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Computational modeling of 3D chromatin plays an important role in understanding the principles of genome organization. We discuss methods for modeling 3D chromatin structures, with focus on a minimalistic polymer model which inverts population Hi-C into high-resolution, high-coverage single-cell chromatin conformations. Utilizing only basic physical properties such as nuclear volume and no adjustable parameters, this model uncovers a few specific Hi-C interactions (15-35 for enhancerrich loci in human cells) that can fold chromatin into individual conformations consistent with single-cell imaging, Dip-C, and FISH-measured genomic distance distributions. Aggregating an ensemble of conformations also reproduces population Hi-C interaction frequencies. Furthermore, this single-cell modeling approach allows quantification of structural heterogeneity and discovery of specific many-body units of chromatin interactions. This minimalistic 3D chromatin polymer model has revealed a number of insights: 1) chromatin scaling rules are a result of volume-confined polymers; 2) TADs form as a byproduct of 3D chromatin folding driven by specific interactions; 3) chromatin folding at many loci is driven by a small number of specific interactions; 4) cell subpopulations equipped with different chromatin structural scaffolds are developmental stage-dependent; and 5) characterization of the functional landscape and epigenetic marks of many-body units which are simultaneously spatially co-interacting within enhancer-rich, euchromatic regions. The implications of these findings in understanding the genome structure-function relationship are also discussed.

We report the Structure-based Pathogenicity Relationship Identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutation clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial (patHOS) units of dele-terious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and cancer driving mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole protein universe.

Missense SNPs are key factors contributing towards many Mendelian disorders and complex diseases. Identifying whether a single amino acid substitution will lead to pathological effects is important for interpreting personal genome and for precision medicine. In this study, we describe a novel method for predicting whether a missense SNP likely brings about pathological effects. Our approach integrates sequence information, biophysical properties, and topological properties of protein structures. In our test dataset consisting of 500 deleterious variants and 500 neutral, our method achieves an accuracy of 0.823. The ROC curve of model has an AUC of 0.910. Our methods outperforms two well known methods, and is comparable with the widely used Polyphen-2 method, while requiring a much smaller amount (approximately 25%) of training data. Our method can be used to aid in distinguishing driver and passenger mutations in cancer and in assessing missense mutations assocaited with rare diseases. It can also be used to identifying mutations in rare disease where only limited patient exome data exsit.

Bacterial porins often exhibit ion conductance and gating behavior which can be modulated by pH. However, the underlying control mechanism of gating is often complex, and direct inspection of the protein structure is generally insufficient for full mechanistic understanding. Here we describe Pretzel, a computational framework that can effectively model loop-based gating events in membrane proteins. Our method combines Monte Carlo conformational sampling, structure clustering, ensemble energy evaluation, and a topological gating criterion to model the equilibrium gating state under the pH environment of interest. We discuss details of applying Pretzel to the porin outer membrane protein G (OmpG).

Single-cell chromatin studies provide insights into how chromatin structure relates to functions of individual cells. However, balancing high-resolution and genome wide-coverage remains challenging. We describe a computational method for the reconstruction of large 3D-ensembles of single-cell (sc) chromatin conformations from population Hi-C that we apply to study embryogenesis in Drosophila. With minimal assumptions of physical properties and without adjustable parameters, our method generates large ensembles of chromatin conformations via deep-sampling. Our method identifies specific interactions, which constitute 5–6% of Hi-C frequencies, but surprisingly are sufficient to drive chromatin folding, giving rise to the observed Hi-C patterns. Modeled sc-chromatins quantify chromatin heterogeneity, revealing significant changes during embryogenesis. Furthermore, >50% of modeled sc-chromatin maintain topologically associating domains (TADs) in early embryos, when no population TADs are perceptible. Domain boundaries become fixated during development, with strong preference at binding-sites of insulator-complexes upon the midblastula transition. Overall, high-resolution 3D-ensembles of sc-chromatin conformations enable further in-depth interpretation of population Hi-C, improving understanding of the structure-function relationship of genome organization., Balancing high resolution and broad genome coverage in single-cell Hi-C approaches remains challenging. Here, the authors describe a computational method for the reconstruction of a large 3D-ensemble of single-cell chromatin conformations from population Hi-C measurements and apply this model to study embryogenesis in Drosophila.

Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore unsuitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvolving Hi-C data and identifies significant many-body interactions. For a diverse set of highly active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions., Genome Biology, 21, ISSN:1474-760X

Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions can play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore, not suitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, that reconstructs structural ensembles based on Hi-C data and identifies significant many-body interactions. For a diverse set of highly-active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase-accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions.

With the rapid progress of cancer genome studies, many missense mutations in populations of somatic cells of different cancer types and at different stages have been identified. However, it is challenging to understand the implications of these cancer-related variants. We have developed a computational method that integrates structural, topographical, and evolutionary information for assessments of biochemical effects and the extent of deleteriousness of the cancer-related variants. We have mapped somatic missense mutations from the Catalogue of Somatic Mutations In Cancer (COSMIC) to 3D structures in the Protein Data Bank (PDB). Our results show that a large portion of these missense mutations is located on protein surface pockets, which often serve as a structural and functional unit of cancer variants. We provide detailed analysis of several examples and assessment on the importance of these variants, including prediction of previously unreported cancer-variants, along with independent evidence from the literature. Furthermore, we show our predictions can inform on the functional roles and the mechanism of predicted cancer variants.

Outer membrane protein G (OmpG) from Escherichia coli has exhibited pH-dependent gating that can be employed by bacteria to alter the permeability of their outer membranes in response to environmental changes. We developed a computational model, Protein Topology of Zoetic Loops (Pretzel), to investigate the roles of OmpG extracellular loops implicated in gating. The key interactions predicted by our model were verified by single-channel recording data. Our results indicate that the gating equilibrium is primarily controlled by an electrostatic interaction network formed between the gating loop and charged residues in the lumen. The results shed light on the mechanism of OmpG gating and will provide a fundamental basis for the engineering of OmpG as a nanopore sensor. Our computational Pretzel model could be applied to other outer membrane proteins that contain intricate dynamic loops that are functionally important.

Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR

Outer membrane protein G (OmpG), a monomeric beta-barrel membrane protein, has the potential to be used as a nano-sensor device. To achieve this goal, it is important to understand OmpG's ion current conducting properties. Previous studies have demonstrated pH-dependent gating, namely, current flow through the porin decreases as the pH decreases. While it is thought that loop regions have strong influence on the OmpG gating state, there is uncertainty on the mechanism that induces the functional switch as the pH is altered. Based on the DiSGro method of protein loop modeling, we extensively sample the three-dimensional conformations of multiple interacting loop regions of OmpG at various pH levels. Specifically, 10,000 loop samples are generated at pH 5 and pH 7 for each of 6 mutant OmpG variants and the wild type. These loops are used to study the conformations of mutant OmpG variants with experimentally observed differences in gating properties.