Node features of chromosome structure networks and their connections to genome annotation.

The 3D conformations of chromosomes can encode biological significance, and the implications of such structures have been increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from Hi-C pairwise genome contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. We investigate how additional properties of chromosome structure can be deduced using an abstract graph representation of the contact heatmap, and describe specific network properties that can have a strong connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties. These properties are useful for characterizing certain aspects of chromosomal structure. We examined the ability of network properties to differentiate several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, depletion of chromosome architectural proteins, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties exhibit greater capability in characterizing genome annotations compared to diffusive or ultra-local node features. For example, the local square clustering coefficient can be a strong classifier of lamina-associated domains. We demonstrated that network properties can be useful for highlighting large-scale chromosome structure differences that emerge in different biological situations.

Surface plasmon resonance aptasensing and computational analysis of Staphylococcus aureus IsdA surface protein.

Staphylococcus aureus (S. aureus), a common foodborne pathogen, poses significant public health challenges due to its association with various infectious diseases. A key player in its pathogenicity, which is the IsdA protein, is an essential virulence factor in S. aureus infections. In this work, we present an integrated in-silico and experimental approach using MD simulations and surface plasmon resonance (SPR)-based aptasensing measurements to investigate S. aureus biorecognition via IsdA surface protein binding. SPR, a powerful real-time and label-free technique, was utilized to characterize interaction dynamics between the aptamer and IsdA protein, and MD simulations was used to characterize the stable and dynamic binding regions. By characterizing and optimizing pivotal parameters such as aptamer concentration and buffer conditions, we determined the aptamer's binding performance. Under optimal conditions of pH 7.4 and 150 mM NaCl concentration, the kinetic parameters were determined; ka = 3.789 × 104/Ms, kd = 1.798 × 103/s, and KD = 4.745 × 10-8 M. The simulations revealed regions of interest in the IsdA-aptamer complex. Region I, which includes interactions between amino acid residues H106 and R107 and nucleotide residues 9G, 10U, 11G and 12U of the aptamer, had the strongest interaction, based on ΔG and B-factor values, and hence contributed the most to the stability of the interaction. Region II, which covers residue 37A reflects the dynamic nature of the interaction due to frequent contacts. The approach presents a rigorous characterization of aptamer-IsdA binding behavior, supporting the potential application of the IsdA-binding aptamer system for S. aureus biosensing.

Novel Covalent Modifier-Induced Local Conformational Changes within the Intrinsically Disordered Region of the Androgen Receptor.

Intrinsically disordered regions (IDRs) of transcription factors play an important biological role in liquid condensate formation and gene regulation. It is thus desirable to investigate the druggability of IDRs and how small-molecule binders can alter their conformational stability. For the androgen receptor (AR), certain covalent ligands induce important changes, such as the neutralization of the condensate. To understand the specificity of ligand-IDR interaction and potential implications for the mechanism of neutralizing liquid-liquid phase separation (LLPS), we modeled and performed computer simulations of ligand-bound peptide segments obtained from the human AR. We analyzed how different covalent ligands affect local secondary structure, protein contact map, and protein-ligand contacts for these protein systems. We find that effective neutralizers make specific interactions (such as those between cyanopyrazole and tryptophan) that alter the helical propensity of the peptide segments. These findings on the mechanism of action can be useful for designing molecules that influence IDR structure and condensate of the AR in the future.

Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment.

Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state.

Statistical Analysis of Protein-Ligand Interaction Patterns in Nuclear Receptor RORγ.

The receptor RORγ belongs to the nuclear receptor superfamily that senses small signaling molecules and regulates at the gene transcription level. Since RORγ has a high basal activity and plays an important role in immune responses, inhibitors targeting this receptor have been a focus for many studies. The receptor-ligand interaction is complex, and often subtle differences in ligand structure can determine its role as an inverse agonist or an agonist. We examined more than 130 existing RORγ crystal structures that have the same receptor complexed with different ligands. We reported the features of receptor-ligand interaction patterns and the differences between agonist and inverse agonist binding. Specific changes in the contact interaction map are identified to distinguish active and inactive conformations. Further statistical analysis of the contact interaction patterns using principal component analysis reveals a dominant mode which separates allosteric binding vs. canonical binding and a second mode which may indicate active vs. inactive structures. We also studied the nature of constitutive activity by performing a 100-ns computer simulation of apo RORγ. Using constitutively active nuclear receptor CAR as a comparison, we identified a group of conserved contacts that have similar contact strength between the two receptors. These conserved contact interactions, especially a couple key contacts in H11-H12 interaction, can be considered essential to the constitutive activity of RORγ. These protein-ligand and internal protein contact interactions can be useful in the development of new drugs that direct receptor activity.

Protein conformational switch discerned via network centrality properties.

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation.

The conformational ensemble of intrinsically disordered proteins, such as α-synuclein, are responsible for their function and malfunction. Misfolding of α-synuclein can lead to neurodegenerative diseases, and the ability to study their conformations and those of other intrinsically disordered proteins under varying physiological conditions can be crucial to understanding and preventing pathologies. In contrast to well-folded peptides, a consensus feature of IDPs is their low hydropathy and high charge, which makes their conformations sensitive to pH perturbation. We examine a prominent member of this subset of IDPs, α-synuclein, using a divide-and-conquer scheme that provides enhanced sampling of IDP structural ensembles. We constructed conformational ensembles of α-synuclein under neutral (pH ~ 7) and low (pH ~ 3) pH conditions and compared our results with available information obtained from smFRET, SAXS, and NMR studies. Specifically, α-synuclein has been found to in a more compact state at low pH conditions and the structural changes observed are consistent with those from experiments. We also characterize the conformational and dynamic differences between these ensembles and discussed the implication on promoting pathogenic fibril formation. We find that under low pH conditions, neutralization of negatively charged residues leads to compaction of the C-terminal portion of α-synuclein while internal reorganization allows α-synuclein to maintain its overall end-to-end distance. We also observe different levels of intra-protein interaction between three regions of α-synuclein at varying pH and a shift towards more hydrophilic interactions with decreasing pH.

Inferring chromosome radial organization from Hi-C data.

BACKGROUND: The nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations. RESULTS: A number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromosome ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells. CONCLUSIONS: Our analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demonstrate which stages of the approach can extract meaningful information, and also describe limitations of pairwise contacts alone to predict absolute 3D positions.

Using contact statistics to characterize structure transformation of biopolymer ensembles.

As a unique subset of functional polymers, many biopolymers have a set of well-defined three-dimensional (3D) structural characteristics that can be described by spatial contacts between monomers. Statistical analysis of the contacts has been extremely productive in characterizing the biopolymer structural ensemble, such as for 3D chromosome structures. Often, native contacts and compartment structures are the focus of the studies, while the generic polymer aspect, such as the overall decaying of contacts with increasing sequence distance, is analyzed separately or preemptively removed. Here, we explore insights that can be gained by performing "compartment analysis" that keeps the distance decay, which we believe is particularly useful for characterizing the structure transformation of biopolymers. We tested contact analysis on several such transformations under physical perturbation or biological processes, including (1) unfolding of proteins induced by thermal denaturation, (2) chromosome conformation transition during the cell cycle, and (3) chromosome unpacking by physicochemical perturbations. Useful score functions were developed to further quantitatively characterize the transformation judging from the contact analysis. We also find that the sinusoidal undertone of eigenvector patterns (the "unwanted," low frequency signal, in contrast to the detailed A/B compartment) that had previously been attributed to biological effects of centromere proximal and distal interactions may in fact reflect a universal feature of polymers that have relatively weaker long-range contacts.

Regulatory Mechanics of Constitutive Androstane Receptors: Basal and Ligand-Directed Actions.

Constitutive androstane receptor (CAR) is a nuclear hormone receptor that primarily functions in sensing and metabolizing xenobiotics. The basal activity of this receptor is relatively high, and CAR is deemed active in the absence of ligand. The (over)activation can promote drug toxicity and tumor growth. Thus, therapeutic treatments seek inverse agonists to inhibit or modulate CAR activities. To advance our understanding of the regulatory mechanisms of CAR, we used computational and experimental approaches to elucidate three aspects of CAR activation and inactivation: (1) ligand-dependent actions, (2) ligand-orthologue specificity, and (3) constitutive activity. For ligand-dependent actions, we examined the ligand-bound simulations and identified two sets of ligand-induced contacts promoting CAR activation via coactivator binding (H11-H12 contact) or inactivation via corepressor binding (H4-H11 contact). For orthologue specificity, we addressed a puzzling fact that murine CAR (mCAR) and human CAR (hCAR) respond differently to the same ligand (CITCO), despite their high sequence homology. We found that the helix H7 of hCAR is responsible for a stronger binding of the ligand CITCO compared to mCAR, hence a stronger CITCO-induced activation. For basal activity, we reported computer-generated unliganded CAR structures and critical mutagenesis (mCAR's V209A and N333D) results of a cell-based transcription assay. Our results reveal that the basal conformation of CAR shares prominent features with the agonist-bound form, and helix HX has an important contribution to the constitutive activity. These findings altogether can be useful for the understanding of constitutively active receptors and the design of drug molecules targeting them.

Effector-Binding-Directed Dimerization and Dynamic Communication between Allosteric Sites of Ribonucleotide Reductase.

Proteins forming dimers or larger complexes can be strongly influenced by their effector-binding status. We investigated how the effector-binding event is coupled with interface formation via computer simulations, and we quantified the correlation of two types of contact interactions: between the effector and its binding pocket and between protein monomers. This was achieved by connecting the protein dynamics at the monomeric level with the oligomer interface information. We applied this method to ribonucleotide reductase (RNR), an essential enzyme for de novo DNA synthesis. RNR contains two important allosteric sites, the s-site (specificity site) and the a-site (activity site), which bind different effectors. We studied these different binding states with atomistic simulation and used their coarse-grained contact information to analyze the protein dynamics. The results reveal that the effector-protein dynamics at the s-site and dimer interface formation are positively coupled. We further quantify the resonance level between these two events, which can be applied to other similar systems. At the a-site, different effector-binding states (ATP vs dATP) drastically alter the protein dynamics and affect the activity of the enzyme. On the basis of these results, we propose a new mechanism of how the a-site regulates enzyme activation.

Characterizing the 3D structure and dynamics of chromosomes and proteins in a common contact matrix framework.

Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described using contact matrices. Principal component analysis (PCA) on the contact data has been used to interrogate both protein and chromosome structures and/or dynamics. However, as these fields have developed separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I-PCA) has been applied to chromosome contact matrices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E-PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E-PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from microscopy data can reveal the dominant motion (concerted fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromosome structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simulation snapshots of proteins, the major component reported the consensus features of the structure, making this a promising approach for future analysis of semi-structured proteins.

CAMERRA: An analysis tool for the computation of conformational dynamics by evaluating residue-residue associations.

A computational method which extracts the dominant motions from an ensemble of biomolecular conformations via a correlation analysis of residue-residue contacts is presented. The algorithm first renders the structural information into contact matrices, then constructs the collective modes based on the correlated dynamics of a selected set of dynamic contacts. Associated programs can bridge the results for further visualization using graphics software. The aim of this method is to provide an analysis of conformations of biopolymers from the contact viewpoint. It may assist a systematical uncovering of conformational switching mechanisms existing in proteins and biopolymer systems in general by statistical analysis of simulation snapshots. In contrast to conventional correlation analyses of Cartesian coordinates (such as distance covariance analysis and Cartesian principal component analysis), this program also provides an alternative way to locate essential collective motions in general. Herein, we detail the algorithm in a stepwise manner and comment on the importance of the method as applied to decoding allosteric mechanisms. © 2018 Wiley Periodicals, Inc.

Characterizing protein conformations by correlation analysis of coarse-grained contact matrices.

We have developed a method to capture the essential conformational dynamics of folded biopolymers using statistical analysis of coarse-grained segment-segment contacts. Previously, the residue-residue contact analysis of simulation trajectories was successfully applied to the detection of conformational switching motions in biomolecular complexes. However, the application to large protein systems (larger than 1000 amino acid residues) is challenging using the description of residue contacts. Also, the residue-based method cannot be used to compare proteins with different sequences. To expand the scope of the method, we have tested several coarse-graining schemes that group a collection of consecutive residues into a segment. The definition of these segments may be derived from structural and sequence information, while the interaction strength of the coarse-grained segment-segment contacts is a function of the residue-residue contacts. We then perform covariance calculations on these coarse-grained contact matrices. We monitored how well the principal components of the contact matrices is preserved using various rendering functions. The new method was demonstrated to assist the reduction of the degrees of freedom for describing the conformation space, and it potentially allows for the analysis of a system that is approximately tenfold larger compared with the corresponding residue contact-based method. This method can also render a family of similar proteins into the same conformational space, and thus can be used to compare the structures of proteins with different sequences.

DMSO enhanced conformational switch of an interfacial enzyme.

Interfacial proteins function in unique heterogeneous solvent environments, such as water-oil interfaces. One important example is microbial lipase, which is activated in an oil-water emulsion phase and has many important enzymatic functions. A unique aprotic dipolar organic solvent, dimethyl sulfoxide (DMSO), has been shown to increase the activity of lipases, but the mechanism behind this enhancement is still unknown. Here, all-atom molecular dynamics simulations of lipase in a binary solution were performed to examine the effects of DMSO on the dynamics of the gating mechanism. The amphiphilic α5 region of the lipase was a focal point for the analysis, since the structural ordering of α5 has been shown to be important for gating under other perturbations. Compared to the closed-gorge ensemble in an aqueous environment, the conformational ensemble shifts towards open-gorge structures in the presence of DMSO solvents. Increased width of the access channel is particularly prevalent in 45% and 60% DMSO concentrations (w/w). As the amount of DMSO increases, the α5 region of the lipase becomes more α-helical, as we previously observed in studies that address water-oil interfacial and high pressure activation. We believe that the structural ordering of α5 plays an essential role on gating and lipase activity.

The Promiscuity of Allosteric Regulation of Nuclear Receptors by Retinoid X Receptor.

The promiscuous protein retinoid X receptor (RXR) displays essential allosteric regulation of several members in the nuclear hormone receptor superfamily via heterodimerization and (anti)cooperative binding of cognate ligands. Here, the structural basis of the positive allostery of RXR and constitutive androstane receptor (CAR) is revealed. In contrast, a similar computational approach had previously revealed the mechanism for negative allostery in the complex of RXR and thyroid receptor (TR). By comparing the positive and negative allostery of RXR complexed with CAR and TR respectively, we reported the promiscuous allosteric control involving RXR. We characterize the allosteric mechanism by expressing the correlated dynamics of selected residue-residue contacts which was extracted from atomistic molecular dynamics simulation and statistical analysis. While the same set of residues in the binding pocket of RXR may initiate the residue-residue interaction network, RXR uses largely different sets of contacts (only about one-third identical) and allosteric modes to regulate TR and CAR. The promiscuity of RXR control may originate from multiple factors, including (1) the frustrated fit of cognate ligand 9c to the RXR binding pocket and (2) the different ligand-binding features of TR (loose) versus CAR (tight) to their corresponding cognate ligands.

Pressure-induced conformational switch of an interfacial protein.

A special class of proteins adopts an inactive conformation in aqueous solution and activates at an interface (such as the surface of lipid droplet) by switching their conformations. Lipase, an essential enzyme for breaking down lipids, serves as a model system for studying such interfacial proteins. The underlying conformational switch of lipase induced by solvent condition is achieved through changing the status of the gated substrate-access channel. Interestingly, a lipase was also reported to exhibit pressure activation, which indicates it is drastically active at high hydrostatic pressure. To unravel the molecular mechanism of this unusual phenomenon, we examined the structural changes induced by high hydrostatic pressures (up to 1500 MPa) using molecular dynamics simulations. By monitoring the width of the access channel, we found that the protein undergoes a conformational transition and opens the access channel at high pressures (>100 MPa). Particularly, a disordered amphiphilic α5 region of the protein becomes ordered at high pressure. This positive correlation between the channel opening and α5 ordering is consistent with the early findings of the gating motion in the presence of a water-oil interface. Statistical analysis of the ensemble of conformations also reveals the essential collective motions of the protein and how these motions contribute to gating. Arguments are presented as to why heightened sensitivity to high-pressure perturbation can be a general feature of switchable interfacial proteins. Further mutations are also suggested to validate our observations. Proteins 2016; 84:820-827. © 2016 Wiley Periodicals, Inc.

Investigation of Carbohydrate Recognition via Computer Simulation.

Carbohydrate recognition by proteins, such as lectins and other (bio)molecules, can be essential for many biological functions. Recently, interest has arisen due to potential protein and drug design and future bioengineering applications. A quantitative measurement of carbohydrate-protein interaction is thus important for the full characterization of sugar recognition. We focus on the aspect of utilizing computer simulations and biophysical models to evaluate the strength and specificity of carbohydrate recognition in this review. With increasing computational resources, better algorithms and refined modeling parameters, using state-of-the-art supercomputers to calculate the strength of the interaction between molecules has become increasingly mainstream. We review the current state of this technique and its successful applications for studying protein-sugar interactions in recent years.

Mapping allostery through computational glycine scanning and correlation analysis of residue-residue contacts.

Understanding allosteric mechanisms is essential for the physical control of molecular switches and downstream cellular responses. However, it is difficult to decode essential allosteric motions in a high-throughput scheme. A general two-pronged approach to performing automatic data reduction of simulation trajectories is presented here. The first step involves coarse-graining and identifying the most dynamic residue-residue contacts. The second step is performing principal component analysis of these contacts and extracting the large-scale collective motions expressed via these residue-residue contacts. We demonstrated the method using a protein complex of nuclear receptors. Using atomistic modeling and simulation, we examined the protein complex and a set of 18 glycine point mutations of residues that constitute the binding pocket of the ligand effector. The important motions that are responsible for the allostery are reported. In contrast to conventional induced-fit and lock-and-key binding mechanisms, a novel "frustrated-fit" binding mechanism of RXR for allosteric control was revealed.

Brownian dynamics simulation of peptides with the University of Houston Brownian Dynamics (UHBD) program.

This chapter provides the background theory and a practical protocol for performing Brownian dynamics simulation of peptides. Brownian dynamics simulation represents a complementary approach to Monte Carlo and molecular dynamics methods. Unlike Monte Carlo methods, it could provide dynamical information in a timescale longer than the momentum relaxation time. On the other hand, it is faster than molecular dynamics by approximating the solvent by a continuum and by operating in the over-damped limit. This chapter introduces the use of the University of Houston Brownian Dynamics (UHBD) program [1, 2] to perform Brownian dynamics simulation on peptides.