MOTIVATION: Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. RESULTS: We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (×105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization. AVAILABILITY AND IMPLEMENTATION: It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

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 mutational 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 clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

The seven papers in this special section examine the latest advances in the field of integrated precision medicine technologies. In the majority of medical conditions common therapeutic approaches are usually effective in only a small percentage of the patient population. Recent scientific discoveries implicate as possible causes of this lack of effect the multifactorial nature of most diseases and the patient variability in disease expression, genetic disposition, and environmental exposures. It has become apparent that in order to improve the response to therapy and long term prognosis, treatment must be specifically tailored to the disease and the patient. Precision medicine is an attempt to maximize effectiveness by taking into account individual variability in clinical presentation, medical history, genes, environment, and lifestyle. It is a leap beyond the promise of “personalization” empowered by recent technological advances. However, progress in precision medicine has been slow due to the lack of “precision” in the traditional research, translation, and clinical practice. Current approaches are largely empirical, fragmented, lack integration, and rely on population statistics, with inadequate feedback between disciplines. In addition, most of the information required for personalization is either missing or unutilized. New technological developments can help overcome these hurdles of imprecision to achieve the full promise of precision medicine.

The IEEE International Conference on Biomedical and Health Informatics (BHI) is a special topic conference of the IEEE Engineering in Medicine and Biology Society (IEEEEMBS). BHI2017 was co-located with the annual HIMSS Conference & Exhibition in Rosen Plaza Hotel, Orlando, Florida, USA during Feb. 16-19, 2017. The focus of BHI2017 was on "informatics for personalized, precision and preventive healthcare." Advancing health informatics has been identified as a grand challenge for engineering in the 21st century by the National Academy of Engineering. Managing and improving human health will require novel and creative informatics solutions to accelerate scientific discovery, translate innovations into successful treatments, re-engineer care practices and infrastructure, all with effective use of hardware and software. BHI2017 provided a unique forum showcasing enabling technologies in both methodology and clinical advancement in data/information acquisition, transmission, storage, retrieval, visualization, processing, analysis, interpretation and validation. In addition, it demonstrated how integrative informatics solutions can be employed in novel clinical applications, and how the deployment of integrated bioinformatics, with the support of Internet of Things (IoT), can facilitate precision and preventive medicine. In this special issue, six papers are selected to exhibit recent development in areas highlighted in BHI2017. The papers are briefly summarized here.

MOTIVATION: Transmembrane beta-barrel proteins (TMBs) serve a multitude of essential cellular functions in Gram-negative bacteria, mitochondria and chloroplasts. Transfer free energies (TFEs) of residues in the transmembrane (TM) region provides fundamental quantifications of thermodynamic stabilities of TMBs, which are important for the folding and the membrane insertion processes, and may help in understanding the structure-function relationship. However, experimental measurement of TFEs of TMBs is challenging. Although a recent computational method can be used to calculate TFEs, the results of which are in excellent agreement with experimentally measured values, this method does not scale up, and is limited to small TMBs. RESULTS: We have developed an approximation method that calculates TFEs of TM residues in TMBs accurately, with which depth-dependent transfer free energy profiles can be derived. Our results are in excellent agreement with experimental measurements. This method is efficient and applicable to all bacterial TMBs regardless of the size of the protein. AVAILABILITY AND IMPLEMENTATION: An online webserver is available at http://tanto.bioe.uic.edu/tmb-tfe . CONTACT: : [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. RESULTS: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. CONCLUSIONS: We demonstrate the effectiveness of the tool through two case studies on the computed probabilistic landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases.

Nuclear landmarks and biochemical factors play important roles in the organization of the yeast genome. The interaction pattern of budding yeast as measured from genome-wide 3C studies are largely recapitulated by model polymer genomes subject to landmark constraints. However, the origin of inter-chromosomal interactions, specific roles of individual landmarks, and the roles of biochemical factors in yeast genome organization remain unclear. Here we describe a multi-chromosome constrained self-avoiding chromatin model (mC-SAC) to gain understanding of the budding yeast genome organization. With significantly improved sampling of genome structures, both intra- and inter-chromosomal interaction patterns from genome-wide 3C studies are accurately captured in our model at higher resolution than previous studies. We show that nuclear confinement is a key determinant of the intra-chromosomal interactions, and centromere tethering is responsible for the inter-chromosomal interactions. In addition, important genomic elements such as fragile sites and tRNA genes are found to be clustered spatially, largely due to centromere tethering. We uncovered previously unknown interactions that were not captured by genome-wide 3C studies, which are found to be enriched with tRNA genes, RNAPIII and TFIIS binding. Moreover, we identified specific high-frequency genome-wide 3C interactions that are unaccounted for by polymer effects under landmark constraints. These interactions are enriched with important genes and likely play biological roles.

The ten papers from this special sectoin were presented at the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14).

Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DISGRO). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is 1:53 A° , with a lowest energy RMSD of 2:99 A° , and an average ensembleRMSD of 5:23 A° . A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about 10 cpu minutes for 12-residue loops, compared to ca 180 cpu minutes using the FALCm method. Test results on benchmark datasets show that DISGRO performs comparably or better than previous successful methods, while requiring far less computing time. DISGRO is especially effective in modeling longer loops (10-17 residues).

BACKGROUND: Identifying structurally similar proteins with different chain topologies can aid studies in homology modeling, protein folding, protein design, and protein evolution. These include circular permuted protein structures, and the more general cases of non-cyclic permutations between similar structures, which are related by non-topological rearrangement beyond circular permutation. We present a method based on an approximation algorithm that finds sequence-order independent structural alignments that are close to optimal. We formulate the structural alignment problem as a special case of the maximum-weight independent set problem, and solve this computationally intensive problem approximately by iteratively solving relaxations of a corresponding integer programming problem. The resulting structural alignment is sequence order independent. Our method is also insensitive to insertions, deletions, and gaps. RESULTS: Using a novel similarity score and a statistical model for significance p-value, we are able to discover previously unknown circular permuted proteins between nucleoplasmin-core protein and auxin binding protein, between aspartate rasemase and 3-dehydrogenate dehydralase, as well as between migration inhibition factor and arginine repressor which involves an additional strand-swapping. We also report the finding of non-cyclic permuted protein structures existing in nature between AML1/core binding factor and ribofalvin synthase. Our method can be used for large scale alignment of protein structures regardless of the topology. CONCLUSION: The approximation algorithm introduced in this work can find good solutions for the problem of protein structure alignment. Furthermore, this algorithm can detect topological differences between two spatially similar protein structures. The alignment between MIF and the arginine repressor demonstrates our algorithm's ability to detect structural similarities even when spatial rearrangement of structural units has occurred. The effectiveness of our method is also demonstrated by the discovery of previously unknown circular permutations. In addition, we report in this study the finding of a naturally occurring non-cyclic permuted protein between AML1/Core Binding Factor chain F and riboflavin synthase chain A.

UNLABELLED: Motivation. Protein design aims to identify sequences compatible with a given protein fold but incompatible to any alternative folds. To select the correct sequences and to guide the search process, a design scoring function is critically important. Such a scoring function should be able to characterize the global fitness landscape of many proteins simultaneously. RESULTS: To find optimal design scoring functions, we introduce two geometric views and propose a formulation using a mixture of non-linear Gaussian kernel functions. We aim to solve a simplified protein sequence design problem. Our goal is to distinguish each native sequence for a major portion of representative protein structures from a large number of alternative decoy sequences, each a fragment from proteins of different folds. Our scoring function discriminates perfectly a set of 440 native proteins from 14 million sequence decoys. We show that no linear scoring function can succeed in this task. In a blind test of unrelated proteins, our scoring function misclassfies only 13 native proteins out of 194. This compares favorably with about three-four times more misclassifications when optimal linear functions reported in the literature are used. We also discuss how to develop protein folding scoring function.