Evidence map›Paper›PMID 37874196›Full record

ArticleProteins2024

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

Erin R Claussen, P Douglas Renfrew, Christian L Müller, Kevin Drew

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In one paragraph

Article in Proteins, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Erin R ClaussenDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.
P Douglas RenfrewCenter for Computational Biology, Flatiron Institute, New York, New York, USA.
Christian L MüllerDepartment of Statistics, Ludwig-Maximilians-Universität München, Munich, Germany.
Kevin DrewDepartment of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0002-1260-4413

Funding

Discovery and characterization of novel ciliopathy protein complexesR00HD092613 · NICHD · UNIVERSITY OF ILLINOIS AT CHICAGO · PI DREW, KEVIN · 2021 to 2023
$732k
NICHD NIH HHS L40 HD096554NICHD NIH HHS R00 HD092613NIH HHS L40 HD096554NIH HHS R00 HD092613
6 · The paper itself

Abstract

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Indexed as

PeptidomimeticsAlgorithmsMolecular ConformationPeptidesPeptidesPeptidomimeticsCMA-EScovariance matrix adaptation evolution strategyderivative-free optimizationhotspot residuespeptidomimeticRosettascaffold matcher

Identifiers

PMID37874196
PMCPMC10873094

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.