Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Journal of chemical information and modeling - 27 Dec 2016

King Matthew D, Long Thomas, Andersen Timothy, McDougal Owen M

Abstract excerpt

This study demonstrates the utility of genetic algorithms to search exceptionally large and otherwise intractable mutant libraries for sequences with optimal binding affinities for target receptors. The Genetic Algorithm Managed Peptide Mutant Screening (GAMPMS) program was used to search an α-conotoxin (α-CTx) MII mutant library of approximately 41 billion possible peptide sequences for those exhibiting the...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Software
Share to Algorithms
Share to Drug Discovery

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Molecular

Share to Nicotinic Antagonists

Share to Peptide Library

Share to Receptors, Nicotinic

Source

Open in PubMed Open full text

Identifiers

PMID: 28024403
DOI: 10.1021/acs.jcim.6b00095
PMCID: PMC5367625

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Genetic Algorithm Managed Peptide Mutant Screening: Optimizing Peptide Ligands for Targeted Receptor Binding.PMID 28024403Read ↗

GAMPMS: Genetic algorithm managed peptide mutant screening.Semantic proximity Read ↗

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations.Semantic proximity Read ↗

Computational Design of α-Conotoxins to Target Specific Nicotinic Acetylcholine Receptor Subtypes.Semantic proximity Read ↗

High Throughput Random Mutagenesis and Single Molecule Real Time Sequencing of the Muscle Nicotinic Acetylcholine Receptor.Semantic proximity Read ↗

Informed training set design enables efficient machine learning-assisted directed protein evolution.Semantic proximity Read ↗

Complete characterization of the mutation landscape reveals the effect on amylin stability and amyloidogenicity.Semantic proximity Read ↗

Identification of a negative allosteric site on human α4β2 and α3β4 neuronal nicotinic acetylcholine receptors.Semantic proximity Read ↗

Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems.Semantic proximity Read ↗

Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors.Semantic proximity Read ↗

Basic Residues at Position 11 of α-Conotoxin LvIA Influence Subtype Selectivity between α3β2 and α3β4 Nicotinic Receptors via an Electrostatic Mechanism.Semantic proximity Read ↗

Interactions of Globular and Ribbon [γ4E]GID with α4β2 Neuronal Nicotinic Acetylcholine Receptor.Semantic proximity Read ↗

Structural and functional studies of the modulator NS9283 reveal agonist-like mechanism of action at α4β2 nicotinic acetylcholine receptors.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.