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Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms.

ABSTRACT: Mutations in PITX2 have been implicated in several genetic disorders, particularly Axenfeld-Rieger syndrome. In order to determine the most reliable bioinformatics tools to assess the likely pathogenicity of PITX2 variants, the results of bioinformatics predictions were compared to the impact of variants on PITX2 structure and function. The MutPred, Provean, and PMUT bioinformatic tools were found to have the highest performance in predicting the pathogenicity effects of all 18 characterized missense variants in PITX2, all with sensitivity and specificity >93%. Applying these three programs to assess the likely pathogenicity of 13 previously uncharacterized PITX2 missense variants predicted 12/13 variants as deleterious, except A30V which was predicted as benign variant for all programs. Molecular modeling of the PITX2 homoedomain predicts that of the 31 known PITX2 variants, L54Q, F58L, V83F, V83L, W86C, W86S, and R91P alter PITX2's structure. In contrast, the remaining 24 variants are not predicted to change PITX2's structure. The results of molecular modeling, performed on all the PITX2 missense mutations located in the homeodomain, were compared with the findings of eight protein stability programs. CUPSAT was found to be the most reliable in predicting the effect of missense mutations on PITX2 stability. Our results showed that for PITX2, and likely other members of this homeodomain transcription factor family, MutPred, Provean, PMUT, molecular modeling, and CUPSAT can reliably be used to predict PITX2 missense variants pathogenicity.

SUBMITTER: Seifi M

PROVIDER: S-EPMC5903617 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms.

Seifi Morteza M Walter Michael A MA

PloS one 20180417 4

Mutations in PITX2 have been implicated in several genetic disorders, particularly Axenfeld-Rieger syndrome. In order to determine the most reliable bioinformatics tools to assess the likely pathogenicity of PITX2 variants, the results of bioinformatics predictions were compared to the impact of variants on PITX2 structure and function. The MutPred, Provean, and PMUT bioinformatic tools were found to have the highest performance in predicting the pathogenicity effects of all 18 characterized mis ...[more]

PMID: 29664915

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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

Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms.

Publications

Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms.

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