Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

Interpretable deep residual network uncovers nucleosome positioning and associated features.

Nucleosomes represent elementary building units of eukaryotic chromosomes and consist of DNA wrapped around a histone octamer flanked by linker DNA segments. Nucleosomes are central in epigenetic pathways and their genomic positioning is associated with regulation of gene expression, DNA replication, DNA methylation and DNA repair, among other functions. Building on prior discoveries that DNA sequences noticeably affect nucleosome positioning, our objective is to identify nucleosome positions and related features across entire genome. Here, we introduce an interpretable framework based on the concepts of deep residual networks (NuPoSe). Trained on high-coverage human experimental MNase-seq data, NuPoSe is able to learn sequence and structural patterns associated with nucleosome organization in human genome. NuPoSe can be also applied to unseen data from different organisms and cell types. Our findings point to 43 informative features, most of them constitute tri-nucleotides, di-nucleotides and one tetra-nucleotide. Most features are significantly associated with the nucleosomal structural characteristics, namely, periodicity of nucleosomal DNA and its location with respect to a histone octamer. Importantly, we show that features derived from the 27 bp linker DNA flanking nucleosomes contribute up to 10% to the quality of the prediction model. This, along with the comprehensive training sets, deep-learning architecture, and feature selection method, may contribute to the NuPoSe's 80-89% classification accuracy on different independent datasets.

SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.

MOTIVATION: Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins. RESULTS: A novel sequence-based method (SAAMBE-MEM) for predicting binding free energy changes (ΔΔG) in membrane protein-protein complexes due to mutations has been developed. This method utilized the MPAD database, which contains binding affinities for wild-type and mutant membrane protein complexes. A machine learning model was developed to predict ΔΔG by leveraging features such as amino acid indices and position-specific scoring matrices (PSSM). Through extensive dataset curation and feature extraction, SAAMBE-MEM was trained and validated using the XGBoost regression algorithm. The optimal feature set, including PSSM-related features, achieved a Pearson correlation coefficient of 0.64, outperforming existing methods trained on the SKEMPI database. Furthermore, it was demonstrated that SAAMBE-MEM performs much better when utilizing evolution-based features in contrast to physicochemical features. AVAILABILITY AND IMPLEMENTATION: The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. The cleaned MPAD database is available at the website.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule.

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

DNA methylation cues in nucleosome geometry, stability and unwrapping.

Cytosine methylation at the 5-carbon position is an essential DNA epigenetic mark in many eukaryotic organisms. Although countless structural and functional studies of cytosine methylation have been reported, our understanding of how it influences the nucleosome assembly, structure, and dynamics remains obscure. Here, we investigate the effects of cytosine methylation at CpG sites on nucleosome dynamics and stability. By applying long molecular dynamics simulations on several microsecond time scale, we generate extensive atomistic conformational ensembles of full nucleosomes. Our results reveal that methylation induces pronounced changes in geometry for both linker and nucleosomal DNA, leading to a more curved, under-twisted DNA, narrowing the adjacent minor grooves, and shifting the population equilibrium of sugar-phosphate backbone geometry. These DNA conformational changes are associated with a considerable enhancement of interactions between methylated DNA and the histone octamer, doubling the number of contacts at some key arginines. H2A and H3 tails play important roles in these interactions, especially for DNA methylated nucleosomes. This, in turn, prevents a spontaneous DNA unwrapping of 3-4 helical turns for the methylated nucleosome with truncated histone tails, otherwise observed in the unmethylated system on several microseconds time scale.

SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions.

MOTIVATION: Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet. RESULTS: Here, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein-DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations. AVAILABILITYAND IMPLEMENTATION: It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel yeast strain Geotrichum sp. CS-67 capable of accumulating heavy metal ions.

Bioremediation, which has several advantages over traditional methods, represents an alternative means of dealing with heavy metal pollution. We screened for microorganisms showing heavy metal tolerance in polluted mangrove soils. A novel yeast, Geotrichum sp. CS-67, was discovered and tested for tolerance of Cu2+, Zn2+, and Ni2+. Zn2+ was the most efficiently sequestered by Geotrichum sp. CS-67 followed by Ni2+ and Cu2+. Zn2+ and Ni2+ were actively taken up into the cell, while Cu2+ was adsorbed to the cell wall. We used RNA-Seq to show that a large number of genes involved in the physiological and biochemical processing of heavy metals were differentially expressed in this yeast when it was subjected to Zn2+ and Ni2+ stress. From this panel, we selected the SED1, GDI1 and ZRT1 genes for validation by qRT-PCR and discovered that, during Zn2+ and Ni2+ stress, SED1 and GDI1 were upregulated, while ZRT1 was downregulated, which was consistent with the RNA-Seq results and the biochemical function of these genes. In conclusion, the novel yeast Geotrichum sp. CS-67 has a marked ability to accumulate heavy metal ions, making it of great interest as a possible microbial agent for heavy metal pollution remediation in the future.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

DelPhi Suite: New Developments and Review of Functionalities.

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object-oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large-scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Predicting protein-DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver.

Motivation: Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments. Results: Here we report a new method Single Amino acid Mutation binding free energy change of Protein-DNA Interaction (SAMPDI). The method utilizes modified Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge-based terms delivered from investigations of the physicochemical properties of protein-DNA complexes. The method is benchmarked against experimentally determined binding free energy changes caused by 105 mutations in 13 proteins (compiled ProNIT database and data from recent references), and results in correlation coefficient of 0.72. Availability and implementation: http://compbio.clemson.edu/SAMPDI. Contact: ealexov@clemson.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Three additional patients with EED-associated overgrowth: potential mutation hotspots identified?

Variants have been identified in the embryonic ectoderm development (EED) gene in seven patients with syndromic overgrowth similar to that observed in Weaver syndrome. Here, we present three additional patients with missense variants in the EED gene. All the missense variants reported to date (including the three presented here) have localized to one of seven WD40 domains of the EED protein, which are necessary for interaction with enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). In addition, among the seven patients reported in the literature and the three new patients presented here, all of the reported pathogenic variants except one occurred at one of four amino acid residues in the EED protein. The recurrence of pathogenic variation at these loci suggests that these residues are functionally important (mutation hotspots). In silico modeling and calculations of the free energy changes resulting from these variants suggested that they not only destabilize the EED protein structure but also adversely affect interactions between EED, EZH2, and/or H3K27me3. These cases help demonstrate the mechanism(s) by which apparently deleterious variants in the EED gene might cause overgrowth and lend further support that amino acid residues in the WD40 domain region may be mutation hotspots.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases.

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

Novel Genetic Markers for Early Detection of Elevated Breast Cancer Risk in Women.

This study suggests that two newly discovered variants in the MSH2 gene, which codes for a DNA mismatch repair (MMR) protein, can be associated with a high risk of breast cancer. While variants in the MSH2 gene are known to be linked with an elevated cancer risk, the MSH2 gene is not a part of the standard kit for testing patients for elevated breast cancer risk. Here we used the results of genetic testing of women diagnosed with breast cancer, but who did not have variants in BRCA1 and BRCA2 genes. Instead, the test identified four variants with unknown significance (VUS) in the MSH2 gene. Here, we carried in silico analysis to develop a classifier that can distinguish pathogenic from benign mutations in MSH2 genes taken from ClinVar. The classifier was then used to classify VUS in MSH2 genes, and two of them, p.Ala272Val and p.Met592Val, were predicted to be pathogenic mutations. These two mutations were found in women with breast cancer who did not have mutations in BRCA1 and BRCA2 genes, and thus they are suggested to be considered as new bio-markers for the early detection of elevated breast cancer risk. However, before this is done, an in vitro validation of mutation pathogenicity is needed and, moreover, the presence of these mutations should be demonstrated in a higher number of patients or in families with breast cancer history.

Identification and characterization of a missense mutation in the O-linked ß-N-acetylglucosamine (O-GlcNAc) transferase gene that segregates with X-linked intellectual disability.

O-GlcNAc is a regulatory post-translational modification of nucleocytoplasmic proteins that has been implicated in multiple biological processes, including transcription. In humans, single genes encode enzymes for its attachment (O-GlcNAc transferase (OGT)) and removal (O-GlcNAcase (OGA)). An X-chromosome exome screen identified a missense mutation, which encodes an amino acid in the tetratricopeptide repeat, in OGT (759G>T (p.L254F)) that segregates with X-linked intellectual disability (XLID) in an affected family. A decrease in steady-state OGT protein levels was observed in isolated lymphoblastoid cell lines from affected individuals, consistent with molecular modeling experiments. Recombinant expression of L254F-OGT demonstrated that the enzyme is active as both a glycosyltransferase and an HCF-1 protease. Despite the reduction in OGT levels seen in the L254F-OGT individual cells, we observed that steady-state global O-GlcNAc levels remained grossly unaltered. Surprisingly, lymphoblastoids from affected individuals displayed a marked decrease in steady-state OGA protein and mRNA levels. We observed an enrichment of the OGT-containing transcriptional repressor complex mSin3A-HDAC1 at the proximal promoter region of OGA and correspondingly decreased OGA promoter activity in affected cells. Global transcriptome analysis of L254F-OGT lymphoblastoids compared with controls revealed a small subset of genes that are differentially expressed. Thus, we have begun to unravel the molecular consequences of the 759G>T (p.L254F) mutation in OGT that uncovered a compensation mechanism, albeit imperfect, given the phenotype of affected individuals, to maintain steady-state O-GlcNAc levels. Thus, a single amino acid substitution in the regulatory domain (the tetratricopeptide repeat domain) of OGT, which catalyzes the O-GlcNAc post-translational modification of nuclear and cytosolic proteins, appears causal for XLID.

DelPhiForce web server: electrostatic forces and energy calculations and visualization.

SUMMARY: Electrostatic force is an essential component of the total force acting between atoms and macromolecules. Therefore, accurate calculations of electrostatic forces are crucial for revealing the mechanisms of many biological processes. We developed a DelPhiForce web server to calculate and visualize the electrostatic forces at molecular level. DelPhiForce web server enables modeling of electrostatic forces on individual atoms, residues, domains and molecules, and generates an output that can be visualized by VMD software. Here we demonstrate the usage of the server for various biological problems including protein-cofactor, domain-domain, protein-protein, protein-DNA and protein-RNA interactions. AVAILABILITY AND IMPLEMENTATION: The DelPhiForce web server is available at: http://compbio.clemson.edu/delphi-force. CONTACT: delphi@clemson.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Investigation of the Missense Mutations in DHCR7 Gene Associated with Smith-Lemli-Opitz Syndrome.

Smith-Lemli-Opitz syndrome (SLOS) is a cholesterol synthesis disorder characterized by physical, mental, and behavioral symptoms. It is caused by mutations in 7-dehydroxycholesterolreductase gene (DHCR7) encoding DHCR7 protein, which is the rate-limiting enzyme in the cholesterol synthesis pathway. Here we demonstrate that pathogenic mutations in DHCR7 protein are located either within the transmembrane region or are near the ligand-binding site, and are highly conserved among species. In contrast, non-pathogenic mutations observed in the general population are located outside the transmembrane region and have different effects on the conformational dynamics of DHCR7. All together, these observations suggest that the non-classified mutation R228Q is pathogenic. Our analyses indicate that pathogenic effects may affect protein stability and dynamics and alter the binding affinity and flexibility of the binding site.

Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes.

Protein-nucleic acid interactions play a crucial role in many biological processes. This work investigates the changes of pKa values and protonation states of ionizable groups (including nucleic acid bases) that may occur at protein-nucleic acid binding. Taking advantage of the recently developed pKa calculation tool DelphiPka, we utilize the large protein-nucleic acid interaction database (NPIDB database) to model pKa shifts caused by binding. It has been found that the protein's interfacial basic residues experience favorable electrostatic interactions while the protein acidic residues undergo proton uptake to reduce the energy cost upon the binding. This is in contrast with observations made for protein-protein complexes. In terms of DNA/RNA, both base groups and phosphate groups of nucleotides are found to participate in binding. Some DNA/RNA bases undergo pKa shifts at complex formation, with the binding process tending to suppress charged states of nucleic acid bases. In addition, a weak correlation is found between the pH-optimum of protein-DNA/RNA binding free energy and the pH-optimum of protein folding free energy. Overall, the pH-dependence of protein-nucleic acid binding is not predicted to be as significant as that of protein-protein association. Proteins 2017; 85:282-295. © 2016 Wiley Periodicals, Inc.

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding.

Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease-causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index (Pd) and relative folding (Ppr,f) and binding (Ppr,b) probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease.

Cofactors-loaded quaternary structure of lysine-specific demethylase 5C (KDM5C) protein: Computational model.

The KDM5C gene (also known as JARID1C and SMCX) is located on the X chromosome and encodes a ubiquitously expressed 1560-aa protein, which plays an important role in lysine methylation (specifically reverses tri- and di-methylation of Lys4 of histone H3). Currently, 13 missense mutations in KDM5C have been linked to X-linked mental retardation. However, the molecular mechanism of disease is currently unknown due to the experimental difficulties in expressing such large protein and the lack of experimental 3D structure. In this work, we utilize homology modeling, docking, and experimental data to predict 3D structures of KDM5C domains and their mutual arrangement. The resulting quaternary structure includes KDM5C JmjN, ARID, PHD1, JmjC, ZF domains, substrate histone peptide, enzymatic cofactors, and DNA. The predicted quaternary structure was investigated with molecular dynamic simulation for its stability, and further analysis was carried out to identify features measured experimentally. The predicted structure of KDM5C was used to investigate the effects of disease-causing mutations and it was shown that the mutations alter domain stability and inter-domain interactions. The structural model reported in this work could prompt experimental investigations of KDM5C domain-domain interaction and exploration of undiscovered functionalities. Proteins 2016; 84:1797-1809. © 2016 Wiley Periodicals, Inc.

Revealing the Effects of Missense Mutations Causing Snyder-Robinson Syndrome on the Stability and Dimerization of Spermine Synthase.

Missense mutations in spermine synthase (SpmSyn) protein have been shown to cause the Snyder-Robinson syndrome (SRS). Depending on the location within the structure of SpmSyn and type of amino acid substitution, different mechanisms resulting in SRS were proposed. Here we focus on naturally occurring amino acid substitutions causing SRS, which are situated away from the active center of SpmSyn and thus are not directly involved in the catalysis. Two of the mutations, M35R and P112L, are reported for the first time in this study. It is demonstrated, both experimentally and computationally, that for such mutations the major effect resulting in dysfunctional SpmSyn is the destabilization of the protein. In vitro experiments indicated either no presence or very little amount of the mutant SpmSyn in patient cells. In silico modeling predicted that all studied mutations in this work destabilize SpmSyn and some of them abolish homo-dimer formation. Since dimerization and structural stability are equally important for the wild type function of SpmSyn, it is proposed that the SRS caused by mutations occurring in the N-domain of SpmSyn is a result of dysfunctional mutant proteins being partially unfolded and degraded by the proteomic machinery of the cell or being unable to form a homo-dimer.