Performance of protein stability predictors

By Sofia Khan, Mauno Vihinen. 2010

312 citations recorded by the source corpus.

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In Genetics

In Molecular Biology

Citations in the corpus

Fast and flexible design of novel proteins using graph neural networks (2019, unknown): “Limitations of those methods include the relatively low accuracy of existing force fields ( 6 , 7 ) and the inability to sample more than a miniscule portion of the vast search space (sequence space size is 20 N , N being the number of residues).”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “In another study, 11 methods were evaluated [Khan and Vihinen, 2010].”
Capturing the mutational landscape of the beta-lactamase TEM-1 (2013, background): “The limited power of ΔΔG prediction softwares (33) may explain why BLOSUM62 and accessibility data improve the models.”
PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, unknown): “Three recent studies independently assessed and compared the performances of several of those predictors, using datasets of experimentally characterized mutants that had not been used to train any of the predictive models [22,29,30].”
How Structural and Physicochemical Determinants Shape Sequence Constraints in a Functional Enzyme (2015, methodology): “A set of 28 descriptors was assembled that describes different properties of the amino acids with low correlation to each other (S1 Table), based on previous works that developed minimal complete sets of amino acid properties [36,37] and combinations of them.”
Performance of mutation pathogenicity prediction methods on missense variants (2011, result): “We classified residues with ASAs r 10% as buried and with ASAs Z 25% as exposed, similarly as in a previous study [Khan and Vihinen, 2010].”