ModBase, a database of annotated comparative protein structure models and associated resources.

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein-protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein-ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

SAXS Merge: an automated statistical method to merge SAXS profiles using Gaussian processes.

Small-angle X-ray scattering (SAXS) is an experimental technique that allows structural information on biomolecules in solution to be gathered. High-quality SAXS profiles have typically been obtained by manual merging of scattering profiles from different concentrations and exposure times. This procedure is very subjective and results vary from user to user. Up to now, no robust automatic procedure has been published to perform this step, preventing the application of SAXS to high-throughput projects. Here, SAXS Merge, a fully automated statistical method for merging SAXS profiles using Gaussian processes, is presented. This method requires only the buffer-subtracted SAXS profiles in a specific order. At the heart of its formulation is non-linear interpolation using Gaussian processes, which provides a statement of the problem that accounts for correlation in the data.

A convective replica-exchange method for sampling new energy basins.

Replica-exchange is a powerful simulation method for sampling the basins of a rugged energy landscape. The replica-exchange method's sampling is efficient because it allows replicas to perform round trips in temperature space, thereby visiting both low and high temperatures in the same simulation. However, replicas have a diffusive walk in temperature space, and the round trip rate decreases significantly with the system size. These drawbacks make convergence of the simulation even more difficult than it already is when bigger systems are tackled. Here, we present a simple modification of the exchange method. In this method, one of the replicas steadily raises or lowers its temperature. We tested the convective replica-exchange method on three systems of varying complexity: the alanine dipeptide in implicit solvent, the GB1 ß-hairpin in explicit solvent and the Aß(25-35) homotrimer in a coarse grained representation. For the highly frustrated Aß(25-35) homotrimer, the proposed "convective" replica-exchange method is twice as fast as the standard method. It discovered 24 out of 27 free-energy basins in less than 500 ns. It also prevented the formation of groups of replicas that usually form on either side of an exchange bottleneck, leading to a more efficient sampling of new energy basins than in the standard method.

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation.

BACKGROUND: Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns. RESULTS: Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. CONCLUSION: Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.

Automatic Bayesian Weighting for SAXS Data.

Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state.

Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution.

Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult; current methods require that the users choose the resolution of interaction maps based on dataset quality and sequencing depth. Here, we introduce Binless, a resolution-agnostic method that adapts to the quality and quantity of available data, to detect both interactions and differences. Binless relies on an alternate representation of Hi-C data, which leads to a more detailed classification of paired-end reads. Using a large-scale benchmark, we demonstrate that Binless is able to call interactions with higher reproducibility than other existing methods. Binless, which is freely available, can thus reliably be used to identify chromatin loops as well as for differential analysis of chromatin interaction maps.

Promoter bivalency favors an open chromatin architecture in embryonic stem cells.

In embryonic stem cells (ESCs), developmental gene promoters are characterized by their bivalent chromatin state, with simultaneous modification by MLL2 and Polycomb complexes. Although essential for embryogenesis, bivalency is functionally not well understood. Here, we show that MLL2 plays a central role in ESC genome organization. We generate a catalog of bona fide bivalent genes in ESCs and demonstrate that loss of MLL2 leads to increased Polycomb occupancy. Consequently, promoters lose accessibility, long-range interactions are redistributed, and ESCs fail to differentiate. We pose that bivalency balances accessibility and long-range connectivity of promoters, allowing developmental gene expression to be properly modulated.

SAS profile correlations reveal SAS hierarchical nature and information content.

In structural biology, Small-Angle Scattering experiments (SAS) are unique, because although they provide low resolution data, they can be performed in closer-to-native conditions than those arising in X-Ray crystallography. A number of questions on SAS, however, remain unsolved, particularly in the light of modelling ensembles of conformers in solution. In this article, we study the ensemble average and covariance of SAS profiles analytically. Using this ensemble covariance, we demonstrate the hierarchical nature of SAS profiles. Furthermore, we show that the information content is not uniform and reaches its maximum in the intermediate q range. The arguments are generalized using microsecond-scale molecular dynamics trajectories of the lysozyme and on an ensemble of the intrinsically disordered protein p15PAF. We show that for highly flexible systems, the SAS profile is a representation of the ensemble of conformers in solution, and not that of one conformer in particular.

On the demultiplexing of chromosome capture conformation data.

How to describe the multiple chromosome structures that underlie interactions among genome loci and how to quantify the occurrence of these structures in a cell population remain important challenges to solve, which can be addressed via a proper demultiplexing of chromosome capture conformation related data. Here, we first aim to review two main methodologies that have been proposed to tackle this problem: restrained-based methods, in which the resulting chromosome structures stem from the multiple solutions of a distance satisfaction problem; and thermodynamic-based methods, in which the structures stem from the simulation of polymer models. Next, we propose a novel demultiplexing method based on a matrix decomposition of contact maps. To this end, we extend the notion of topologically associated domains (TADs) by introducing that of statistical interaction domains (SIDs). SIDs can overlap and occur in a cell population at certain frequencies, and we propose a simple method to estimate these frequency values. As an application, we show that SIDs that measure 100kb to tens of Mb long occur both frequently and specifically in the human genome.

Restraint-based three-dimensional modeling of genomes and genomic domains.

Chromosomes are large polymer molecules composed of nucleotides. In some species, such as humans, this polymer can sum up to meters long and still be properly folded within the nuclear space of few microns in size. The exact mechanisms of how the meters long DNA is folded into the nucleus, as well as how the regulatory machinery can access it, is to a large extend still a mystery. However, and thanks to newly developed molecular, genomic and computational approaches based on the Chromosome Conformation Capture (3C) technology, we are now obtaining insight on how genomes are spatially organized. Here we review a new family of computational approaches that aim at using 3C-based data to obtain spatial restraints for modeling genomes and genomic domains.

Convective Replica-Exchange in Ergodic Regimes.

In a recent article (J. Comput. Chem. 2013, 34, 132-140), convective replica-exchange (convective-RE) has been presented as an alternative to the standard even-odd transition scheme. Computations on systems of various complexity have shown that convective-RE may increase the number of replica round-trips in temperature space with respect to the standard exchange scheme, leading to a more effective sampling of energy basins. Moreover, it has been shown that the method may prevent the formation of bottlenecks in the diffusive walk of replicas through the space of temperature states. By using an ideal temperature-RE model and a classical harmonic-oscillator RE scheme, we study the performances of convective-RE when ergodicity is not broken and convergence of acceptance probabilities is attained. In this dynamic regime, the round-trip ratio between convective and standard-RE is at maximum ∼ 1.5, a value much smaller than that observed in nonergodic simulations. For large acceptance probabilities, the standard-RE outperforms convective-RE. Our observations suggest that convective-RE can safely be used in either ergodic or non-ergodic regimes; however, convective-RE is advantageous only when bottlenecks occur in the state-space diffusion of replicas, or when acceptance probabilities are globally low. We also show that decoupling of the state-space dynamics of the stick replica from the dynamics of the remaining replicas improves the efficiency of convective-RE at low acceptance probability regimes.

Impact of Thermostats on Folding and Aggregation Properties of Peptides Using the Optimized Potential for Efficient Structure Prediction Coarse-Grained Model.

The simulation of amyloid fibril formation is impossible if one takes into account all chemical details of the amino acids and their detailed interactions with the solvent. We investigate the folding and aggregation of two model peptides using the optimized potential for efficient structure prediction (OPEP) coarse-grained model and replica exchange molecular dynamics (REMD) simulations coupled with either the Langevin or the Berendsen thermostat. For both the monomer of blocked penta-alanine and the trimer of the 25-35 fragment of the Alzheimer's amyloid ß protein, we find little variations in the equilibrium structures and heat capacity curves using the two thermostats. Despite this high similarity, we detect significant differences in the populations of the dominant conformations at low temperatures, whereas the configurational distributions remain the same in proximity of the melting temperature. Aß25-35 trimers at 300 K have an averaged ß-sheet content of 12% and are primarily characterized by fully disordered peptides or a small curved two-stranded ß-sheet stabilized by a disordered peptide. In addition, OPEP molecular dynamics simulations of Aß25-35 hexamers at 300 K with a small curved six-stranded antiparallel ß-sheet do not show any extension of the ß-sheet content. These data support the idea that the mechanism of Aß25-35 amyloid formation does not result from a high fraction of extended ß-sheet-rich trimers and hexamers.