Elucidating Which Pairwise Mutations Affect Protein Stability: An Exhaustive Big Data Approach

Majeske, Nicholas; Jagodzinski, Filip

doi:10.1109/compsac.2018.00078

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…To quantitatively compare a protein mutant to its wild type, we previously developed the Rigidity Distance (RD) metric [26]. To assess the effects of deleting, in silico, an atom from the ligand, we compare the count and sizes of the different rigid clusters in the wild type of the protein-ligand complex and the protein-ligand mutant, via our RD WT→Variant metric:…”

Section: Rigidity Distancementioning

confidence: 99%

PETRA: Drug Engineering via Rigidity Analysis

et al. 2020

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Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of new medicines with high efficacies. Nonetheless, drug development remains a time and cost intensive process. In this work, we have developed a computational pipeline for assessing how individual atoms contribute to a ligand’s effect on the structural stability of a biological target. Our approach takes as input a protein-ligand resolved PDB structure file and systematically generates all possible ligand variants. We assess how the atomic-level edits to the ligand alter the drug’s effect via a graph theoretic rigidity analysis approach. We demonstrate, via four case studies of common drugs, the utility of our pipeline and corroborate our analyses with known biophysical properties of the medicines, as reported in the literature.

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Section: Rigidity Distancementioning

confidence: 99%

PETRA: Drug Engineering via Rigidity Analysis

et al. 2020

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“…The different stabilities of these intermediates may influence the rate of protein folding, depending on the mutated residue and the site of mutation. Pair mutations or triple mutations cause larger changes in the stability of a protein as compared to point mutations. , A large decrease in the folding rate of a protein due to pair mutations corresponds to a higher stabilization of the folding intermediates that results in a higher probability of misfolding. Correlated mutations facilitate the evolution of proteins over geological time scales, which permits a detailed investigation of the amino acid substitutions with respect to protein stability, foldability, and function.…”

Section: Introductionmentioning

confidence: 99%

Effect of Correlated Pair Mutations in Protein Misfolding

Kumar

Biswas

2019

J. Phys. Chem. B

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A Monte Carlo simulation based sequence design method is proposed to explore the effect of correlated pair mutations in proteins. In the designed sequences, the most correlated residue pairs are identified and mutated with all possible amino acid pairs except those already present. The cumulative correlated pair mutations generated an array of mutated sequences. Results show a significant increase in the probability of misfolding for correlated pair mutations as compared to that of the random pair mutations. The pair mutations of correlated residues that are in contact record a higher probability of misfolding as compared to the correlated residues that are not in contact. The probability of misfolding increases on pair mutation of nonlocally correlated residue pairs as compared to that of the locally correlated residue pairs. The choice of a compact or expanded conformation does not depend on the type of correlated pair mutations. Pair mutation of the most correlated residue pairs at the surface with hydrophobic amino acids results in higher misfolding probability as compared to that in the core. An exactly opposite behavior is observed on pair mutation with hydrophilic and charged amino acid pairs. The neutral amino acid pairs do not differentiate between core and surface sites. This study may be used for targeted mutation experiments to predict complex mutation patterns, reengineer the existing proteins, and design new proteins with reduced misfolding propensity.

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“…In order to quantitatively compare a mutated protein to its wild type, we utilize the rigidity distance, RD, metric, which we developed previously to assess how in silico amino acid substitution alter a protein's stability [17,20]. The RD metric evaluates the effect of mutating a residue to one of 19 other possible amino acids.…”

Section: Rigidity Distancementioning

confidence: 99%

Assessing Protein-Drug Resistance Due to Mutations via a Rigidity Analysis in silico Approach

Carpenter

Thackray

Kalthoff

et al.

EPiC Series in Computing

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A mutation to the amino acid sequence of a protein can cause a biomolecule to be resistant to the intended effects of a drug. Assessing the changes of a drug’s efficacy in response to mutations via mutagenesis wet-lab experiments is prohibitively time consuming for even a single point mutation, let alone for all possible mutations. Existing approaches for inferring mutation-induced drug resistance are available, but all of them reason about mutations of residues at or very near the protein-drug interface. However, there are examples of mutations far away from the region where the ligand binds, but which nonetheless render a protein resistant to the effects of the drug. We present a proof-of-concept computational pipeline that generates in silico the set of all possible single point mutations in a protein-ligand complex. We assess drug resistance using a graph theoretic rigidity analysis approach. Unlike existing methods, we are able to assess the impact of mutations far away from the protein-drug interface. We introduce several visualizations for exploring how amino acid substitutions both near and far away from where the ligand interacts with a protein target have a stabilizing or destabilizing effect on the protein-drug complex. We discuss our analytical approach in the context of experimental data from the literature about clinically known protein-drug interactions.

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Elucidating Which Pairwise Mutations Affect Protein Stability: An Exhaustive Big Data Approach

Cited by 6 publications

References 26 publications

PETRA: Drug Engineering via Rigidity Analysis

PETRA: Drug Engineering via Rigidity Analysis

Effect of Correlated Pair Mutations in Protein Misfolding

Assessing Protein-Drug Resistance Due to Mutations via a Rigidity Analysis in silico Approach

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