Literature DB >> 25975567

GAMPMS: Genetic algorithm managed peptide mutant screening.

Thomas Long1, Owen M McDougal2, Tim Andersen1.   

Abstract

The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α3β2-isoform with a library of 64,000 α-conotoxin (α-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α-CTx MII peptide mutants with the α3β2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  genetic algorithm; heuristic screen; high throughput virtual screening; molecular docking; peptide mutation

Mesh:

Substances:

Year:  2015        PMID: 25975567     DOI: 10.1002/jcc.23928

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  5 in total

1.  Genetic Algorithm Managed Peptide Mutant Screening: Optimizing Peptide Ligands for Targeted Receptor Binding.

Authors:  Matthew D King; Thomas Long; Timothy Andersen; Owen M McDougal
Journal:  J Chem Inf Model       Date:  2016-12-07       Impact factor: 4.956

2.  SPIDR: small-molecule peptide-influenced drug repurposing.

Authors:  Matthew D King; Thomas Long; Daniel L Pfalmer; Timothy L Andersen; Owen M McDougal
Journal:  BMC Bioinformatics       Date:  2018-04-16       Impact factor: 3.169

3.  Qualitative Assay to Detect Dopamine Release by Ligand Action on Nicotinic Acetylcholine Receptors.

Authors:  Leanna A Marquart; Matthew W Turner; Owen M McDougal
Journal:  Toxins (Basel)       Date:  2019-11-20       Impact factor: 4.546

4.  Ribbon α-Conotoxin KTM Exhibits Potent Inhibition of Nicotinic Acetylcholine Receptors.

Authors:  Leanna A Marquart; Matthew W Turner; Lisa R Warner; Matthew D King; James R Groome; Owen M McDougal
Journal:  Mar Drugs       Date:  2019-11-28       Impact factor: 5.118

5.  Water Thermodynamics of Peptide Toxin Binding Sites on Ion Channels.

Authors:  Binita Shah; Dan Sindhikara; Ken Borrelli; Abba E Leffler
Journal:  Toxins (Basel)       Date:  2020-10-12       Impact factor: 4.546

  5 in total

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