PremPS: Predicting the impact of missense mutations on protein stability

• Published in: PLoS computational biology Vol. 16; no. 12; p. e1008543
• Main Authors: Chen, Yuting, Lu, Haoyu, Zhang, Ning, Zhu, Zefeng, Wang, Shuqin, Li, Minghui
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.12.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino Acid Substitution; Bias; Biology and Life Sciences; Computational Biology - methods; Computer applications; Crystal structure; Databases, Protein; Datasets; Datasets as Topic; Energy; Impact prediction; Machine learning; Methods; Missense mutation; Mutation; Mutation (Biology); Mutation, Missense; Performance prediction; Physical Sciences; Physiological aspects; Protein Conformation; Protein Stability; Proteins; Proteins - chemistry; Research and Analysis Methods; Stability; Training; China

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutat...