EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models

Folkman, Lukas; Stantić, Bela; Sattar, Abdul; Zhou, Yaoqi

doi:10.1016/j.jmb.2016.01.012

Cited by 115 publications

(106 citation statements)

References 48 publications

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“…sequence-based prediction of mutation-induced stability changes [24], was also used to affirm the stability of the protein upon mutations. Results are displayed in the form of an increase or decrease in stability upon mutation induction.…”

Section: Ease-mmmentioning

confidence: 99%

A comprehensive computational mutation structurefunction approach for determining potential drug target sites in poliovirus 2A protease

Younus¹,

Bhatti²

2018

Trop. J. Pharm Res

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Section: Ease-mmmentioning

confidence: 99%

A comprehensive computational mutation structurefunction approach for determining potential drug target sites in poliovirus 2A protease

Younus¹,

Bhatti²

2018

Trop. J. Pharm Res

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“…For this challenge, the 1EKG structure from the Protein Data Bank was considered as wild type. The Zhou Lab at the Griffith University, labeled as Group 3, submitted three sets of predictions (G3‐1, G3‐2, and G3‐3) using Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE‐MM) algorithm (Folkman, Stantic, Sattar, & Zhou, ). For the assessment we considered only one set of predictions (G3‐1) because the three batches of predictions returned the same

Δ Δ G^{{normalH}_{2} O}

values.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge

et al. 2019

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Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far‐UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild‐type proteins at zero concentration of denaturant (ΔΔGnormalH2O). The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the ΔΔitalicGnormalH2O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

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“…Computational methods are broadly based on the features of protein structure (e.g., FoldX; Schymkowitz et al, or PoPMuSIC; Dehouck, Kwasigroch, Gilis, & Rooman, ) or sequence (e.g., INPS; Fariselli, Martelli, Savojardo, & Casadio, or EASE‐MM; Folkman, Stantic, Sattar, & Zhou, ). Some methods, for example, I‐Mutant (Capriotti, Fariselli, & Casadio, ) and INPS‐MD (Savojardo, Fariselli, Martelli, & Casadio, ), among others, can run in both sequence and structure modes.…”

Section: Introductionmentioning

confidence: 99%

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

et al. 2019

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Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation‐induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation, we explored the utility of high‐throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state‐of‐the‐art predictors against over 7,000 nonsynonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerging as top performers depending on the metric, it is nontrivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability‐affecting. Furthermore, it is unclear as to what extent VSP abundance scores were reasonable proxies for the stability‐related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

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EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models

Cited by 115 publications

References 48 publications

A comprehensive computational mutation structurefunction approach for determining potential drug target sites in poliovirus 2A protease

A comprehensive computational mutation structurefunction approach for determining potential drug target sites in poliovirus 2A protease

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

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