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SPROUTS: a database for the evaluation of protein stability upon point mutation.

ABSTRACT: SPROUTS (Structural Prediction for pRotein fOlding UTility System) is a new database that provides access to various structural data sets and integrated functionalities not yet available to the community. The originality of the SPROUTS database is the ability to gain access to a variety of structural analyses at one place and with a strong interaction between them. SPROUTS currently combines data pertaining to 429 structures that capture representative folds and results related to the prediction of critical residues expected to belong to the folding nucleus: the MIR (Most Interacting Residues), the description of the structures in terms of modular fragments: the TEF (Tightened End Fragments), and the calculation at each position of the free energy change gradient upon mutation by one of the 19 amino acids. All database results can be displayed and downloaded in textual files and Excel spreadsheets and visualized on the protein structure. SPROUTS is a unique resource to access as well as visualize state-of-the-art characteristics of protein folding and analyse the effect of point mutations on protein structure. It is available at http://bioinformatics.eas.asu.edu/sprouts.html.

SUBMITTER: Lonquety M

PROVIDER: S-EPMC2686433 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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SPROUTS: a database for the evaluation of protein stability upon point mutation.

Lonquety Mathieu M Lacroix Zoé Z Papandreou Nikolaos N Chomilier Jacques J

Nucleic acids research 20081022 Database issue

SPROUTS (Structural Prediction for pRotein fOlding UTility System) is a new database that provides access to various structural data sets and integrated functionalities not yet available to the community. The originality of the SPROUTS database is the ability to gain access to a variety of structural analyses at one place and with a strong interaction between them. SPROUTS currently combines data pertaining to 429 structures that capture representative folds and results related to the prediction ...[more]

PMID: 18945702

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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:BackgroundPredicting 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.ResultsHere 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.ConclusionsEvolutionary 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.

Dataset Information

SPROUTS: a database for the evaluation of protein stability upon point mutation.

Publications

SPROUTS: a database for the evaluation of protein stability upon point mutation.

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