The presentation highlights the Protein Families concept, methods used to predict them, and some automated servers for annotation of Hypothetical Proteins
This document provides an overview of several important protein databases:
- SWISS-PROT is an annotated protein sequence database that is maintained collaboratively and contains over 1.29 million entries. TrEMBL is a computer-annotated supplement to SWISS-PROT containing sequences not yet in SWISS-PROT.
- Structural databases like PDB, SCOP, and CATH provide protein structure information. PDB is an international repository for macromolecular structures. SCOP and CATH classify protein domains based on structural similarities and evolutionary relationships.
- Other databases mentioned include InterPro, GOA, Proteome Analysis, and GenBank, which provide functional annotation, gene ontology assignments, proteome analysis
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
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.
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 CATH database hierarchically classifies protein domains obtained from protein structures deposited in the Protein Data Bank. Domain identification and classification uses both manual and automated procedures. CATH includes domains from structures determined at 4 angstrom resolution or better that are at least 40 residues long with 70% or more residues having defined side chains. Submitted protein chains are divided into domains, which are then classified in CATH.
The document discusses the National Center for Biotechnology Information (NCBI), which maintains biological databases and provides bioinformatics tools. NCBI houses both primary databases directly submitted by researchers and secondary databases compiled from primary sources. Major databases include GenBank (nucleotide sequences), PubMed Central (biomedical literature), and reference sequence databases. Tools like BLAST, Entrez, and ORFfinder allow users to search and analyze sequence data. NCBI aims to make biomedical research data freely accessible worldwide.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
This document provides an overview of several important protein databases:
- SWISS-PROT is an annotated protein sequence database that is maintained collaboratively and contains over 1.29 million entries. TrEMBL is a computer-annotated supplement to SWISS-PROT containing sequences not yet in SWISS-PROT.
- Structural databases like PDB, SCOP, and CATH provide protein structure information. PDB is an international repository for macromolecular structures. SCOP and CATH classify protein domains based on structural similarities and evolutionary relationships.
- Other databases mentioned include InterPro, GOA, Proteome Analysis, and GenBank, which provide functional annotation, gene ontology assignments, proteome analysis
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
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.
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 CATH database hierarchically classifies protein domains obtained from protein structures deposited in the Protein Data Bank. Domain identification and classification uses both manual and automated procedures. CATH includes domains from structures determined at 4 angstrom resolution or better that are at least 40 residues long with 70% or more residues having defined side chains. Submitted protein chains are divided into domains, which are then classified in CATH.
The document discusses the National Center for Biotechnology Information (NCBI), which maintains biological databases and provides bioinformatics tools. NCBI houses both primary databases directly submitted by researchers and secondary databases compiled from primary sources. Major databases include GenBank (nucleotide sequences), PubMed Central (biomedical literature), and reference sequence databases. Tools like BLAST, Entrez, and ORFfinder allow users to search and analyze sequence data. NCBI aims to make biomedical research data freely accessible worldwide.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
Rasmol and Swiss-PDB viewer are molecular visualization tools that allow users to view and analyze protein structures. Rasmol can display molecules in various representations like wireframe, cylinders, or ribbons. It supports common file formats like PDB and can rotate, zoom, and translate structures. Swiss-PDB viewer is tightly integrated with homology modeling and allows users to build models, compare structures, and view electron density maps. It utilizes template structures from the PDB to generate models and assess their quality. Both tools provide publication-quality images and interactive visualization of biomolecular structures.
The document discusses various types of biological databases. It describes primary databases that contain original data, secondary databases that contain processed data derived from primary databases, and composite databases that collect and filter data from multiple primary databases. Examples of specific biological databases are provided, including nucleic acid databases like GenBank, protein sequence databases like Swiss-Prot, protein structure database PDB, and metabolic pathway database KEGG. Details about the purpose and features of some of these major databases like GenBank, DDBJ, EMBL, Swiss-Prot, and PDB are outlined in the document.
Biological databases store and organize biological data and information. There are two main types - primary databases that contain original experimental data that cannot be changed, and secondary databases that contain derived data analyzed from primary sources. Examples of primary databases include GenBank for DNA sequences and SWISS-PROT for protein sequences. Secondary databases include PROSITE for protein families and domains, and Pfam for protein family alignments. Biological databases allow sharing of genomic and protein information worldwide and provide a foundation for research.
Whole genome shotgun sequencing involves randomly breaking genomic DNA into small fragments, sequencing the fragments, and then reassembling the sequences using overlapping regions. The document outlines the history and procedure of shotgun sequencing. Genomic DNA is first fragmented, end-repaired, and size-selected into small, medium, and large fragments. Libraries are created for each size fragment and sequenced. A base caller filters poor calls and an assembler finds overlaps to generate continuous nucleotide sequences or contigs of the whole genome.
In shotgun sequencing the genome is broken randomly into short fragments (1 to 2 kbp long) suitable for sequencing. The fragments are ligated into a suitable vector and then partially sequenced. Around 400–500 bp of sequence can be generated from each fragment in a single sequencing run. In some cases, both ends of a fragment are sequenced. Computerized searching for overlaps between individual sequences then assembles the complete sequence.
This document discusses the Basic Local Alignment Search Tool (BLAST), which allows users to compare a query DNA or protein sequence against sequence databases to find regions of local similarity. BLAST breaks the query into short words that are then searched for in database sequences. When words are found in common, BLAST extends the alignment in both directions to find higher-scoring matches. BLAST outputs include a graphical display of alignments, a hit list ranking matches by similarity score, and detailed alignments. BLAST has many applications, such as identifying species, establishing evolutionary relationships, DNA mapping, and locating protein domains.
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.
This is technique used widely for protein separation from a mixture and is very easy and less costly method. Slides cover all essential points about EMSA and it is quite interesting to know that how it detect and separate different proteins and their mobility shift assay.
The document discusses several protein sequence databases including Swiss-Prot, GenPept/TREMBL, PIR, PDB, and MMDB. It provides details on Swiss-Prot, describing it as a manually curated database that distinguishes itself from others through annotations, minimal redundancy, and integration with other databases. The annotations in Swiss-Prot include core data as well as additional details on the protein's function, modifications, domains, structure, and relationships to other proteins and diseases.
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
This document discusses sequence alignment methods. It describes global and local alignment, and algorithms used for alignment including dot matrix analysis, dynamic programming, and word/k-tuple methods as implemented in FASTA and BLAST programs. BLAST and FASTA are described as popular tools for sequence database searches that use heuristic methods and word matching to quickly identify regions of local similarity.
The document provides an overview of the history and scope of bioinformatics. It discusses how bioinformatics emerged from the fields of computer science and biology. The history section outlines major developments from Mendel's work in 1865 to the sequencing of the human genome in 2001. Bioinformatics has various applications in areas like drug development, personalized medicine, and biotechnology. It also has significant scope in India, with growing job opportunities in both the public and private sectors.
The document provides an introduction to BLAST (Basic Local Alignment Search Tool), which is an algorithm used to compare gene and protein sequences to those in public databases. It discusses the types of BLAST programs, the BLAST algorithm, input/output, how to perform a BLAST search, and the functions and objectives of BLAST. Specifically, BLAST is faster than previous sequence comparison methods, it outputs alignments and statistical values to evaluate matches, and its main objectives are to identify related sequences and locate domains through local alignments.
This document discusses bioinformatics, including its goals and applications. Bioinformatics is defined as applying information technology to store, organize, and analyze vast amounts of biological data, such as sequences and structures of proteins and nucleic acids. It merges biology, mathematics, statistics, computer science, and information technology. Bioinformatics helps analyze gene and protein expression, compare genomic data, and simulate DNA, RNA, and proteins. It has applications in molecular medicine, drug development, microbial genomics, crop improvement, and more. Common bioinformatics tools include BLAST for comparing biological sequences.
Bioinformatics combines computer science, statistics, mathematics, and engineering to analyze biological data. Major bioinformatics databases and resources include NCBI, EMBL-EBI, and ExPASy. NCBI was established in 1988 as part of the National Library of Medicine and contains databases like PubMed, OMIM, and PubChem. EMBL-EBI was established in 1980 and provides DNA sequences and additional biological information through tools like Webin and SRS. ExPASy was established in 1993 by the Swiss Institute of Bioinformatics and contains protein databases like Swiss-Prot, TrEMBL, and InterPro.
The National Center for Biotechnology Information (NCBI) was created in 1988 as part of the National Library of Medicine at NIH. It establishes public databases for biological research, develops software tools for sequence analysis, and disseminates biomedical information from its location in Bethesda, MD. NCBI houses several integrated databases including PubMed, GenBank, RefSeq, and UniGene that contain literature, sequences, gene information, and more.
Protein databases contain information on protein sequences, structures, and functions. The major protein databases are:
- Protein Data Bank (PDB) which contains 3D protein structures determined via X-ray crystallography or NMR.
- Swiss-Prot which contains manually annotated protein sequences and functions.
- TrEMBL which supplements Swiss-Prot with automatically annotated translations of DNA sequences.
Protein databases are important for comparing proteins, understanding relationships between proteins, and aiding the study of new proteins. Searching databases is often the first step in protein research.
Entrez is a search engine developed by the National Center for Biotechnology Information (NCB) that allows users to search and retrieve data from over 20 integrated biological databases, including sequences, gene records, citations, and abstracts. It provides a single interface to access all linked information on genes and proteins. Users can perform text searches using Boolean operators and view results in various formats like FASTA and XML.
This document provides an overview of the FASTA software. FASTA is a program used by biologists to study and analyze DNA and protein sequences. It uses a simple text-based format to present sequences and allows for the naming of sequences and inclusion of comments. FASTA is a rapid program that can be used locally or through email servers to find regional similarities between sequences and identify potential matches while ignoring complete sensitivity. It has become a standard tool in biology for sequencing and analyzing proteins and DNA.
The European Bioinformatics Institute (EBI) is a center for bioinformatics research and services located in Hinxton, UK. EBI grew out of EMBL's work providing public biological databases and offers major databases on DNA, RNA, proteins, pathways, and more. EBI's website provides access to these databases as well as a variety of bioinformatics tools for sequence analysis, proteomics, microarrays, and more through different channels on their site.
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
Protein Chemistry-Proteomics-Lec1_Intro.pptSachin Teotia
Proteins can be separated and analyzed using various proteomics techniques. Two-dimensional gel electrophoresis (2D PAGE) separates intact proteins by their isoelectric point (pI) and molecular weight to visualize thousands of protein spots. However, 2D PAGE has limitations such as reproducibility issues. Liquid chromatography (LC) techniques like HPLC and multi-dimensional protein identification technology (MudPIT) provide alternative high resolution separations of protein mixtures and digested peptides. Mass spectrometry (MS) then analyzes intact proteins or peptides separated by these methods to identify proteins by mass and sequence information.
Rasmol and Swiss-PDB viewer are molecular visualization tools that allow users to view and analyze protein structures. Rasmol can display molecules in various representations like wireframe, cylinders, or ribbons. It supports common file formats like PDB and can rotate, zoom, and translate structures. Swiss-PDB viewer is tightly integrated with homology modeling and allows users to build models, compare structures, and view electron density maps. It utilizes template structures from the PDB to generate models and assess their quality. Both tools provide publication-quality images and interactive visualization of biomolecular structures.
The document discusses various types of biological databases. It describes primary databases that contain original data, secondary databases that contain processed data derived from primary databases, and composite databases that collect and filter data from multiple primary databases. Examples of specific biological databases are provided, including nucleic acid databases like GenBank, protein sequence databases like Swiss-Prot, protein structure database PDB, and metabolic pathway database KEGG. Details about the purpose and features of some of these major databases like GenBank, DDBJ, EMBL, Swiss-Prot, and PDB are outlined in the document.
Biological databases store and organize biological data and information. There are two main types - primary databases that contain original experimental data that cannot be changed, and secondary databases that contain derived data analyzed from primary sources. Examples of primary databases include GenBank for DNA sequences and SWISS-PROT for protein sequences. Secondary databases include PROSITE for protein families and domains, and Pfam for protein family alignments. Biological databases allow sharing of genomic and protein information worldwide and provide a foundation for research.
Whole genome shotgun sequencing involves randomly breaking genomic DNA into small fragments, sequencing the fragments, and then reassembling the sequences using overlapping regions. The document outlines the history and procedure of shotgun sequencing. Genomic DNA is first fragmented, end-repaired, and size-selected into small, medium, and large fragments. Libraries are created for each size fragment and sequenced. A base caller filters poor calls and an assembler finds overlaps to generate continuous nucleotide sequences or contigs of the whole genome.
In shotgun sequencing the genome is broken randomly into short fragments (1 to 2 kbp long) suitable for sequencing. The fragments are ligated into a suitable vector and then partially sequenced. Around 400–500 bp of sequence can be generated from each fragment in a single sequencing run. In some cases, both ends of a fragment are sequenced. Computerized searching for overlaps between individual sequences then assembles the complete sequence.
This document discusses the Basic Local Alignment Search Tool (BLAST), which allows users to compare a query DNA or protein sequence against sequence databases to find regions of local similarity. BLAST breaks the query into short words that are then searched for in database sequences. When words are found in common, BLAST extends the alignment in both directions to find higher-scoring matches. BLAST outputs include a graphical display of alignments, a hit list ranking matches by similarity score, and detailed alignments. BLAST has many applications, such as identifying species, establishing evolutionary relationships, DNA mapping, and locating protein domains.
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.
This is technique used widely for protein separation from a mixture and is very easy and less costly method. Slides cover all essential points about EMSA and it is quite interesting to know that how it detect and separate different proteins and their mobility shift assay.
The document discusses several protein sequence databases including Swiss-Prot, GenPept/TREMBL, PIR, PDB, and MMDB. It provides details on Swiss-Prot, describing it as a manually curated database that distinguishes itself from others through annotations, minimal redundancy, and integration with other databases. The annotations in Swiss-Prot include core data as well as additional details on the protein's function, modifications, domains, structure, and relationships to other proteins and diseases.
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
This document discusses sequence alignment methods. It describes global and local alignment, and algorithms used for alignment including dot matrix analysis, dynamic programming, and word/k-tuple methods as implemented in FASTA and BLAST programs. BLAST and FASTA are described as popular tools for sequence database searches that use heuristic methods and word matching to quickly identify regions of local similarity.
The document provides an overview of the history and scope of bioinformatics. It discusses how bioinformatics emerged from the fields of computer science and biology. The history section outlines major developments from Mendel's work in 1865 to the sequencing of the human genome in 2001. Bioinformatics has various applications in areas like drug development, personalized medicine, and biotechnology. It also has significant scope in India, with growing job opportunities in both the public and private sectors.
The document provides an introduction to BLAST (Basic Local Alignment Search Tool), which is an algorithm used to compare gene and protein sequences to those in public databases. It discusses the types of BLAST programs, the BLAST algorithm, input/output, how to perform a BLAST search, and the functions and objectives of BLAST. Specifically, BLAST is faster than previous sequence comparison methods, it outputs alignments and statistical values to evaluate matches, and its main objectives are to identify related sequences and locate domains through local alignments.
This document discusses bioinformatics, including its goals and applications. Bioinformatics is defined as applying information technology to store, organize, and analyze vast amounts of biological data, such as sequences and structures of proteins and nucleic acids. It merges biology, mathematics, statistics, computer science, and information technology. Bioinformatics helps analyze gene and protein expression, compare genomic data, and simulate DNA, RNA, and proteins. It has applications in molecular medicine, drug development, microbial genomics, crop improvement, and more. Common bioinformatics tools include BLAST for comparing biological sequences.
Bioinformatics combines computer science, statistics, mathematics, and engineering to analyze biological data. Major bioinformatics databases and resources include NCBI, EMBL-EBI, and ExPASy. NCBI was established in 1988 as part of the National Library of Medicine and contains databases like PubMed, OMIM, and PubChem. EMBL-EBI was established in 1980 and provides DNA sequences and additional biological information through tools like Webin and SRS. ExPASy was established in 1993 by the Swiss Institute of Bioinformatics and contains protein databases like Swiss-Prot, TrEMBL, and InterPro.
The National Center for Biotechnology Information (NCBI) was created in 1988 as part of the National Library of Medicine at NIH. It establishes public databases for biological research, develops software tools for sequence analysis, and disseminates biomedical information from its location in Bethesda, MD. NCBI houses several integrated databases including PubMed, GenBank, RefSeq, and UniGene that contain literature, sequences, gene information, and more.
Protein databases contain information on protein sequences, structures, and functions. The major protein databases are:
- Protein Data Bank (PDB) which contains 3D protein structures determined via X-ray crystallography or NMR.
- Swiss-Prot which contains manually annotated protein sequences and functions.
- TrEMBL which supplements Swiss-Prot with automatically annotated translations of DNA sequences.
Protein databases are important for comparing proteins, understanding relationships between proteins, and aiding the study of new proteins. Searching databases is often the first step in protein research.
Entrez is a search engine developed by the National Center for Biotechnology Information (NCB) that allows users to search and retrieve data from over 20 integrated biological databases, including sequences, gene records, citations, and abstracts. It provides a single interface to access all linked information on genes and proteins. Users can perform text searches using Boolean operators and view results in various formats like FASTA and XML.
This document provides an overview of the FASTA software. FASTA is a program used by biologists to study and analyze DNA and protein sequences. It uses a simple text-based format to present sequences and allows for the naming of sequences and inclusion of comments. FASTA is a rapid program that can be used locally or through email servers to find regional similarities between sequences and identify potential matches while ignoring complete sensitivity. It has become a standard tool in biology for sequencing and analyzing proteins and DNA.
The European Bioinformatics Institute (EBI) is a center for bioinformatics research and services located in Hinxton, UK. EBI grew out of EMBL's work providing public biological databases and offers major databases on DNA, RNA, proteins, pathways, and more. EBI's website provides access to these databases as well as a variety of bioinformatics tools for sequence analysis, proteomics, microarrays, and more through different channels on their site.
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
Protein Chemistry-Proteomics-Lec1_Intro.pptSachin Teotia
Proteins can be separated and analyzed using various proteomics techniques. Two-dimensional gel electrophoresis (2D PAGE) separates intact proteins by their isoelectric point (pI) and molecular weight to visualize thousands of protein spots. However, 2D PAGE has limitations such as reproducibility issues. Liquid chromatography (LC) techniques like HPLC and multi-dimensional protein identification technology (MudPIT) provide alternative high resolution separations of protein mixtures and digested peptides. Mass spectrometry (MS) then analyzes intact proteins or peptides separated by these methods to identify proteins by mass and sequence information.
1) Proteomics is the large-scale study of proteins, including their structures and functions. It involves techniques like mass spectrometry, 2D gel electrophoresis, and protein sequencing to identify and quantify proteins in biological samples.
2) Proteins are the functional units of cells and carry out many important roles. Studying proteins helps understand biological processes, identify drug targets, and determine the causes of diseases.
3) Techniques in proteomics include separating protein mixtures, identifying proteins through mass spectrometry and database matching, and analyzing post-translational modifications, interactions, and expression levels. This provides insights into protein functions and cellular pathways.
InterPro is a database that classifies proteins into families, domains, and sequence features based on their structural and functional properties. It integrates predictive models from several member databases to annotate unknown protein sequences. Protein signatures like patterns, profiles, fingerprints and hidden Markov models are generated from multiple sequence alignments and used by InterPro for classification. AlphaFold is an artificial intelligence system that can predict protein three-dimensional structures directly from amino acid sequences, representing a major advance in solving the protein folding problem.
Characterizing Protein Families of Unknown FunctionMorgan Langille
by Morgan G. I. Langille & Jonathan A. Eisen. This scientific poster was presented at the 18th Annual International Meeting on Microbial Genomics at Lake Arrowhead, California, USA. Sept. 12-16, 2010.
Pfam is a database of protein families that contains their annotations and multiple sequence alignments generated using hidden Markov models. It was originally created to aid in genome annotation and currently contains over 16,000 protein families. Pfam is freely accessible and widely used for research into protein structure, function, and evolution. It allows users to submit sequences to search for matches to protein families in the database.
Bioinformatics, application by kk sahu sirKAUSHAL SAHU
INTRODUCTION
HISTORY
WHAT IS BIOINFORMATICS
APPLICATIONS
DNA AND RNA LEVELS
CONCLUSION
REFRENCES
"Bioinformatics" to refer to the study of information processes in biotic systems. This definition placed bioinformatics as a field parallel to biophysics or biochemistry (biochemistry is the study of chemical processes in biological systems).
the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.
This document provides an overview of protein sequence analysis techniques. It discusses using databases like UniProt to search unknown protein sequences and analyze BLAST search results. It also describes tools for predicting protein structure and function from sequence, including secondary structure prediction, homology modeling, and multiple sequence alignments to identify evolutionary relationships between proteins. The goal of protein sequence analysis is to characterize proteins in silico and infer their potential structure and function based on sequence similarity and evolutionary relationships to other known proteins.
Biological data is widely distributed over the web and can be retrieved using search engines like Google or data retrieval tools. Dedicated data retrieval tools for molecular biologists include Entrez, DBGET, and SRS which allow text searching of linked databases and sequence searching. Entrez, developed by NCBI, integrates information from databases including GenBank, PubMed, and OMIM. DBGET covers databases like GenBank, EMBL, and PDB. SRS, developed by EBI, integrates over 80 molecular biology databases.
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
Theoretical evaluation of shotgun proteomic analysis strategies; Peptide obse...Keiji Takamoto
This document discusses evaluating different strategies for shotgun proteomic analysis through theoretical modeling. It develops a peptide observability function based on mouse proteomic data to predict how observable peptides are by LC-MS/MS. This function is applied to theoretically digested mouse proteins using different proteases and separation techniques to evaluate their combinations and the separation profiles achieved. The results suggest SAX/trypsin and IEF/trypsin are favorable combinations that provide good separation.
1) AbstractDB & ProteinComplexDB are databases that contain protein complexes extracted from PubMed abstracts along with the abstracts themselves.
2) The databases were developed using a Bayesian classifier to rank abstracts by their relevance to protein complexes based on the frequency of discriminatory words.
3) The databases allow users to validate extracted protein complexes by searching against known complex databases and enable scientists to evaluate and revise the data.
BTC 506 Gene Identification using Bioinformatic Tools-230302130331.pptxChijiokeNsofor
This document discusses several bioinformatics tools and methods for identifying genes from genomic sequences, including:
1. Obtaining sequence data through sequencing technologies and preprocessing data.
2. Using tools like Ensembl, RefSeq and UCSC Genome Browser for gene identification and annotation.
3. Using gene prediction tools like Augustus, GeneMark and Glimmer to predict gene locations and structures.
4. Validating predicted genes through comparison to known genes or experimental validation with RNA-seq or RT-PCR.
Proteomics: lecture (1) introduction to proteomicsClaudine83
This document provides information about a course titled "Proteomics & Bioinformatics" taking place from May 7-11, 2007 in Helsinki, Finland. The course will give an introduction to available proteomic technologies and data mining tools. It will be taught by Sophia Kossida of the Foundation for Biomedical Research of the Academy of Athens, Greece and Esa Pitkänen and Juho Rousu of the University of Helsinki, Finland.
This document discusses various bioinformatics tools and methods for identifying genes from genomic sequences. It begins by defining genes and genomes, then describes reference databases like RefSeq that are important for gene identification. It outlines the general workflow for gene identification, including obtaining sequences, preprocessing, annotation, prediction, and validation. Specific tools mentioned include GENSCAN, Glimmer, and Augustus for gene prediction, and BLAST for sequence alignment. The document also discusses identifying other genomic features like promoters, repeats, and open reading frames. It emphasizes that accurate gene identification requires both computational and experimental approaches.
This document describes the PRESAGE database, which aims to improve communication among structural genomics researchers. The database contains protein sequence annotations from experimental and computational research. Researchers can submit annotations about protein structures they are studying experimentally or predicting computationally. The annotations are classified as experimental to track experimental progress, or prediction at three levels of detail. The database is publicly available online and allows registered users to receive notifications about annotations of interest.
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Bioinformatics is the use of computer science and statistical techniques to analyze and interpret biological data. It involves developing tools to access and manage biological data, analyzing sequences like DNA and proteins, and developing algorithms to understand relationships within large data sets. The main areas of bioinformatics are molecular, cellular, and organismal/community levels. It is used for tasks like gene finding, predicting protein structure and function, understanding evolutionary relationships, and aiding drug discovery.
This document discusses protein secondary structure and methods for predicting it. It introduces protein secondary structure including alpha helices and beta pleated sheets. It then describes several common methods for predicting secondary structure from a protein's amino acid sequence, such as the Chou-Fasman method, nearest neighbor method, hidden Markov models, neural networks, and multiple sequence alignments. The accurate prediction of protein secondary structure from sequence is important for understanding protein structure and function.
Genome and Proteome data integration in RDFNadia Anwar
The document summarizes integrating genome and proteome data from Francisella tularensis in RDF. It discusses integrating data from multiple sources, including genome annotations, proteomics experiments, and transcriptomics data. Semantic data integration across "omes" data silos is demonstrated using RDF and the open source Sesame framework. Reifying biological statements, such as identified peptides and abundances, allows more complex queries across the integrated data.
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2. Date 2
Pitching in
Protein Families and the need
for classification
Domains & Motifs with GPCRs
as example
Vrinda Sharma
Groundwork
Sequence Features
Protein Signatures
Patterns & Profiles
HMMs
Wanchha Maurya
Showstopper
DUFs- a story worth reciting
Databases of Protein Families
Demistifying the Hypotheticals
Rohit Satyam
3. Need for classification
Date 3
Proteins can be classified into
groups based on sequence or
structural similarity.
These groups often contain
well characterised proteins
whose function is known.
Thus, when a novel protein is
identified, its functional
properties can be proposed
based on the group to which
it is predicted to belong.
Source: EMBL-EBI Training Course:
https://www.ebi.ac.uk/training-
beta/online/courses/protein-classification-intro-ebi-
resources/protein-classification/what-are-protein-
families/
4. Protein Families in Brief
Date Your Footer Here
Group of Proteins which
• Shares a common evolutionary origin
• Performs related functions
• Similar in sequence or structure.
Superfamily
Family A
Subfamily
A1
Subfamily
A2
Family B Family C
Subfamily
C1
Subfamily
C2
Subfamily
C3
5. Domain and Motifs
aren’t synonyms
Date
Domains are distinct functional and/or structural
units in a protein.
They are responsible for a particular function or
interaction, contributing to the overall role of a
protein.
Motifs are secondary structure that are formed
due to interaction between alpha-helices and
beta-sheets.
Structure of the SH3 domain
Domain composition of Nck. Nck contains three
SH3 domains plus another domain known as SH2
7. G-Protein Signaling
Date Your Footer Here
• Regulator of GPS domains are protein
structural units that activate GTPase.
• sequences belonging to RGS protein
family(multifunctional GTPase accelerating
protein).
• All RGS protein family member contains RGS
domain ,some (RGS1) consist little more than
domain .
• RGS3 and RGS6 contain additional domains for
other functions .
8. They have seven transmembrane
domains, and interact with
specialized proteins (called G
proteins) to influence intracellular
pathways after binding
extracellular signals
G-protein-coupled receptors
and cancer
Dorsam et al 2007
9. Date Your Footer Here 9
Level2
Level 1
Sub-family
Superfamily GPCRs
Rhodopsin
-like GPCRs
Opsins
Red-
sensitive
opsins
Green-
sensitive
opsins
Blue-
sensitive
opsins
APJ
receptors
Relaxin
Receptors
cAMP
Receptors
Secretin like-
GPCRs
Etc…
The GPCR superfamily hierarchy. Families and subfamilies to which the short-wave-sensitive opsin 1
protein belongs are highlighted in violet.
GPCRs
Regulates: Biological processes, including photoreception, regulation of the immune system, and nervous system
transmission.
Similarity
increases
10. Date 10
What Are Sequence Features?
1.Active Site
2.Binding Site
3. Post Translational Modifications (PTMs)
4. Repeats
Group of amino acid that confer certain characteristics upon a protein ,and maybe important for
overall function
11. Date 11
Protein Signatures
• To classify protein’s family and to
predict the domains or sequence
features we use computational tools
and that tools are the predictive
models known as protein signatures.
• Model refines distantly related
sequences in database are identified.
• Once the model is mature, signature
is ready for protein sequence
analysis.
The Purpose and the Process
12. Date 12
How do Protein Signature compare to other
ways of classifying proteins?
• Multiple sequence alignment gives
us information about classification
which we use to identify amino acid
residues that are conserved in
distantly proteins.
• Protein signature built from
multiple sequence alignment are
usually better at detecting
divergent homologues than
pairwise comparison method.
Identifying the conserved residues
14. Patterns & Profiles
Date 14
Signature Types
Patterns can recognize sequence
features such as binding sites or
active sites of enzymes consist of a
only few amino acids.
Ex: PROSITE database.
1 2
Profiles are built by converting
multiple sequence alignment into
position specific scoring system
(PMMs).
Ex: CDD, HAMAP, PROSITE and
PRODOM.
15. Fingerprints and HMMs
Date 15
Signature Types
3 4
Fingerprints are composed of multiple
short conserved motifs which are drawn
from sequence alignment. They can
distinguish individual subfamilies within
protein families.
Ex : PRINTS database.
Hidden Markov models (HMMs) are
used to convert multiple sequence
alignment into position specific
scoring system.
Ex: Pfam, SMART, TIGRFAM,
PANTHER, SFLD, Superfamily
and Gene 3D.
16. Date 16
Families in search of function
Domains of unknown function (DUFs)
Popovic et al., 2017.,Scientific reports,
The function of the Domain is yet to be discovered
The DUF naming scheme was introduced by Chris
Ponting through the addition of DUF1 and DUF2 to
the SMART database
Goodacre et al 2014.
17.
18. Databases at Glance
Date 18
Databases of
Protein Families
5. PRINTS
Combine Multidomain/motif
information for family categorization.
MSA and Fuzzy Logic (Regex)
6. MobiDB
Homology, Predicted, Curated
Intrinsically Disordered regions
database
7. TIGRFAM
MSA, HMM mainly for prokaryotic
proteins
8. SUPERFAMILY2
Using HMM and protein Sequences
Domain organisation, sequence alignments
and protein sequence details can be
obtained for query sequence
4. PRIDE
Mass-Spec based identification
Provide PTM information and Literature
Evidences
3. Prosite
MSA of homologous Proteins;Based on
Prorules
2. PIRSF
MSA and Clustering with hight similarity
thresholds
1. Pfam
Protein Family, Domains, Motifs and Repeats
(Generated from MSA and HMMs)
1
3 5
7
2
4
8
6
22. Date 22
References
• Dorsam, R.T. and Gutkind, J.S., 2007. G-protein-coupled receptors and cancer. Nature reviews
cancer, 7(2), pp.79-94.
• Bateman, Alex, Penny Coggill, and Robert D. Finn. "DUFs: families in search of function." Acta
Crystallographica Section F: Structural Biology and Crystallization Communications 66, no. 10
(2010): 1148-1152.
• Goodacre, Norman F., Dietlind L. Gerloff, and Peter Uetz. "Protein domains of unknown function are
essential in bacteria." MBio 5, no. 1 (2014).
• EMBL-EBI Training Course: https://www.ebi.ac.uk/training-beta/online/courses/protein-
classification-intro-ebi-resources/protein-classification/what-are-protein-families/