Harianto Tjong

Genome architectures revealed by tethered chromosome conformation capture and population-based modeling

We describe tethered conformation capture (TCC), a method for genome-wide mapping of chromatin interactions. By performing ligations on solid substrates rather than in solution, TCC substantially enhances the signal-to-noise ratio, thereby facilitating a detailed analysis of interactions within and between chromosomes. We identified a group of regions in each chromosome in human cells that account for the majority of interchromosomal interactions. These regions are marked by high transcriptional activity, suggesting that their interactions are mediated by transcriptional machinery. Each of these regions interacts with numerous other such regions throughout the genome in an indiscriminate fashion, partly driven by the accessibility of the partners. As a different combination of interactions is likely present in different cells, we developed a computational method to translate the TCC data into physical chromatin contacts in a population of three-dimensional genome structures. Statistical analysis of the resulting population demonstrates that the indiscriminate properties of interchromosomal interactions are consistent with the well-known architectural features of the human genome.

Physical tethering and volume exclusion determine higher-order genome organization in budding yeast

In this paper we show that tethering of heterochromatic regions to nuclear landmarks and random encounters of chromosomes in the confined nuclear volume are sufficient to explain the higher-order organization of the budding yeast genome. We have quantitatively characterized the contact patterns and nuclear territories that emerge when chromosomes are allowed to behave as constrained but otherwise randomly configured flexible polymer chains in the nucleus. Remarkably, this constrained random encounter model explains in a statistical manner the experimental hallmarks of the S. cerevisiae genome organization, including (1) the folding patterns of individual chromosomes; (2) the highly enriched interactions between specific chromatin regions and chromosomes; (3) the emergence, shape, and position of gene territories; (4) the mean distances between pairs of telomeres; and (5) even the co-location of functionally related gene loci, including early replication start sites and tRNA genes. Therefore, most aspects of the yeast genome organization can be explained without calling on biochemically mediated chromatin interactions. Such interactions may modulate the pre-existing propensity for co-localization but seem not to be the cause for the observed higher-order organization. The fact that geometrical constraints alone yield a highly organized genome structure, on which different functional elements are specifically distributed, has strong implications for the folding principles of the genome and the evolution of its function.

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Significance We provide a method for population-based structure modeling of whole diploid genomes using Hi-C data. The method considers the stochastic nature of chromosome structures, which allows a detailed analysis of the dynamic landscape of genome organizations. We predict and experimentally validate the presence of chromosome-specific higher-order centromere clusters, which can play a key role in the spatial organization of the human genome, specifically influencing the overall chromosome positioning, as well as the preference of specific chromosome conformations. Our approach generate predictive structural models of diploid genomes from Hi-C data, which can provide insights into the guiding principles of 3D genome organizations. Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

Spontaneous conformational change and toxin binding in α7 acetylcholine receptor: Insight into channel activation and inhibition

Nicotinic AChRs (nAChRs) represent a paradigm for ligand-gated ion channels. Despite intensive studies over many years, our understanding of the mechanisms of activation and inhibition for nAChRs is still incomplete. Here, we present molecular dynamics (MD) simulations of the α7 nAChR ligand-binding domain, both in apo form and in α-Cobratoxin-bound form, starting from the respective homology models built on crystal structures of the acetylcholine-binding protein. The toxin-bound form was relatively stable, and its structure was validated by calculating mutational effects on the toxin-binding affinity. However, in the apo form, one subunit spontaneously moved away from the conformation of the other four subunits. This motion resembles what has been proposed for leading to channel opening. At the top, the C loop and the adjacent β7-β8 loop swing downward and inward, whereas at the bottom, the F loop and the C terminus of β10 swing in the opposite direction. These swings appear to tilt the whole subunit clockwise. The resulting changes in solvent accessibility show strong correlation with experimental results by the substituted cysteine accessibility method upon addition of acetylcholine. Our MD simulation results suggest a mechanistic model in which the apo form, although predominantly sampling the “closed” state, can make excursions into the “open” state. The open state has high affinity for agonists, leading to channel activation, whereas the closed state upon distortion has high affinity for antagonists, leading to inhibition.

Prediction of Protein Solubility from Calculation of Transfer Free Energy

Solubility plays a major role in protein purification, and has serious implications in many diseases. We studied the effects of pH and mutations on protein solubility by calculating the transfer free energy from the condensed phase to the solution phase. The condensed phase was modeled as an implicit solvent, with a dielectric constant lower than that of water. To account for the effects of pH, the protonation states of titratable side chains were sampled by running constant-pH molecular dynamics simulations. Conformations were then selected for calculations of the electrostatic solvation energy: once for the condensed phase, and once for the solution phase. The average transfer free energy from the condensed phase to the solution phase was found to predict reasonably well the variations in solubility of ribonuclease Sa and insulin with pH. This treatment of electrostatic contributions combined with a similar approach for nonelectrostatic contributions led to a quantitative rationalization of the effects of point mutations on the solubility of ribonuclease Sa. This study provides valuable insights into the physical basis of protein solubility.

GBr6NL: A generalized Born method for accurately reproducing solvation energy of the nonlinear Poisson-Boltzmann equation

The nonlinear Poisson-Boltzmann (NLPB) equation can provide accurate modeling of electrostatic effects for nucleic acids and highly charged proteins. Generalized Born methods have been developed to mimic the linearized Poisson-Boltzmann (LPB) equation at substantially reduced cost. The computer time for solving the NLPB equation is approximately fivefold longer than for the LPB equation, thus presenting an even greater obstacle. Here we present the first generalized Born method, GBr(6)NL, for mimicking the NLPB equation. GBr(6)NL is adapted from GBr(6), a generalized Born method recently developed to reproduce the solvation energy of the LPB equation [Tjong and Zhou, J. Phys. Chem. B 111, 3055 (2007)]. Salt effects predicted by GBr(6)NL on 55 proteins overall deviate from NLPB counterparts by 0.5 kcal/mol from ionic strengths from 10 to 1000 mM, which is approximately 10% of the average magnitudes of the salt effects. GBr(6)NL predictions for the salts effects on the electrostatic interaction energies of two protein:RNA complexes are very promising.

TopDom: an efficient and deterministic method for identifying topological domains in genomes

Genome-wide proximity ligation assays allow the identification of chromatin contacts at unprecedented resolution. Several studies reveal that mammalian chromosomes are composed of topological domains (TDs) in sub-mega base resolution, which appear to be conserved across cell types and to some extent even between organisms. Identifying topological domains is now an important step toward understanding the structure and functions of spatial genome organization. However, current methods for TD identification demand extensive computational resources, require careful tuning and/or encounter inconsistencies in results. In this work, we propose an efficient and deterministic method, TopDom, to identify TDs, along with a set of statistical methods for evaluating their quality. TopDom is much more efficient than existing methods and depends on just one intuitive parameter, a window size, for which we provide easy-to-implement optimization guidelines. TopDom also identifies more and higher quality TDs than the popular directional index algorithm. The TDs identified by TopDom provide strong support for the cross-tissue TD conservation. Finally, our analysis reveals that the locations of housekeeping genes are closely associated with cross-tissue conserved TDs. The software package and source codes of TopDom are available at http://zhoulab.usc.edu/TopDom/.

Exploring the Spatial and Temporal Organization of a Cell’s Proteome

To increase our current understanding of cellular processes, such as cell signaling and division, knowledge is needed about the spatial and temporal organization of the proteome at different organizational levels. These levels cover a wide range of length and time scales: from the atomic structures of macromolecules for inferring their molecular function, to the quantitative description of their abundance, and spatial distribution in the cell. Emerging new experimental technologies are greatly increasing the availability of such spatial information on the molecular organization in living cells. This review addresses three fields that have significantly contributed to our understanding of the proteomes spatial and temporal organization: first, methods for the structure determination of individual macromolecular assemblies, specifically the fitting of atomic structures into density maps generated from electron microscopy techniques; second, research that visualizes the spatial distributions of these complexes within the cellular context using cryo electron tomography techniques combined with computational image processing; and third, methods for the spatial modeling of the dynamic organization of the proteome, specifically those methods for simulating reaction and diffusion of proteins and complexes in crowded intracellular fluids. The long-term goal is to integrate the varied data about a proteomes organization into a spatially explicit, predictive model of cellular processes.

PI2PE: protein interface/interior prediction engine

The side chains of the 20 types of amino acids, owing to a large extent to their different physical properties, have characteristic distributions in interior/surface regions of individual proteins and in interface/non-interface portions of protein surfaces that bind proteins or nucleic acids. These distributions have important structural and functional implications. We have developed accurate methods for predicting the solvent accessibility of amino acids from a protein sequence and for predicting interface residues from the structure of a protein-binding or DNA-binding protein. The methods are called WESA, cons-PPISP and DISPLAR, respectively. The web servers of these methods are now available at http://pipe.scs.fsu.edu. To illustrate the utility of these web servers, cons-PPISP and DISPLAR predictions are used to construct a structural model for a multicomponent protein–DNA complex.

The Folding Transition-State Ensemble of a Four-Helix Bundle Protein: Helix Propensity as a Determinant and Macromolecular Crowding as a Probe

The four-helix bundle protein Rd-apocyt b(562), a redesigned stable variant of apocytochrome b(562), exhibits two-state folding kinetics. Its transition-state ensemble has been characterized by Phi-value analysis. To elucidate the molecular basis of the transition-state ensemble, we have carried out high-temperature molecular dynamics simulations of the unfolding process. In six parallel simulations, unfolding started with the melting of helix I and the C-terminal half of helix IV, and followed by helix III, the N-terminal half of helix IV and helix II. This ordered melting of the helices is consistent with the conclusion from native-state hydrogen exchange, and can be rationalized by differences in intrinsic helix propensity. Guided by experimental Phi-values, a putative transition-state ensemble was extracted from the simulations. The residue helical probabilities of this transition-state ensemble show good correlation with the Phi-values. To further validate the putative transition-state ensemble, the effect of macromolecular crowding on the relative stability between the unfolded ensemble and the transition-state ensemble was calculated. The resulting effect of crowding on the folding kinetics agrees well with experimental observations. This study shows that molecular dynamics simulations combined with calculation of crowding effects provide an avenue for characterize the transition-state ensemble in atomic details.