Dataset Information

DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations.

ABSTRACT: BACKGROUND:Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (??G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ??G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ??G(A???B)?=?-??G(B???A), where A and B are amino acids. RESULTS:Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ??G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ??G values of ~?0.5 and ~?0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ??G of a reciprocal variation as almost equal (depending on the sequence profile) to -??G of the direct variation. This is a valuable property that is missing in the majority of the methods. CONCLUSIONS:Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ??G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods.

SUBMITTER: Montanucci L

PROVIDER: S-EPMC6606456 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations.

Montanucci Ludovica L Capriotti Emidio E Frank Yotam Y Ben-Tal Nir N Fariselli Piero P

BMC bioinformatics 20190703 Suppl 14

<h4>Background</h4>Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees th ...[more]

PMID: 31266447

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Project description:MotivationMutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling.ResultsWe developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations.Availability and implementationhttp://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.eduSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:BACKGROUND: A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DeltaDeltaG) upon single point mutation may also help the annotation process. The experimental DeltaDeltaG values are affected by uncertainty as measured by standard deviations. Most of the DeltaDeltaG values are nearly zero (about 32% of the DeltaDeltaG data set ranges from -0.5 to 0.5 kcal/mole) and both the value and sign of DeltaDeltaG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DeltaDeltaG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DeltaDeltaG<-1.0 kcal/mol), stabilizing mutations (DeltaDeltaG>1.0 kcal/mole) and neutral mutations (-1.0</=DeltaDeltaG</=1.0 kcal/mole). RESULTS: In this paper a support vector machine starting from the protein sequence or structure discriminates between stabilizing, destabilizing and neutral mutations. We rank all the possible substitutions according to a three state classification system and show that the overall accuracy of our predictor is as high as 56% when performed starting from sequence information and 61% when the protein structure is available, with a mean value correlation coefficient of 0.27 and 0.35, respectively. These values are about 20 points per cent higher than those of a random predictor. CONCLUSIONS: Our method improves the quality of the prediction of the free energy change due to single point protein mutations by adopting a hypothesis of thermodynamic reversibility of the existing experimental data. By this we both recast the thermodynamic symmetry of the problem and balance the distribution of the available experimental measurements of free energy changes. This eliminates possible overestimations of the previously described methods trained on an unbalanced data set comprising a number of destabilizing mutations higher than stabilizing ones.

Dataset Information

DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations.

Publications

DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations.

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