The antimicrobial peptide database is 20 years old: Recent developments and future directions
In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial pep...
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description | In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education. |
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The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.</description><identifier>ISSN: 0961-8368</identifier><identifier>ISSN: 1469-896X</identifier><identifier>EISSN: 1469-896X</identifier><identifier>DOI: 10.1002/pro.4778</identifier><identifier>PMID: 37695921</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Amino acid composition ; Amino Acids ; Anti-Bacterial Agents ; Anti-Infective Agents - chemistry ; Anti-Infective Agents - pharmacology ; Antibacterial activity ; Antimicrobial Cationic Peptides - chemistry ; Antimicrobial Cationic Peptides - pharmacology ; Antimicrobial Peptides ; Glycine ; Hydrophobicity ; Learning algorithms ; Lower bounds ; Lysine ; Machine learning ; Membrane proteins ; Optimization ; Peptides ; Proteins ; Salinity tolerance ; Salt tolerance ; Synthetic peptides ; Tools for Protein Science ; Toxicity ; Upper bounds</subject><ispartof>Protein science, 2023-10, Vol.32 (10), p.e4778</ispartof><rights>2023 The Protein Society.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3521-7be82abfdc3df59f2e973c9380d0d64228bb856e7d93ea0e54e22594b561973a3</citedby><cites>FETCH-LOGICAL-c3521-7be82abfdc3df59f2e973c9380d0d64228bb856e7d93ea0e54e22594b561973a3</cites><orcidid>0000-0002-4841-7927</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535814/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535814/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,725,778,782,883,27907,27908,53774,53776</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37695921$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Guangshun</creatorcontrib><title>The antimicrobial peptide database is 20 years old: Recent developments and future directions</title><title>Protein science</title><addtitle>Protein Sci</addtitle><description>In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.</description><subject>Amino acid composition</subject><subject>Amino Acids</subject><subject>Anti-Bacterial Agents</subject><subject>Anti-Infective Agents - chemistry</subject><subject>Anti-Infective Agents - pharmacology</subject><subject>Antibacterial activity</subject><subject>Antimicrobial Cationic Peptides - chemistry</subject><subject>Antimicrobial Cationic Peptides - pharmacology</subject><subject>Antimicrobial Peptides</subject><subject>Glycine</subject><subject>Hydrophobicity</subject><subject>Learning algorithms</subject><subject>Lower bounds</subject><subject>Lysine</subject><subject>Machine learning</subject><subject>Membrane proteins</subject><subject>Optimization</subject><subject>Peptides</subject><subject>Proteins</subject><subject>Salinity tolerance</subject><subject>Salt tolerance</subject><subject>Synthetic peptides</subject><subject>Tools for Protein Science</subject><subject>Toxicity</subject><subject>Upper bounds</subject><issn>0961-8368</issn><issn>1469-896X</issn><issn>1469-896X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkV9L3TAYh8OYzKMT9gkksJvd9Cz_m-xGhqgTBEEceCMhbd7OSNvUpBX89ubgmc5dJSFPHn5vfgh9oWRNCWHfpxTXoq71B7SiQplKG3XzEa2IUbTSXOldtJfzPSFEUMY_oV1eKyMNoyt0e30H2I1zGEKbYhNcjyeY5uABeze7xmXAIWNG8BO4lHHs_Q98BS2MM_bwCH2chrLPxeFxt8xLKg9DgnYOccyf0U7n-gwH23Uf_T49uT7-VV1cnp0f_7yoWi4ZreoGNHNN51vuO2k6BqbmreGaeOKVYEw3jZYKam84OAJSAGPSiEYqWkjH99HRi3damgH8Jl1yvZ1SGFx6stEF-_5mDHf2T3y0lEguNRXF8G1rSPFhgTzbIeQW-t6NEJdsmVaCylpyWtCv_6H3cUljmW9DGSWIlOZNWL415wTdaxpK7Ka0co52U1pBD_9N_wr-bYk_AzJTkw0</recordid><startdate>202310</startdate><enddate>202310</enddate><creator>Wang, Guangshun</creator><general>Wiley Subscription Services, Inc</general><general>John Wiley & Sons, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4841-7927</orcidid></search><sort><creationdate>202310</creationdate><title>The antimicrobial peptide database is 20 years old: Recent developments and future directions</title><author>Wang, Guangshun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3521-7be82abfdc3df59f2e973c9380d0d64228bb856e7d93ea0e54e22594b561973a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Amino acid composition</topic><topic>Amino Acids</topic><topic>Anti-Bacterial Agents</topic><topic>Anti-Infective Agents - chemistry</topic><topic>Anti-Infective Agents - pharmacology</topic><topic>Antibacterial activity</topic><topic>Antimicrobial Cationic Peptides - chemistry</topic><topic>Antimicrobial Cationic Peptides - pharmacology</topic><topic>Antimicrobial Peptides</topic><topic>Glycine</topic><topic>Hydrophobicity</topic><topic>Learning algorithms</topic><topic>Lower bounds</topic><topic>Lysine</topic><topic>Machine learning</topic><topic>Membrane proteins</topic><topic>Optimization</topic><topic>Peptides</topic><topic>Proteins</topic><topic>Salinity tolerance</topic><topic>Salt tolerance</topic><topic>Synthetic peptides</topic><topic>Tools for Protein Science</topic><topic>Toxicity</topic><topic>Upper bounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Guangshun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Protein science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Guangshun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The antimicrobial peptide database is 20 years old: Recent developments and future directions</atitle><jtitle>Protein science</jtitle><addtitle>Protein Sci</addtitle><date>2023-10</date><risdate>2023</risdate><volume>32</volume><issue>10</issue><spage>e4778</spage><pages>e4778-</pages><issn>0961-8368</issn><issn>1469-896X</issn><eissn>1469-896X</eissn><abstract>In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20-years-old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and nonhemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>37695921</pmid><doi>10.1002/pro.4778</doi><orcidid>https://orcid.org/0000-0002-4841-7927</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Amino acid composition Amino Acids Anti-Bacterial Agents Anti-Infective Agents - chemistry Anti-Infective Agents - pharmacology Antibacterial activity Antimicrobial Cationic Peptides - chemistry Antimicrobial Cationic Peptides - pharmacology Antimicrobial Peptides Glycine Hydrophobicity Learning algorithms Lower bounds Lysine Machine learning Membrane proteins Optimization Peptides Proteins Salinity tolerance Salt tolerance Synthetic peptides Tools for Protein Science Toxicity Upper bounds |
title | The antimicrobial peptide database is 20 years old: Recent developments and future directions |
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