Specific human phenotype is largely determined by stability, activity, and interactions between numerous biomolecules which work together to provide specific cellular functions. Although the majority of genetic variations are likely to be neutral, a substantial fraction of them might explain the origins of Mendelian and complex diseases. Somatic mutations may contribute significantly to tumorigenesis, and driver mutations may allow cancer cells to sustain proliferative signaling. However, finding functionally important mutations and predicting their molecular mechanisms largely remains an unsolved problem. Many diseases are caused by protein malfunctions whereas missense mutations can render proteins nonfunctional and may be responsible for many diseases. From the clinical perspective, these non-neutral mutations affecting human health represent the main interest. For some diseases and genes, particularly following the Mendelian inheritance patterns, the causal genotype-phenotype relationship has been already established, while for complex polygenic diseases involving multiple factors it is still unknown. Signaling networks involve a dense network of protein interactions and are often deregulated in many diseases including cancer. Therefore the analysis of protein complexes, disease-related interaction networks and the effects of mutations on network properties would give us important clues for understanding the molecular mechanisms of diseases and allow for the treatment and prevention. A missense mutation that alters protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein-protein binding is critical for a wide range of biomedical applications. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. The evolution of cancer is driven by somatic mutations and clonal selection of these mutations. A growing body of evidence supports mutation rate dependence on the local DNA sequence context for various types of mutations. We develop methods for the analysis of cancer context-dependent mutations, some of them have been implemented in computational online tool MutaGene. This tool explores DNA context-dependent mutational patterns and underlying somatic cancer mutagenesis, analyzes mutational profiles of cancer samples, identifies the combinations of underlying mutagenic processes. The combination of mutagenic processes can be identified in any query sample with subsequent comparison to mutational profiles derived from malignant and benign samples. In addition, mutagen or cancer-specific mutational background models are applied to calculate expected DNA and protein site mutability to decouple relative contributions of mutagenesis and selection in carcinogenesis, thus elucidating the site-specific driving events in cancer. Nucleosomes represent elementary building blocks of chromatin and unique systems to study protein-DNA binding and principles of its regulation. There are four types of core histones (H3, H4, H2A, H2B), two copies of each forming the nucleosome core particle. Long N-terminal histone tails protrude from the octamer and have many post-translational modification sites, which constitute the so-called histone code. Basic histone types are known to be encoded by a set of genes which give rise to a family of histone variants that can be incorporated into nucleosomes and may have functional and structural significance. It was shown that histone variants can be implicated in many important biological processes including transcription regulation, DNA repair, heterochromatin formation, chromosome segregation and mitosis. All these processes, in turn, can be altered in cancer. The details of DNA positioning on the nucleosome and specific DNA conformation can provide key regulatory signals about the accessibility of chromatin and DNA to chromatin remodeling factors. Hydroxyl-radical footprinting (HRF) of proteinDNA complexes is a chemical technique that probes nucleosome organization in solution with a high precision unattainable by other methods. We proposed an integrative modeling method for constructing high-resolution atomistic models of nucleosomes based on HRF experiments. Our method precisely identifies DNA positioning on nucleosome by combining HRF data for both DNA strands with the pseudo-symmetry constraints. We apply our integrative method to characterize the atomistic structures of different variant nucleosomes.
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