ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user’s research

ADAPTABLE is a webserver and database of antimicrobial peptides that uses sequence and property alignment to highlight their mode of action against the threat of resistance in medicine and agriculture. Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of “property alignment” to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

[1]  Y. Kapila,et al.  Nisin, an apoptogenic bacteriocin and food preservative, attenuates HNSCC tumorigenesis via CHAC1 , 2012, Cancer medicine.

[2]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[3]  Roger Beuerman,et al.  Defensins knowledgebase: a manually curated database and information source focused on the defensins family of antimicrobial peptides , 2006, Nucleic Acids Res..

[4]  Kuan Y. Chang,et al.  A Large-Scale Structural Classification of Antimicrobial Peptides , 2015, BioMed research international.

[5]  Xia Li,et al.  APD2: the updated antimicrobial peptide database and its application in peptide design , 2008, Nucleic Acids Res..

[6]  R. Hammami,et al.  MilkAMP: a comprehensive database of antimicrobial peptides of dairy origin , 2014 .

[7]  Arnold D. Robbins.  Gnu awk user's guide : effective awk programming edition 1.0 , 1996 .

[8]  Guangshun Wang.  Improved methods for classification, prediction, and design of antimicrobial peptides. , 2015, Methods in molecular biology.

[9]  Bono Lučić,et al.  Knowledge-based computational methods for identifying or designing novel, non-homologous antimicrobial peptides , 2011, European Biophysics Journal.

[10]  Gajendra P. S. Raghava,et al.  SATPdb: a database of structurally annotated therapeutic peptides , 2015, Nucleic Acids Res..

[11]  A. Bahar,et al.  Antimicrobial Peptides , 2013, Pharmaceuticals.

[12]  R. Hammami,et al.  A New Structure-based Classification of Gram-positive Bacteriocins , 2010, The protein journal.

[13]  M. Lefranc,et al.  DBAASP: database of antimicrobial activity and structure of peptides. , 2014, FEMS microbiology letters.

[14]  A. Kappo,et al.  Reactive Oxygen Species, Apoptosis, Antimicrobial Peptides and Human Inflammatory Diseases , 2015, Pharmaceuticals.

[15]  Gajendra P. S. Raghava,et al.  CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides , 2015, Nucleic Acids Res..

[16]  Lee Whitmore,et al.  The Peptaibol Database: a database for sequences and structures of naturally occurring peptaibols , 2004, Nucleic Acids Res..

[17]  Qosay A. E. Al-Balas,et al.  Peptide consensus sequence determination for the enhancement of the antimicrobial activity and selectivity of antimicrobial peptides , 2016, Infection and drug resistance.

[18]  Yang Zhang,et al.  The I-TASSER Suite: protein structure and function prediction , 2014, Nature Methods.

[19]  C. Portugal,et al.  Plant antimicrobial peptides , 2015 .

[20]  Zhe Wang,et al.  APD: the Antimicrobial Peptide Database , 2004, Nucleic Acids Res..

[21]  P. Y. Chou,et al.  Prediction of the secondary structure of proteins from their amino acid sequence. , 2006 .

[22]  G. Crooks,et al.  WebLogo: a sequence logo generator. , 2004, Genome research.

[23]  Khaled Sebei,et al.  ANTISTAPHYBASE: database of antimicrobial peptides (AMPs) and essential oils (EOs) against methicillin-resistant Staphylococcus aureus (MRSA) and Staphylococcus aureus , 2016, Archives of Microbiology.

[24]  G. Vogel.  Meet WHO’s dirty dozen: The 12 bacteria for which new drugs are most urgently needed , 2017 .

[25]  R. Lai,et al.  The chemistry and biological activities of peptides from amphibian skin secretions. , 2015, Chemical reviews.

[26]  Cheng Shi,et al.  DRAMP 2.0, an updated data repository of antimicrobial peptides , 2019, Scientific Data.

[27]  Hanmei Xu,et al.  DRAMP: a comprehensive data repository of antimicrobial peptides , 2016, Scientific Reports.

[28]  P. Y. Chou,et al.  Prediction of protein conformation. , 1974, Biochemistry.

[29]  R. Epand.  Host Defense Peptides and Their Potential as Therapeutic Agents , 2016, Springer International Publishing.

[30]  A. Tassanakajon,et al.  Sequence diversity and evolution of antimicrobial peptides in invertebrates. , 2015, Developmental and comparative immunology.

[31]  X Zhang,et al.  A low-molecular-weight inhibitor against the chemokine receptor CXCR4: a strong anti-HIV peptide T140. , 1998, Biochemical and biophysical research communications.

[32]  Gajendra P. S. Raghava,et al.  CPPsite: a curated database of cell penetrating peptides , 2012, Database J. Biol. Databases Curation.

[33]  Gajendra P. S. Raghava,et al.  Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides , 2013, Nucleic Acids Res..

[34]  Evan Bolton,et al.  PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..

[35]  Simon Fong,et al.  AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest , 2018, Scientific Reports.

[36]  Xiaowei Zhao,et al.  LAMP: A Database Linking Antimicrobial Peptides , 2013, PloS one.

[37]  Tin Wee Tan,et al.  ANTIMIC: a database of antimicrobial sequences , 2004, Nucleic Acids Res..

[38]  Y. Shai,et al.  Mode of action of linear amphipathic α-helical antimicrobial peptides , 1998 .

[39]  Yibing Huang,et al.  Alpha-helical cationic antimicrobial peptides: relationships of structure and function , 2010, Protein & Cell.

[40]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[41]  P. Y. Chou,et al.  Empirical predictions of protein conformation. , 1978, Annual review of biochemistry.

[42]  S. Piotto,et al.  YADAMP: yet another database of antimicrobial peptides. , 2012, International journal of antimicrobial agents.

[43]  Manoj Kumar,et al.  AVPpred: collection and prediction of highly effective antiviral peptides , 2012, Nucleic Acids Res..

[44]  David J. Craik,et al.  ConoServer: updated content, knowledge, and discovery tools in the conopeptide database , 2011, Nucleic Acids Res..

[45]  J. Richardson,et al.  Amino acid preferences for specific locations at the ends of alpha helices. , 1988, Science.

[46]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[47]  Gajendra P. S. Raghava,et al.  CancerPPD: a database of anticancer peptides and proteins , 2014, Nucleic Acids Res..

[48]  David WheelerVersion,et al.  Pairwise Sequence Alignment , 2007 .

[49]  Davor Juretic,et al.  DADP: the database of anuran defense peptides , 2012, Bioinform..

[50]  Sergio Orduz,et al.  InverPep: A database of invertebrate antimicrobial peptides. , 2017, Journal of global antimicrobial resistance.

[51]  Jun Liu,et al.  Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences , 2015, Bioinform..

[52]  P. Pelegrini,et al.  Antifungal defensins and their role in plant defense , 2014, Front. Microbiol..

[53]  N. Segata,et al.  The Microbe Directory: An annotated, searchable inventory of microbes’ characteristics , 2018, Gates open research.

[54]  B. Bechinger,et al.  The Interactions of Histidine-containing Amphipathic Helical Peptide Antibiotics with Lipid Bilayers , 1999, The Journal of Biological Chemistry.

[55]  John M. Walker,et al.  The Proteomics Protocols Handbook , 2005, Humana Press.

[56]  Ole Tange,et al.  GNU Parallel: The Command-Line Power Tool , 2011, login Usenix Mag..

[57]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[58]  Faiza Hanif Waghu,et al.  CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides , 2015, Nucleic Acids Res..

[59]  M. Zasloff.  Antimicrobial peptides of multicellular organisms , 2002, Nature.

[60]  T. D. Schneider,et al.  Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.

[61]  Riadh Hammami,et al.  PhytAMP: a database dedicated to antimicrobial plant peptides , 2008, Nucleic Acids Res..

[62]  Riadh Hammami,et al.  BACTIBASE: a new web-accessible database for bacteriocin characterization , 2007, BMC Microbiology.

[63]  Ole Tange,et al.  GNU Parallel 20150322 ('Hellwig') , 2015 .

[64]  R D Appel,et al.  Protein identification and analysis tools in the ExPASy server. , 1999, Methods in molecular biology.

[65]  C. Aisenbrey,et al.  The Mechanisms of Action of Cationic Antimicrobial Peptides Refined by Novel Concepts from Biophysical Investigations. , 2019, Advances in experimental medicine and biology.

[66]  Bakhtiar Affendi Rosdi,et al.  Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity , 2015, BioMed research international.

[67]  D. Hoskin,et al.  Studies on anticancer activities of antimicrobial peptides. , 2008, Biochimica et biophysica acta.

[68]  M. Kumar,et al.  HIPdb: A Database of Experimentally Validated HIV Inhibiting Peptides , 2013, PloS one.

[69]  X. Su,et al.  Anticancer potential of bioactive peptides from animal sources (Review). , 2017, Oncology reports.

[70]  G. Maccari,et al.  BaAMPs: the database of biofilm-active antimicrobial peptides , 2015, Biofouling.

[71]  Artem Cherkasov,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm068 Databases and ontologies AMPer: a database and an automated discovery tool for antimicrobial peptides , 2022 .

[72]  Guangshun Wang,et al.  Antimicrobial Peptides in 2014 , 2015, Pharmaceuticals.

[73]  B. Bechinger.  Towards membrane protein design: pH-sensitive topology of histidine-containing polypeptides. , 1996, Journal of molecular biology.

[74]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[75]  Gajendra P. S. Raghava,et al.  ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures , 2014, Database J. Biol. Databases Curation.

[76]  M. O. Dayhoff,et al.  Establishing homologies in protein sequences. , 1983, Methods in enzymology.

[77]  P. Schmitt,et al.  An intimate link between antimicrobial peptide sequence diversity and binding to essential components of bacterial membranes. , 2016, Biochimica et biophysica acta.

[78]  Yang Zhang,et al.  A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction , 2013, Scientific Reports.

[79]  Manoj Kumar,et al.  AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses , 2013, Nucleic Acids Res..

[80]  Yang Zhang,et al.  I-TASSER server: new development for protein structure and function predictions , 2015, Nucleic Acids Res..

[81]  Eugene W. Myers,et al.  An Overview of Sequence Comparison Algorithms in Molecular Biology , 2004 .

[82]  J. Bloom,et al.  Mutational effects on stability are largely conserved during protein evolution , 2013, Proceedings of the National Academy of Sciences.

[83]  Rahul Kumar,et al.  In Silico Approach for Predicting Toxicity of Peptides and Proteins , 2013, PloS one.

[84]  C. Hill,et al.  Bioengineering Lantibiotics for Therapeutic Success , 2015, Front. Microbiol..

[85]  K. Chou,et al.  Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods , 2011, PloS one.

[86]  Xia Li,et al.  APD3: the antimicrobial peptide database as a tool for research and education , 2015, Nucleic Acids Res..

[87]  Vanesa C Albiol Matanic,et al.  Antiviral activity of antimicrobial cationic peptides against Junin virus and herpes simplex virus. , 2004, International journal of antimicrobial agents.

[88]  Conan K. L. Wang,et al.  ConoServer, a database for conopeptide sequences and structures , 2008, Bioinform..

[89]  R. Hammami,et al.  BACTIBASE second release: a database and tool platform for bacteriocin characterization , 2010, BMC Microbiology.

[90]  Guido van Rossum,et al.  The Python language reference manual: for Python version 3.2 , 2011 .