The document discusses different types of biological databases including:
- Primary databases which archive experimental data without modification.
- Secondary databases which apply algorithms to analyze and curate primary data.
- Derived databases which filter and merge data from primary databases based on criteria.
It provides examples of popular protein, structure, pathway, and other biological databases that store and organize various types of biological data.
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
Bioinformatics is the application of computer science and information technology to biological data. It helps analyze biological data to gain understanding. Biological databases store biological information collected from experiments in an organized manner. There are primary databases containing raw experimental data and secondary databases containing analyzed data. Major types of biological databases include sequence databases for nucleic acid and protein sequences, and structural databases like PDB for 3D protein structures. Databases can be retrieved using tools like Entrez, SRS, and BLAST to find related sequences and information. Biological databases play an important role in research by acting as repositories of information.
Protein databases can contain either sequence or structure information. Some key protein sequence databases include PIR, Swiss-Prot, and TrEMBL. PIR classifies entries by annotation level, Swiss-Prot aims to provide high annotation levels and interlink information, and TrEMBL contains all coding sequences with some entries eventually incorporated into Swiss-Prot. Important structure databases are PDB, which contains 3D protein structures, and SCOP and CATH, which classify evolutionary and structural relationships between protein domains.
This document discusses protein sequence databases and their role in storing protein data generated from genome projects and new proteomics technologies. It describes several types of protein databases, including universal repositories like GenPept that store sequences with little annotation, and expertly curated databases like Swiss-Prot that enrich sequence data with additional validation and integration. Specialized databases also exist that focus on specific protein families, organisms, structures like SCOP, or classifications like CATH.
The document discusses various biological databases including:
1. Nucleic acid, protein, and structure databases that store gene and protein sequence data, protein structures, and related information.
2. Specialized databases focused on specific topics like virus structures or immunology.
3. Expression and proteomics databases that record gene expression measurements.
Bioinformatics is the application of Information technology to store, organize and analyze the vast amount of biological data which is available in the form of sequences and structures of proteins and nucleic acids. The biological information of nucleic acids is available as sequences while the data of proteins is available as sequences and structures.
A biological database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The activity of preparing a database can be divided in to:
Collection of data in a form which can be easily accessed
Making it available to a multi-user system (always available for the user)
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.
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
Bioinformatics is the application of computer science and information technology to biological data. It helps analyze biological data to gain understanding. Biological databases store biological information collected from experiments in an organized manner. There are primary databases containing raw experimental data and secondary databases containing analyzed data. Major types of biological databases include sequence databases for nucleic acid and protein sequences, and structural databases like PDB for 3D protein structures. Databases can be retrieved using tools like Entrez, SRS, and BLAST to find related sequences and information. Biological databases play an important role in research by acting as repositories of information.
Protein databases can contain either sequence or structure information. Some key protein sequence databases include PIR, Swiss-Prot, and TrEMBL. PIR classifies entries by annotation level, Swiss-Prot aims to provide high annotation levels and interlink information, and TrEMBL contains all coding sequences with some entries eventually incorporated into Swiss-Prot. Important structure databases are PDB, which contains 3D protein structures, and SCOP and CATH, which classify evolutionary and structural relationships between protein domains.
This document discusses protein sequence databases and their role in storing protein data generated from genome projects and new proteomics technologies. It describes several types of protein databases, including universal repositories like GenPept that store sequences with little annotation, and expertly curated databases like Swiss-Prot that enrich sequence data with additional validation and integration. Specialized databases also exist that focus on specific protein families, organisms, structures like SCOP, or classifications like CATH.
The document discusses various biological databases including:
1. Nucleic acid, protein, and structure databases that store gene and protein sequence data, protein structures, and related information.
2. Specialized databases focused on specific topics like virus structures or immunology.
3. Expression and proteomics databases that record gene expression measurements.
Bioinformatics is the application of Information technology to store, organize and analyze the vast amount of biological data which is available in the form of sequences and structures of proteins and nucleic acids. The biological information of nucleic acids is available as sequences while the data of proteins is available as sequences and structures.
A biological database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The activity of preparing a database can be divided in to:
Collection of data in a form which can be easily accessed
Making it available to a multi-user system (always available for the user)
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.
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/
Sequence and Structural Databases of DNA and Protein, and its significance in...SBituila
This document discusses various DNA and protein sequence and structural databases, including their history, roles, and available tools. Some of the key databases mentioned are NCBI, EMBL, DDBJ, GenBank, UniProt, and PDB. NCBI maintains large public nucleotide and protein databases and provides analysis tools. EMBL collects and distributes sequence data. PDB is a database for 3D structural data of biomolecules. Together, these databases provide essential resources for genomic and proteomic research.
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.
This document provides an overview of protein databases. It discusses the importance of protein databases for storing and analyzing protein sequence, structure, and functional data generated by modern biology. It summarizes several major public protein databases, including UniProt, NCBI RefSeq, PDB, InterPro, and Pfam, which contain protein sequences, structures, families, domains, and functional annotations. Searching and comparing sequences in these databases is an important first step in studying new proteins.
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.
The document discusses protein sequence databases which store large amounts of data generated from genome projects and protein analysis technologies. It describes two main types - sequence repositories which store raw sequences with little annotation, and expertly curated databases like Swiss-Prot and PIR which enrich sequences with additional validated information. It also covers protein structure databases SCOP and CATH which classify domains based on structural similarities and evolutionary relationships.
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
The document discusses various types of biological databases that contain information on amino acids, nucleic acids, proteins and their structures. It describes primary databases that contain sequence data as well as secondary and tertiary databases that include protein structure information like motifs, domains and atomic coordinates derived from techniques like X-ray crystallography. Major databases discussed include Swiss-Prot, PDB, EMBL and their roles in archiving sequence data, annotating proteins and classifying structural information using systems like SCOP and CATH.
The document discusses protein structure concepts and related computational problems. It covers what proteins are made of, including amino acids, peptides, and protein structure levels from primary to quaternary. It also discusses protein domains, structural databases like PDB and SCOP, and principles of structural alignment and superimposition to identify structural similarities between proteins.
This document summarizes different types of biological data and biological databases. It discusses primary databases like GenBank, EMBL and DDBJ that contain raw nucleotide sequence data. Secondary databases like KEGG and Pfam analyze and annotate primary database content. Composite databases like NCBI aggregate data from multiple primary sources. Protein databases discussed include Swiss-Prot, TrEMBL, PDB, and Pfam. Structural databases such as SCOP, CATH and PDB organize protein structures.
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.
The document summarizes the various database resources and tools provided by the National Center for Biotechnology Information (NCBI). It describes NCBI's data retrieval systems like Entrez and PubMed, sequence analysis tools like BLAST, and resources for gene sequences, chromosomal sequences, genomes, gene expression, and protein structures. NCBI maintains the GenBank nucleic acid sequence database and provides data retrieval, analysis, and additional biological resources through its website.
This document discusses biological databases. It begins by defining biological databases as large, organized bodies of persistent biological data that can be updated, queried and retrieved. It then provides examples of popular databases like GenBank, SwissProt and PIR. The document discusses the importance of databases and different types of biological databases, categorized by the content or nature of the data. Specifically, it describes primary and secondary nucleotide and protein sequence databases like GenBank, EMBL, DDBJ, SwissProt and PIR.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
The document discusses different text-based database retrieval systems for accessing biological data, including Entrez, SRS, and DBGET/LinkDB. It describes their key features and how each system allows users to search text databases using queries, with Entrez providing linked related data across multiple databases. An example shows how each system can be used to retrieve and view related information for a SwissProt protein entry.
BITS: Overview of important biological databases beyond sequencesBITS
Module 4 Other relevant biological data sources beyond sequences
Part of training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
This document discusses biological databases. It defines biological databases as structured, searchable collections of biological data that are periodically updated and cross-referenced. It notes that biological databases store data electronically and systematize, make available, and allow analysis of computed biological data. The document then describes some key features of biological databases, including data heterogeneity, high data volumes, uncertainty, data curation, integration, sharing, and dynamic nature. It also provides examples of different types of biological databases classified by data type, maintainer, access, source, design, and organism covered.
This document summarizes biological databases and provides examples. It discusses:
1. The need for biological databases to store and analyze the large amounts of data generated by experiments.
2. Examples of common database types including nucleotide sequences, protein sequences, enzymes, pathways, literature, and more.
3. How sequence alignment tools like BLAST are used to compare queries to database entries and identify similar sequences.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
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/
Sequence and Structural Databases of DNA and Protein, and its significance in...SBituila
This document discusses various DNA and protein sequence and structural databases, including their history, roles, and available tools. Some of the key databases mentioned are NCBI, EMBL, DDBJ, GenBank, UniProt, and PDB. NCBI maintains large public nucleotide and protein databases and provides analysis tools. EMBL collects and distributes sequence data. PDB is a database for 3D structural data of biomolecules. Together, these databases provide essential resources for genomic and proteomic research.
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.
This document provides an overview of protein databases. It discusses the importance of protein databases for storing and analyzing protein sequence, structure, and functional data generated by modern biology. It summarizes several major public protein databases, including UniProt, NCBI RefSeq, PDB, InterPro, and Pfam, which contain protein sequences, structures, families, domains, and functional annotations. Searching and comparing sequences in these databases is an important first step in studying new proteins.
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.
The document discusses protein sequence databases which store large amounts of data generated from genome projects and protein analysis technologies. It describes two main types - sequence repositories which store raw sequences with little annotation, and expertly curated databases like Swiss-Prot and PIR which enrich sequences with additional validated information. It also covers protein structure databases SCOP and CATH which classify domains based on structural similarities and evolutionary relationships.
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
The document discusses various types of biological databases that contain information on amino acids, nucleic acids, proteins and their structures. It describes primary databases that contain sequence data as well as secondary and tertiary databases that include protein structure information like motifs, domains and atomic coordinates derived from techniques like X-ray crystallography. Major databases discussed include Swiss-Prot, PDB, EMBL and their roles in archiving sequence data, annotating proteins and classifying structural information using systems like SCOP and CATH.
The document discusses protein structure concepts and related computational problems. It covers what proteins are made of, including amino acids, peptides, and protein structure levels from primary to quaternary. It also discusses protein domains, structural databases like PDB and SCOP, and principles of structural alignment and superimposition to identify structural similarities between proteins.
This document summarizes different types of biological data and biological databases. It discusses primary databases like GenBank, EMBL and DDBJ that contain raw nucleotide sequence data. Secondary databases like KEGG and Pfam analyze and annotate primary database content. Composite databases like NCBI aggregate data from multiple primary sources. Protein databases discussed include Swiss-Prot, TrEMBL, PDB, and Pfam. Structural databases such as SCOP, CATH and PDB organize protein structures.
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.
The document summarizes the various database resources and tools provided by the National Center for Biotechnology Information (NCBI). It describes NCBI's data retrieval systems like Entrez and PubMed, sequence analysis tools like BLAST, and resources for gene sequences, chromosomal sequences, genomes, gene expression, and protein structures. NCBI maintains the GenBank nucleic acid sequence database and provides data retrieval, analysis, and additional biological resources through its website.
This document discusses biological databases. It begins by defining biological databases as large, organized bodies of persistent biological data that can be updated, queried and retrieved. It then provides examples of popular databases like GenBank, SwissProt and PIR. The document discusses the importance of databases and different types of biological databases, categorized by the content or nature of the data. Specifically, it describes primary and secondary nucleotide and protein sequence databases like GenBank, EMBL, DDBJ, SwissProt and PIR.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
The document discusses different text-based database retrieval systems for accessing biological data, including Entrez, SRS, and DBGET/LinkDB. It describes their key features and how each system allows users to search text databases using queries, with Entrez providing linked related data across multiple databases. An example shows how each system can be used to retrieve and view related information for a SwissProt protein entry.
BITS: Overview of important biological databases beyond sequencesBITS
Module 4 Other relevant biological data sources beyond sequences
Part of training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
This document discusses biological databases. It defines biological databases as structured, searchable collections of biological data that are periodically updated and cross-referenced. It notes that biological databases store data electronically and systematize, make available, and allow analysis of computed biological data. The document then describes some key features of biological databases, including data heterogeneity, high data volumes, uncertainty, data curation, integration, sharing, and dynamic nature. It also provides examples of different types of biological databases classified by data type, maintainer, access, source, design, and organism covered.
This document summarizes biological databases and provides examples. It discusses:
1. The need for biological databases to store and analyze the large amounts of data generated by experiments.
2. Examples of common database types including nucleotide sequences, protein sequences, enzymes, pathways, literature, and more.
3. How sequence alignment tools like BLAST are used to compare queries to database entries and identify similar sequences.
Similar a Types of biological databases-protein database (20)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
2. 1/23/2024 Computational Structural Biology (BIO455) - CC 87
Biological data are complex, exception-ridden, vast and incomplete. A collection of biological data arranged
in computer readable form that enhances the speed of search and retrieval and convenient to use is called
biological database.
The main purpose of a biological database is to store and manage biological data and information in
computer readable forms.
A range of information like
biological sequences
structures
binding sites
metabolic interactions
molecular action
functional relationships
protein families, motifs and homologous
can be retrieved by using biological databases.
Biological databases
3. 1/23/2024 Computational Structural Biology (BIO455) - CC 88
It can also be called an archival database since it archives the experimental results submitted by the
scientists.
The primary database is populated with experimentally derived data like genome sequence, macromolecular
structure, etc. The data entered here remains uncurated (no modifications are performed over the data).
It contains unique data obtained from the laboratory and these data are made accessible to normal users
without any change.
The data are given accession numbers when they are entered into the database. The same data can later be
retrieved using the accession number. Accession number identifies each data uniquely and it never changes.
Examples –
Nucleic Acid Databases: GenBank and DDBJ
Protein Databases: PDB,SwissProt, PIR, TrEMBL, Metacyc, etc.
Primary databases
4. 1/23/2024 Computational Structural Biology (BIO455) - CC 89
The data stored in these types of databases are the analyzed result of the primary database.
Computational algorithms are applied to the primary database and meaningful and informative data is
stored inside the secondary database.
The data here are highly curated(processing the data before it is presented in the database).
A secondary database is better and contains more valuable knowledge compared to the primary database.
Examples:
InterPro (protein families, motifs, and domains)
UniProt Knowledgebase (sequence and functional information on proteins)
Secondary Database:
5. 1/23/2024 Computational Structural Biology (BIO455) - CC 90
The data entered in these types of databases are first compared and then filtered based on desired criteria.
The initial data are taken from the primary database, and then they are merged together based on certain
conditions.
It helps in searching sequences rapidly. Derived Databases contain non-redundant data.
Derived Databases
Examples:
SCOP, CATH, KEGG
7. 1/23/2024 Computational Structural Biology (BIO455) - CC 92
Protein Sequence Databases
PIR
( https://proteininformationresource.org/ )
PIR (Protein Information Resource) is a popular protein sequence database that provides information on
functionally annotated protein sequences.
PIR maintains three databases, the Protein Sequence Database (PSD), the Non-redundant Reference (NREF)
sequence database, and the integrated Protein Classification (iProClass) database, which contains
annotated protein sequences, classification information, and protein family, function, and structure
information.
8. 1/23/2024 Computational Structural Biology (BIO455) - CC
93
SWISS-PROT
(integrated with Uniprot)
SWISS-PROT is a protein sequence database that provides high levels of annotations, including
information on the protein’s function, domain structure, post-translational modifications, and variants.
Swiss-Prot is jointly managed by the SIB (Swiss Institute of Bioinformatics) and the EBI (European
Bioinformatics Institute).
The database distinguishes itself from other protein sequence databases by three criteria:
(i) annotations, which cover a broad range of information,
(ii) minimal redundancy, which ensures that each sequence is represented only once, and
(iii) integration with other databases, which enables cross-referencing and retrieval of information from
related databases.
TrEMBL
TrEMBL is a computer-annotated supplement of Swiss-Prot. TrEMBL entries follow the Swiss-Prot format.
It contains all the translations of EMBL (European Molecular Biology Laboratory) nucleotide sequence entries
that have not yet been integrated into Swiss-Prot.
9. 1/23/2024 Computational Structural Biology (BIO455) - CC 94
Protein Structure Databases: Protein Data Bank (PDB)
Protein structure databases are collections of information related to the
three-dimensional structure and secondary structure of proteins.
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological
molecules, such as proteins and nucleic acids.
The data, typically obtained by X-ray crystallography, NMR spectroscopy, or, increasingly, cryo-electron
microscopy, and submitted by biologists and biochemists from around the world, are freely accessible on
the Internet via the websites of its member organisations, PDBe, PDBj, RCSB, and BMRB
Most major scientific journals and some funding agencies now require scientists to submit their structure
data to the PDB.
Many other databases use protein structures deposited in the PDB. For example, SCOP and CATH classify
protein structures, while PDBsum provides a graphic overview of PDB entries using information from
other sources, such as Gene ontology.
www.wwpdb.org
ebi.ac.uk
www.rcsb.org
bmrb.io
pdbj.org
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I. Class: Types of folds, e.g., beta sheets.
II. Fold: The different shapes of domains within a class.
III. Superfamily: The domains in a fold are grouped into superfamilies, which have at least a distant common ancestor.
IV. Family: The domains in a superfamily are grouped into families, which have a more recent common ancestor.
V. Protein domain: The domains in families are grouped into protein domains, which are essentially the same protein.
VI. Species: The domains in "protein domains" are grouped according to species.
VII. Domain: part of a protein. For simple proteins, it can be the entire protein.
Structural Classification of Proteins (SCOP) database
Manual classification of protein structural domains based on similarities of their structures and amino acid
sequences.
A motivation for this classification is to determine the evolutionary relationship between proteins.
Proteins with the same shapes but having little sequence or functional similarity are placed in different
superfamilies, and are assumed to have only a very distant common ancestor.
Proteins having the same shape and some similarity of sequence and/or function are placed in "families",
and are assumed to have a closer common ancestor.
http://scop.mrc-lmb.cam.ac.uk/scop/
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1.All alpha proteins: Domains consisting of α-helices
2.All beta proteins: Domains consisting of β-sheets
3.Alpha and beta proteins: Mainly parallel beta sheets (beta-alpha-beta units)
4.Alpha and beta proteins (a+b): Mainly antiparallel beta sheets (segregated alpha and beta regions)
5.Multi-domain proteins (alpha and beta): Folds consisting of two or more domains belonging to different classes
6.Membrane and cell surface proteins and peptides: Does not include proteins in the immune system
7.Small proteins : Usually dominated by metal ligand, cofactor, and/or disulfide bridges
Classes
Folds
Each class contains a number of distinct folds. This classification level indicates similar tertiary structure,
but not necessarily evolutionary relatedness.
For example, the "All-α proteins" class contains >280 distinct folds, including:
Globin-like (core: 6 helices; folded leaf, partly opened),
long alpha-hairpin (2 helices; antiparallel hairpin, left-handed twist) and
Type I dockerin domains (tandem repeat of two calcium-binding loop-helix motifs)
Domains within a fold are further classified into superfamilies.
This is a largest grouping of proteins for which structural similarity is sufficient to indicate evolutionary relatedness
and therefore share a common ancestor.
For example, the two superfamilies of the "Globin-like" fold are: the Globin superfamily and alpha-helical
ferredoxin superfamily
Superfamily
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CATH database
cathdb.info
The CATH Protein Structure Classification database is a free, publicly available online resource that provides
information on the evolutionary relationships of protein domains.
The four main levels of the CATH hierarchy:
1. Class: The overall secondary-structure content of the domain. (Equivalent to the SCOP Class)
2. Architecture: High structural similarity but no evidence of homology.
3. Topology/fold: A large-scale grouping of topologies which share particular structural features (Equivalent
to the 'fold' level in SCOP)
4. Homologous superfamily: Indicative of a demonstrable evolutionary relationship. (Equivalent to SCOP
superfamily)
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Protein-Protein Interaction Databases
Protein-protein interaction databases are collections of information on the interactions between proteins.
Relationships between different proteins and their functions in biological systems.
BIND (https://bio.tools/bind )
BIND (Biomolecular Interaction Network Database) is a database that stores detailed descriptions of interactions,
molecular complexes, and pathways between various biomolecules, including proteins, nucleic acids, and small
molecules.
The database is designed to be used for data mining and can be used to study networks of interactions and map
pathways across different species. The database can also provide information for kinetic simulations.
DIP (https://dip.doe-mbi.ucla.edu/dip/Main.cgi )
DIP (Database of Interacting Proteins) is a database that contains protein-protein interaction information that has been
compiled through both manual curations and computational methods.
It is useful for understanding protein functions, and their relationships with other proteins. It can also be used to study
the properties of networks of interacting proteins, evaluate predictions of protein-protein interactions, and explore the
evolution of these interactions.
MINT (https://mint.bio.uniroma2.it/ )
MINT (Molecular Interaction) is a database that stores information on functional interactions between biological
molecules such as proteins, RNA, and DNA.
It also stores information on enzymatic modifications of partner molecules.
The database primarily focuses on experimentally verified protein-protein interactions and considers both direct and
indirect relationships.
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Protein Pattern and Profile Databases
Protein pattern and profile databases contain information on motifs found in sequences.
Sequence motifs correspond to structural or functional features in proteins.
So, the use of protein sequence patterns or profiles is a valuable tool in determining the function of proteins.
InterPro (https://www.ebi.ac.uk/interpro/ )
InterPro is a database that contains information on protein families, domains, and functional sites.
It was created by combining several major protein signature databases, including PROSITE, Pfam, PRINTS, ProDom, and
SMART into a single comprehensive resource.
PROSITE (https://prosite.expasy.org/ )
PROSITE is a collection of signatures that identify patterns or profiles in proteins, which can provide information on
their biological functions.
The signatures in the database are linked to annotation documents that provide information on the protein family or
domain detected, including its name, function, 3D structure, and references.
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Metabolic Pathway Databases
Metabolic pathway databases contain information about enzymes, biochemical reactions, and metabolic pathways.
ENZYME (https://enzyme.expasy.org/ )
ENZYME is a database that stores information on enzyme nomenclature.
It is used as the nomenclature source for enzyme names and reactions by most metabolic databases as well as by other
biomolecular databases.
KEGG (https://www.genome.jp/kegg/pathway.html )
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive database that maps out molecular and cellular
pathways involving interactions between genes and molecules.
It is composed of pathway maps, molecule tables, gene tables, and genome maps, and is used to build functional maps
of metabolic and regulatory pathways.