics tools that are used in evolutionary studies. We
will begin by carrying out a manual sequence comparison, so that the basic concepts can
be introduced, and the remainder of the project will be carried out at The Biology
Workbench, a set of bioinformatics analysis programs managed by The San Diego
Supercomputing Center at the University of California, San Diego.
Objectives
• To introduce the principles of molecular evolution
• To acquaint you with the tools that are available to compare nucleotide and
amino acid sequences
• To learn about the use of protein sequences in reconstructions of evolutionary history
Project
Branching evolution occurs when one ancestral species gives rise to two or more progeny
species. However, speciation events don't involve the vast majority of the genes in a
genome. That is, for most genes, both of the progeny species inherit identical genes from
the ancestor. Following speciation, these genes evolve independently in the separate
lineages. Studies of molecular evolution therefore rely heavily on comparisons of related
sequences from different organisms.
Shown below is an alignment of two homologous sequences that we will use as a starting
place. Homologous sequences are sequences that have descended from a common
ancestral sequence. You can't meaningfully compare sequences unless they are
homologous. This alignment uses the single letter amino acid code, in which G represents
glycine, Q represents glutamine, etc. The aligned proteins have been shown to be involved
in the metabolism of similar, but different, toxic compounds. As you can see, these amino
acid sequences are very similar and it is easy to recognize that they are related by common
descent.
2
dntAc: KMGVDDEVIVSRQNDGSVR
nahAc: KMGIDDEVIVSRQSDGSIR
An expanded version of this alignment is shown below. In this expanded alignment, both
the amino acids and the corresponding DNA nucleotides are shown. For ease of analysis,
the codons have been broken into separate entries in a table.
Alignment of nahAc and dntAc sequences.
K M G V D E V I V
dntAc AAA ATG GGC GTC GAT GAA GTC ATC GTC
nahAc ...
PROCheck is a tool used to evaluate protein models by analyzing residue geometry. It takes a protein structure file as input, then analyzes residue-by-residue geometry and overall structure geometry. PROCheck outputs various plots and listings that analyze features like the Ramachandran plot, residue properties, and distorted geometry regions. It aims to assess how normal or unusual a protein structure is compared to parameters from high-resolution structures.
Generic approach for predicting unannotated protein pair function using proteinIAEME Publication
This document discusses several approaches for predicting protein function, including methods that use amino acid sequences, protein structures, genomic sequences, phylogenetic data, microarray expression data, and protein interaction networks. It provides details on each type of data source and summarizes common computational techniques used for protein function prediction, such as homology-based approaches, clustering-based approaches, and classification-based approaches.
This document discusses homology modeling, which is a computational technique used to develop atomic-resolution models of proteins based on their amino acid sequences and known 3D structures of homologous proteins. It describes the key steps in homology modeling as template identification, target-template alignment, model building and refinement, and model validation. The advantages of homology modeling include that it is faster than experimental techniques. However, the accuracy depends on factors like the sequence identity between the target and template.
The Protein Data Bank (PDB) is a single worldwide database that stores 3D structural data of proteins and nucleic acids. It is operated by Rutgers University, the San Diego Supercomputer Center, and the Research Collaboratory for Structural Bioinformatics. The PDB is freely accessible online and contains over 76,000 biomolecular structure entries as of 2011. It uses a common file format to represent structural data and is updated weekly as new entries are submitted by researchers.
This document provides an overview of protein structure prediction. It begins by defining the primary, secondary, and tertiary protein structures. It then discusses how to predict secondary structures using servers like PSIPRED and features like transmembrane domains. Next, it covers retrieving protein structures from the PDB and using BLAST and homology modeling to predict structures for proteins without known structures. It concludes by noting the challenges in predicting protein movements and interactions.
The document discusses various methods for determining protein structures, including X-ray crystallography and NMR spectroscopy. It provides details on how each method works and their advantages/disadvantages. Key databases for accessing protein structure data are also introduced, such as the Protein Data Bank (PDB) and NCBI's Molecular Modeling Database (MMDB).
This document discusses protein structural bioinformatics and methods for predicting protein structure using bioinformatics approaches. It defines protein structural bioinformatics as focusing on representing, storing, analyzing and displaying protein structural information at the atomic scale. It describes how bioinformatics can be used to visualize, align, classify and predict protein structures. It also summarizes several specific methods for predicting protein secondary structure and tertiary structure, including homology modeling, threading and ab initio prediction.
This document discusses several important databases and tools for protein structure and molecular modeling. It describes the Protein Data Bank (PDB) as a repository for 3D structural data of proteins and nucleic acids. It also outlines the National Center for Biotechnology Information (NCBI) and its Molecular Modeling Database (MMDB), which contains experimentally resolved protein structures from PDB with additional features. Other databases and tools mentioned include UniProt, ExPASy, BLAST, and their uses in analyzing protein sequences, structures, functions, and evolutionary relationships.
EMBL-EBI is a free molecular database that provides comprehensive information across various life science domains. It draws data from multiple external sources and databases to offer detailed information on topics like nucleotide sequences, protein structures, genomes, and related literature. Users can access this data online through the EMBL-EBI website and search tools or download selected data and software for offline use.
Practical 9 protein structure and function (3)Osama Barayan
This document is a lab report on studying protein structure and function using prediction tools and visualization software. It discusses using Rasmol to visualize the 3D structure of rabbit muscle triose phosphate isomerase from its PDB file. Several protein structure prediction servers are also compared, and their predictions for the secondary structure of triose phosphate isomerase are analyzed. The lab report further explores using Rasmol to analyze other protein structures from the PDB, identify heteroatoms and active sites.
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Protein structure prediction primary structure analysis.pptx
1. Protein structure prediction
using bioinformatic tools
1
Okechukwu Francis
Programme: PhD Biotechnology
SCHOOL OF HEALTH SCIENCE AND TECHNOLOGY (SoHST)
2. What structure protein structure prediction?
Protein structure prediction refers to the process of predicting the
three-dimensional structure of a protein molecule based on its amino
acid sequence. Proteins are large biomolecules made up of chains of
amino acids that fold into unique three-dimensional shapes, which
determine their functions.
The process of predicting the structure of a protein typically involves
using computational methods to simulate the folding process of the
protein chain, and to predict the interactions between amino acids that
stabilize the final folded structure. These methods can be divided into
two main categories: template-based modelling and ab initio
modelling.
3. In template-based modelling, the structure of a protein is predicted
based on the known structure of a homologous protein with a similar
amino acid sequence. This method relies on the assumption that
proteins with similar sequences have similar structures.
4. In ab initio modelling, the structure of a protein is predicted
based on the first principles, without relying on the known
structure of any homologous proteins. This method involves
simulating the folding process of the protein from scratch,
using principles of physics and chemistry to predict the most
stable conformation of the protein.
Protein structure prediction is an important field of study
because it can help to elucidate the functions of proteins, and
can provide insights into the mechanisms of disease and drug
development. However, predicting protein structures is still a
challenging problem, and accurate predictions are not always
possible.
5. How to analyze protein primary and secondary structure using psi-pred
PSI-PRED is a widely used tool for predicting protein secondary structure from the primary amino acid sequence.
However, it does not analyze the primary structure itself.
To analyze the primary structure of a protein using PSI-PRED, you would first need to use another tool to obtain the
amino acid sequence of the protein of interest. Once you have the sequence, you can use PSI-PRED to predict the
secondary structure, which includes alpha helices, beta sheets, and coil regions.
The predicted secondary structure can then be used to help infer the tertiary structure of the protein.
Here are the basic steps to use PSI-PRED for secondary structure prediction:
1. Obtain the amino acid sequence of the protein of interest.
2. Go to the PSI-PRED website (http://bioinf.cs.ucl.ac.uk/psipred/) and click on the "Submit a Job" button.
3. Paste the amino acid sequence into the text box provided.
4. Choose the "PSI-PRED + PSIPRED-SS2" option for the prediction method.
5. Click the "Submit" button to initiate the prediction.
6. Wait for the prediction to finish, which may take several minutes to a few hours depending on the size of the protein.
7. Download the results, which will include a prediction of the secondary structure in the form of a prediction file.
Once you have the predicted secondary structure, you can use it to help predict the tertiary structure using tools such as
homology modelling or molecular dynamics simulations.
6.
7.
8. Homology modelling using Swiss model
Homology modelling is a computational method used to predict the three-dimensional structure of a protein based on
the known structure of a related protein. Swiss Model is a widely used tool for homology modelling.
Here are the basic steps to use Swiss Model for homology modelling:
1. Go to the Swiss Model website (https://swissmodel.expasy.org/) and click on the "Start Modeling" button.
2. Paste the amino acid sequence of the protein of interest into the text box provided.
3. Choose a suitable template structure for the homology modelling. Swiss Model provides several options for
selecting a template, including searching the Protein Data Bank (PDB) or using a custom template.
4. Click on the search template to access various templates on the database which would be generated automatically
by Swiss Model.
5. Review the alignment and make any necessary adjustments.
6. Submit the modelling job to Swiss Model.
7. Wait for the modelling to finish, which may take several minutes to a few hours depending on the size of the protein
and the complexity of the modelling.
8. Download the results, which will include the predicted three-dimensional structure of the protein in the form of a
PDB file.
Note: After obtaining the predicted structure, it is important to validate the quality of the model. This can be done
using tools such as PROCHECK or MolProbity, which assess the stereochemical quality of the model and compare it to
known protein structures. Further refinement of the model can also be performed using molecular dynamics simulations
or other methods.
9.
10.
11.
12. Molecular visualization using jmol
Jmol is a free, open-source software program that is commonly used for molecular visualization and analysis. It
can be used to view and manipulate molecular structures, and to visualize properties such as electrostatic
potential, hydrogen bonding, and solvent accessibility.
Here are the basic steps to use Jmol for molecular visualization:
1. Obtain the molecular structure you wish to visualize in a format that Jmol can read, such as a PDB file.
2. Download and install Jmol on your computer from the Jmol website (http://jmol.sourceforge.net/).
3. Launch Jmol by double-clicking on the Jmol application icon.
4. In the Jmol window, go to the "File" menu and choose "Open".
5. Select the PDB file you wish to visualize and click "Open".
6. The molecule will be displayed in the Jmol window, and you can use the mouse to rotate, zoom, and
translate the view.
7. You can also use Jmol to visualize various molecular properties. For example, to display the electrostatic
potential of the molecule, go to the "Display" menu and choose "Electrostatic Potential". To display
hydrogen bonding, go to the "Display" menu and choose "Hydrogen Bonds".
8. You can also use Jmol to analyze the molecular structure. For example, you can measure distances between
atoms or calculate the surface area of the molecule.
9. When you are finished using Jmol, go to the "File" menu and choose "Exit" to close the program.
Jmol has many other features and capabilities beyond those listed here, so it is recommended to consult the
Jmol user manual or online documentation for more detailed instructions and examples.
13.
14. Protein structure model evaluation using PROCHECK
PROCHECK is a software program used to evaluate the quality of protein structures. It
assesses the stereochemical quality of the model and compares it to known protein
structures to identify any potential errors or anomalies in the structure.
Here are the basic steps to use PROCHECK for protein structure model evaluation:
Obtain the protein structure you wish to evaluate in a format that PROCHECK can
read, such as a PDB file.
Download and install PROCHECK on your computer from the PROCHECK
website (https://www.ebi.ac.uk/thornton-srv/software/PROCHECK/).
Launch PROCHECK by typing "procheck" at the command prompt or by double-
clicking on the PROCHECK application icon.
In the PROCHECK window, go to the "File" menu and choose "Load Protein".
Select the PDB file you wish to evaluate and click "Open".
The PROCHECK analysis will begin automatically, and the results will be
displayed in the PROCHECK window.
15. The PROCHECK results include several types of information, including the
Ramachandran plot, which shows the distribution of the backbone dihedral angles in the
protein structure. PROCHECK compares this distribution to a distribution derived from
high-resolution protein structures to identify any outliers or unusual conformations in the
protein structure. Other information provided by PROCHECK includes the quality factor
(Q-factor), which is a measure of the overall quality of the structure, and a summary of
potential errors or anomalies in the structure.
Review the PROCHECK results and identify any potential errors or anomalies in the
protein structure. If necessary, refine the structure using additional methods such as
molecular dynamics simulations or energy minimization.
When you are finished using PROCHECK, go to the "File" menu and choose "Exit" to
close the program.
PROCHECK has many other features and capabilities beyond those listed here, so it is
recommended to consult the PROCHECK user manual or online documentation for more
detailed instructions and examples.