Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides

Gautam, Ankur; Chaudhary, Kumardeep; Singh, Sandeep; Joshi, Anshika; Anand, Paras; Tuknait, Abhishek; Mathur, Deepika; Varshney, Grish C.; Raghava, Gajendra P. S.

doi:10.1093/nar/gkt1008

Cited by 120 publications

(80 citation statements)

References 35 publications

(34 reference statements)

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“…That said, future work will focus on ways to 359 balance sequence diversity with predicted activity. Finally, another key design criterion that may be ripe for machine 360 learning approaches is prediction of toxicity (Gautam et al, 2014). If effective, computational toxicity prediction 361 would be a critical step toward design of clinical useful AMPs.…”

Section: Peptide Design 307mentioning

confidence: 99%

Deep learning regression model for antimicrobial peptide design

Witten

2019

Preprint

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7Antimicrobial peptides (AMPs) are naturally occurring or synthetic peptides that show promise for treating 8 antibiotic-resistant pathogens. Machine learning techniques are increasingly used to identify naturally occurring 9AMPs, but there is a dearth of purely computational methods to design novel effective AMPs, which would speed 10 AMP development. We collected a large database, Giant Repository of AMP Activities (GRAMPA), containing 11 AMP sequences and associated MICs. We designed a convolutional neural network to perform combined 12 classification and regression on peptide sequences to quantitatively predict AMP activity against Escherichia coli. 13Our predictions outperformed the state of the art at AMP classification and were also effective at regression, for 14 which there were no publicly available comparisons. We then used our model to design novel AMPs and 15 experimentally demonstrated activity of these AMPs against the pathogens E. coli, Pseudomonas aeruginosa, and 16Staphylococcus aureus. Data, code, and neural network architecture and parameters are available at

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Section: Peptide Design 307mentioning

confidence: 99%

Deep learning regression model for antimicrobial peptide design

Witten

2019

Preprint

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“…Peptides containing various therapeutic properties have been discovered67891011121314151617181920 and their number is increasing with time21. Owing to their applicability, a number of bioinformatics platforms have been developed to assist peptide therapeutics22232425262728. According to a recent report, 128 peptides are in the clinical pipeline.…”

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confidence: 99%

PEPlife: A Repository of the Half-life of Peptides

Mathur

Prakash

Anand

et al. 2016

Sci Rep

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118

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Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

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“…We applied the tool to in‐house experimentally derived CD4 + restricted epitopes, however the tool is equally applicable to CD8 + T‐cell epitopes, as shown for the IEDB‐derived data set. In addition to the epitopes, the tool can also be applied to cluster and explore interesting patterns in any linear peptide data set (such as cell‐penetrating peptides, tumour homing peptides, haemolytic peptides, therapeutic peptides).…”

Section: Discussionmentioning

confidence: 99%

Development of a novel clustering tool for linear peptide sequences

et al. 2018

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SummaryEpitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the ‘clique method’, in which all members of the cluster must share the same minimal level of identity with each other, and the ‘connected graph method’, in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel ‘cluster‐breaking algorithm’ for consensus sequence driven sub‐clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the ‘clique method’, (ii) sets of allergen‐derived peptides from related species for the ‘connected graph method’ and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T‐cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed ‘cluster‐breaking algorithm’. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/.

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Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides

Cited by 120 publications

References 35 publications

Deep learning regression model for antimicrobial peptide design

Deep learning regression model for antimicrobial peptide design

PEPlife: A Repository of the Half-life of Peptides

Development of a novel clustering tool for linear peptide sequences

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