DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

Abstract The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

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

[2]  M J Harvey,et al.  ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale. , 2009, Journal of chemical theory and computation.

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

[4]  Vladimir B. Bajic,et al.  DAMPD: a manually curated antimicrobial peptide database , 2011, Nucleic Acids Res..

[5]  M. Natália D. S. Cordeiro,et al.  First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens , 2016, J. Chem. Inf. Model..

[6]  Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks , 2020, ACS omega.

[7]  Tzong-Yi Lee,et al.  dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data , 2018, Nucleic Acids Res..

[8]  V. V. Kleandrova,et al.  Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity. , 2016, ACS combinatorial science.

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

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

[11]  M. Benincasa,et al.  Design, antimicrobial activity and mechanism of action of Arg-rich ultra-short cationic lipopeptides , 2019, PloS one.

[12]  Qingshan Huang,et al.  LAMP2: a major update of the database linking antimicrobial peptides , 2020, Database J. Biol. Databases Curation.

[13]  Boris Vishnepolsky,et al.  Comment on: 'Empirical comparison of web-based antimicrobial peptide prediction tools' , 2019, Bioinform..

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

[15]  B. L. de Groot,et al.  CHARMM36m: an improved force field for folded and intrinsically disordered proteins , 2016, Nature Methods.

[16]  Hiroyuki Kurata,et al.  Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties , 2019, FEBS letters.

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

[18]  Boris Vishnepolsky,et al.  Prediction of Linear Cationic Antimicrobial Peptides Based on Characteristics Responsible for Their Interaction with the Membranes , 2013, J. Chem. Inf. Model..

[19]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information , 2008, Nucleic Acids Res..

[20]  S. Blondelle,et al.  Lipid-induced conformation and lipid-binding properties of cytolytic and antimicrobial peptides: determination and biological specificity. , 1999, Biochimica et biophysica acta.

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

[22]  The UniProt Consortium,et al.  UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..

[23]  F Eisenmenger,et al.  Third type of secondary structure: noncooperative mobile conformation. Protein Data Bank analysis. , 1987, Biochemical and biophysical research communications.

[24]  Durdam Das,et al.  PlantPepDB: A manually curated plant peptide database , 2020, Scientific Reports.

[25]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[26]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[27]  S. Krimm,et al.  New chain conformations of poly(glutamic acid) and polylysine. , 1968, Biopolymers.

[28]  A. Gautam,et al.  THPdb: Database of FDA-approved peptide and protein therapeutics , 2017, PloS one.

[29]  M. V. van Raaij,et al.  Double-stranded helical twisted beta-sheet channels in crystals of gramicidin S grown in the presence of trifluoroacetic and hydrochloric acids. , 2007, Acta crystallographica. Section D, Biological crystallography.

[30]  Shigeki Mitaku,et al.  Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces , 2002, Bioinform..

[31]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[32]  Jesus A. Beltran,et al.  Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis , 2019, Bioinform..

[33]  O. Geiger,et al.  Bacterial membrane lipids: diversity in structures and pathways. , 2016, FEMS microbiology reviews.

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

[35]  Andreas Prlic,et al.  NGL viewer: web‐based molecular graphics for large complexes , 2018, Bioinform..

[36]  Virapong Prachayasittikul,et al.  HemoPred: a web server for predicting the hemolytic activity of peptides. , 2017, Future medicinal chemistry.

[37]  Gajendra P. S. Raghava,et al.  Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features , 2018, Front. Pharmacol..

[38]  Sadaf Gull,et al.  AMP0: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[39]  Andrei Gabrielian,et al.  Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria , 2018, J. Chem. Inf. Model..

[40]  Andrei Gabrielian,et al.  DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides , 2015, Nucleic acids research.

[41]  G. Del Rio,et al.  Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs , 2019, Molecules.

[42]  Peng Qiu,et al.  Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[43]  Peter L. Freddolino,et al.  Force field bias in protein folding simulations. , 2009, Biophysical journal.

[44]  David S. Goodsell,et al.  The RCSB Protein Data Bank: views of structural biology for basic and applied research and education , 2014, Nucleic Acids Res..

[45]  A. Gabrielian,et al.  De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria , 2019, Pharmaceuticals.

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

[47]  Catherine Sarazin,et al.  ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user’s research , 2019, Life Science Alliance.

[48]  Balachandran Manavalan,et al.  mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides , 2019, International journal of molecular sciences.