I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure.
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TLDR
I-Mutant2.0 is introduced as a unique and valuable helper for protein design, even when the protein structure is not yet known with atomic resolution.Abstract:
I-Mutant2.0 is a support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. I-Mutant2.0 predictions are performed starting either from the protein structure or, more importantly, from the protein sequence. This latter task, to the best of our knowledge, is exploited for the first time. The method was trained and tested on a data set derived from ProTherm, which is presently the most comprehensive available database of thermodynamic experimental data of free energy changes of protein stability upon mutation under different conditions. I-Mutant2.0 can be used both as a classifier for predicting the sign of the protein stability change upon mutation and as a regression estimator for predicting the related ΔΔG values. Acting as a classifier, I-Mutant2.0 correctly predicts (with a cross-validation procedure) 80% or 77% of the data set, depending on the usage of structural or sequence information, respectively. When predicting ΔΔG values associated with mutations, the correlation of predicted with expected/experimental values is 0.71 (with a standard error of 1.30 kcal/mol) and 0.62 (with a standard error of 1.45 kcal/mol) when structural or sequence information are respectively adopted. Our web interface allows the selection of a predictive mode that depends on the availability of the protein structure and/or sequence. In this latter case, the web server requires only pasting of a protein sequence in a raw format. We therefore introduce I-Mutant2.0 as a unique and valuable helper for protein design, even when the protein structure is not yet known with atomic resolution. Availability: http://gpcr.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi.read more
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Journal ArticleDOI
Stability effects of mutations and protein evolvability
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TL;DR: Way of predicting and analyzing stability effects of mutations, and mechanisms that buffer or compensate for these destabilizing effects and thereby promote protein evolvabilty, in nature and in the laboratory are described.
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Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information
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mCSM: predicting the effects of mutations in proteins using graph-based signatures
TL;DR: It is shown that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario.
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Automated inference of molecular mechanisms of disease from amino acid substitutions
Biao Li,Vidhya G. Krishnan,Matthew Mort,Fuxiao Xin,Kishore K. Kamati,David Neil Cooper,Sean D. Mooney,Predrag Radivojac +7 more
TL;DR: A new computational model, MutPred, is developed that is based upon protein sequence, and which models changes of structural features and functional sites between wild-type and mutant sequences and can provide insight into the specific molecular mechanism responsible for the disease state.
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DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
TL;DR: DynaMut is presented, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes.
References
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