Somatic mutations may contribute significantly to tumorigenesis. The majority of these mutations are largely neutral (passenger mutations) in comparison to a few driver mutations that give cells the selective advantage leading to their proliferation. From the clinical perspective, these non-neutral mutations affecting human health represent the main interest. Such binary driver-passenger model can be adjusted by taking into account additive pleiotropic effect of mutations. Cancer mutations might have different functional consequences in various cancer types and patients, they can lead to activation or deactivation of proteins and dysregulation of a variety of cellular processes. For the vast majority of mutations, the mechanisms of their occurrence in DNA and phenotypic effects on proteins, pathways and cells are largely unknown. Signaling networks involve a dense network of protein interactions and are often deregulated in many cancers. The analysis of protein complexes, disease-related interaction networks and the effects of mutations on network properties would provide 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 protein function, potentially leading to disease. The availability of computational methods to evaluate the impact of mutations on protein-protein and protein-DNA 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 issue, we introduced 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. MutaBind uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. 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 developed a new resource, MutaGene, which combines different methods and tools for the analysis of cancer context-dependent mutations. MutaGene explores DNA context-dependent mutational patterns and underlying somatic cancer mutagenesis, analyzes mutational profiles of cancer samples, identifies the combinations of underlying mutagenic processes and prioritizes cancer point mutations with respect to their cancerogenicity. The combination of mutagenic processes can be identified in any patient 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. Mutation rate can vary throughout the genome due to chromatin packaging, nucleosome positioning and other factors. 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. 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 protein-DNA 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, HYDroxyl-Radical footprinting Interpretation for DNA (HYDROID). The stages of the HYDROID include extraction of the lane profiles from gel images, quantification of the DNA cleavage frequency at every nucleotide, and theoretical estimation of the DNA cleavage frequency from atomistic structural models followed by comparison of experimental and theoretical results. HYDROID precisely identifies DNA positioning on nucleosome by combining HRF data for both DNA strands with the pseudo-symmetry constraints derived from nucleosome structure. We apply our integrative method to characterize the atomistic structures of different nucleosome containing systems. In summary, we develop computational methods to analyze and interpret genetic and epigenetic changes observed in cancer patients by integrating data-driven and hypothesis-driven approaches.

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Zhao, Feiyang; Zheng, Lei; Goncearenco, Alexander et al. (2018) Computational Approaches to Prioritize Cancer Driver Missense Mutations. Int J Mol Sci 19:
NCBI Resource Coordinators (2018) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 46:D8-D13
Rogozin, Igor B; Goncearenco, Alexander; Lada, Artem G et al. (2018) DNA polymerase ? mutational signatures are found in a variety of different types of cancer. Cell Cycle 17:348-355
Xiao, Hua; Wang, Feng; Wisniewski, Jan et al. (2017) Molecular basis of CENP-C association with the CENP-A nucleosome at yeast centromeres. Genes Dev 31:1958-1972
Goncearenco, Alexander; Li, Minghui; Simonetti, Franco L et al. (2017) Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows. Methods Mol Biol 1647:221-236
El Kennani, Sara; Adrait, Annie; Shaytan, Alexey K et al. (2017) MS_HistoneDB, a manually curated resource for proteomic analysis of human and mouse histones. Epigenetics Chromatin 10:2
Li, Minghui; Goncearenco, Alexander; Panchenko, Anna R (2017) Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols. Methods Mol Biol 1550:235-260
Rogozin, Igor B; Pavlov, Youri I; Goncearenco, Alexander et al. (2017) Mutational signatures and mutable motifs in cancer genomes. Brief Bioinform :
Shaytan, Alexey K; Xiao, Hua; Armeev, Grigoriy A et al. (2017) Hydroxyl-radical footprinting combined with molecular modeling identifies unique features of DNA conformation and nucleosome positioning. Nucleic Acids Res 45:9229-9243
Goncearenco, Alexander; Rager, Stephanie L; Li, Minghui et al. (2017) Exploring background mutational processes to decipher cancer genetic heterogeneity. Nucleic Acids Res :

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