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Prediction and interpretation of deleterious coding variants in terms of protein structural stability. / Ancien, François; Pucci, Fabrizio; Godfroid, Maxime; Rooman, Marianne.

In: Scientific Reports, Vol. 8, No. 1, 4480, 14.03.2018.

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@article{d37f4da838ca455793d34a7009756ba6,
title = "Prediction and interpretation of deleterious coding variants in terms of protein structural stability",
abstract = "The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71{\%} in cross validation. As expected, it has a very high positive predictive value of 89{\%}: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75{\%}, and allowed predicting more than 1/4 of the variants with 95{\%} positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/ .",
keywords = "Algorithms, Computational Biology/methods, Databases, Protein, Genetic Variation, Humans, Neural Networks (Computer), Open Reading Frames, Point Mutation, Protein Stability, Proteins/chemistry, Structure-Activity Relationship",
author = "Fran{\cc}ois Ancien and Fabrizio Pucci and Maxime Godfroid and Marianne Rooman",
year = "2018",
month = "3",
day = "14",
doi = "10.1038/s41598-018-22531-2",
language = "English",
volume = "8",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Prediction and interpretation of deleterious coding variants in terms of protein structural stability

AU - Ancien, François

AU - Pucci, Fabrizio

AU - Godfroid, Maxime

AU - Rooman, Marianne

PY - 2018/3/14

Y1 - 2018/3/14

N2 - The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/ .

AB - The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/ .

KW - Algorithms

KW - Computational Biology/methods

KW - Databases, Protein

KW - Genetic Variation

KW - Humans

KW - Neural Networks (Computer)

KW - Open Reading Frames

KW - Point Mutation

KW - Protein Stability

KW - Proteins/chemistry

KW - Structure-Activity Relationship

UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852127/

U2 - 10.1038/s41598-018-22531-2

DO - 10.1038/s41598-018-22531-2

M3 - Article

C2 - 29540703

VL - 8

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 4480

ER -

ID: 48418098