The next-generation sequencing technologies have now brought the dream of individual genome identification close to reality. However, new advances in genome sequencing are necessary but not sufficient for identifying functionally important variants and understanding the origins of many diseases. 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 the 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. Moreover, genetic variants with low penetrance, weakly associated with disease phenotypes, can only be annotated for large samples and for many diseases their genetic determinants have to be discovered. 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 disease mutations on network properties would give us important clues for understanding the molecular mechanisms of diseases and allow their treatment and prevention. In fact, many disease mutations are located on protein binding interfaces and may affect the specificity of recognition and protein binding affinity. 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 monomeric Casitas B-lineage lymphoma RING E3 ligases are emerging therapeutic targets in cancer treatment. RING domain of Cbl has E3 ligase activity and ubiquitinylates activated receptor tyrosine kinases. CBLs can bind to the ubiquitin-conjugating enzyme (E2) in a complex with ubiquitin and substrate protein and facilitate the transfer of Ub from E2 to a lysine residue of the substrate. Cbl represents a convenient system to investigate the mechanistic aspects of cancer mutations since several Cbl structures are available representing the snapshots of different stages of Cbl activation cycle. We applied our previously developed computational method to predict the effects of Cbl mutations on different Cbl states. Our predictions were tested by performing blind experiments on Cbl-mediated EGFR ubiquitination assays and showed a remarkable agreement between experimental densitometry data and changes in Cbl stability. We showed both experimentally and computationally that about one third of tested cancer mutations were probably passengers and did not impact the ubiquitination activity while others, potentially driver mutations, affected different stages of Cbl activation cycle either completely abolishing its ligase activity or partially attenuating it. The evolution of cancer is driven by somatic mutations and clonal selection of these mutations. It is therefore important to decouple mutagenesis from selection in order to characterize driving events in tumor evolution. Mutational process can be affected by local DNA sequence context around the mutated site. We developed a new computational method, MutaGene, that allows to estimate and analyze context-dependent mutational signatures to calculate the expected background mutabilities of nucleotide and amino acids in DNA and protein positions, connecting processes operating at DNA level to protein phenotype. Mutability can be used for identification of cancer driver mutations thereby linking cancer genotype with phenotype and decoupling relative contributions of mutagenesis and selection in carcinogenesis. Nucleosomes represent elementary building blocks of chromatin and unique systems to study protein-protein, protein-DNA binding and principles of their 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 all 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. We developed a new version of HistoneDB database, 'HistoneDB 2.0--with variants', that is a comprehensive database of histone protein sequences, classified by histone types and variants. All entries in the database are supplemented by rich sequence and structural annotations with many interactive tools to explore and compare sequences of different variants from various organisms. The determination of nucleosome core particle structure by X-ray crystallography has been a giant leap forward in our understanding of chromatin structure, however, it is now becoming more evident that the obtained crystal structure does not represent the only possible functionally important conformation. Molecular dynamics simulations of nucleosomes allow to go beyond crystallography in our understanding of nucleosomes by providing a framework to analyze the interactions through a dynamic conformational ensemble, by understanding the behavior of histone tails and by studying the variations in histone sequences and their dynamical behavior. Our goal is to understand the influence of histone sequence variation on the structure and dynamics of nucleosomes and identify key determinants, which affect nucleosome function. In our approach we integrate molecular modeling, bioinformatics sequence analysis together with experimental data in order to bridge the gap between these methods. As a result we identified and characterized the rearrangements in nucleosomes on a microsecond timescale including the coupling between the conformation of the histone tails and the DNA geometry. We found that certain histone tail conformations promoted DNA bulging near its entry/exit sites, resulting in the formation of twist defects within the DN
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