Abstract
Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/.
Highlights
There has been a rapid development of genome-wide techniques in the last decade along with significant lowering of the cost of gene sequencing, which generated widely available genomic data
Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering
The core of the PremPDI method is based on molecular mechanics force fields and fast side
Summary
There has been a rapid development of genome-wide techniques in the last decade along with significant lowering of the cost of gene sequencing, which generated widely available genomic data. The interpretation of genomic data and prediction of the association of genetic variations with diseases and phenotypes still require significant improvement [1]. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to many diseases, such as cancer and heart diseases [2,3,4]. One possible way to assess the effect of a mutation on protein-DNA interaction is to experimentally measure the binding affinity change. The development of reliable computational approaches to predict the effects of missense mutations on proteins and their complexes would give us important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of diseases and facilitating their treatment and prevention
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