CGeNArate: a sequence-dependent coarse-grained model of DNA for accurate atomistic MD simulations of kb-long duplexes.

We present CGeNArate, a new model for molecular dynamics simulations of very long segments of B-DNA in the context of biotechnological or chromatin studies. The developed method uses a coarse-grained Hamiltonian with trajectories that are back-mapped to the atomistic resolution level with extreme accuracy by means of Machine Learning Approaches. The method is sequence-dependent and reproduces very well not only local, but also global physical properties of DNA. The efficiency of the method allows us to recover with a reduced computational effort high-quality atomic-resolution ensembles of segments containing many kilobases of DNA, entering into the gene range or even the entire DNA of certain cellular organelles.

Deciphering the mechanical code of the genome and epigenome.

Diverse DNA-deforming processes are impacted by the local mechanical and structural properties of DNA, which in turn depend on local sequence and epigenetic modifications. Deciphering this mechanical code (that is, this dependence) has been challenging due to the lack of high-throughput experimental methods. Here we present a comprehensive characterization of the mechanical code. Utilizing high-throughput measurements of DNA bendability via loop-seq, we quantitatively established how the occurrence and spatial distribution of dinucleotides, tetranucleotides and methylated CpG impact DNA bendability. We used our measurements to develop a physical model for the sequence and methylation dependence of DNA bendability. We validated the model by performing loop-seq on mouse genomic sequences around transcription start sites and CTCF-binding sites. We applied our model to test the predictions of all-atom molecular dynamics simulations and to demonstrate that sequence and epigenetic modifications can mechanically encode regulatory information in diverse contexts.

MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution.

The linear sequence of DNA provides invaluable information about genes and their regulatory elements along chromosomes. However, to fully understand gene function and regulation, we need to dissect how genes physically fold in the three-dimensional nuclear space. Here we describe immuno-OligoSTORM, an imaging strategy that reveals the distribution of nucleosomes within specific genes in super-resolution, through the simultaneous visualization of DNA and histones. We combine immuno-OligoSTORM with restraint-based and coarse-grained modeling approaches to integrate super-resolution imaging data with Hi-C contact frequencies and deconvoluted micrococcal nuclease-sequencing information. The resulting method, called Modeling immuno-OligoSTORM, allows quantitative modeling of genes with nucleosome resolution and provides information about chromatin accessibility for regulatory factors, such as RNA polymerase II. With Modeling immuno-OligoSTORM, we explore intercellular variability, transcriptional-dependent gene conformation, and folding of housekeeping and pluripotency-related genes in human pluripotent and differentiated cells, thereby obtaining the highest degree of data integration achieved so far to our knowledge.

Solvent Sites Improve Docking Performance of Protein-Protein Complexes and Protein-Protein Interface-Targeted Drugs.

Protein-protein interactions (PPIs) are essential, and modulating their function through PPI-targeted drugs is an important research field. PPI sites are shallow protein surfaces readily accessible to the solvent, thus lacking a proper pocket to fit a drug, while their lack of endogenous ligands prevents drug design by chemical similarity. The development of PPI-blocking compounds is, therefore, a tough challenge. Mixed solvent molecular dynamics has been shown to reveal protein-ligand interaction hot spots in protein active sites by identifying solvent sites (SSs). Furthermore, our group has shown that SSs significantly improve protein-ligand docking. In the present work, we extend our analysis to PPI sites. In particular, we analyzed water, ethanol, and phenol-derived sites in terms of their capacity to predict protein-drug and protein-protein interactions. Subsequently, we show how this information can be incorporated to improve both protein-ligand and protein-protein docking. Finally, we highlight the presence of aromatic clusters as key elements of the corresponding interactions.

Cosolvent Sites-Based Discovery of Mycobacterium Tuberculosis Protein Kinase G Inhibitors.

Computer-aided drug discovery methods play a major role in the development of therapeutically important small molecules, but their performance needs to be improved. Molecular dynamics simulations in mixed solvents are useful in understanding protein-ligand recognition and improving molecular docking predictions. In this work, we used ethanol as a cosolvent to find relevant interactions for ligands toward protein kinase G, an essential protein of Mycobacterium tuberculosis (Mtb). We validated the hot spots by screening a database of fragment-like compounds and another one of known kinase inhibitors. Next, we performed a pharmacophore-guided docking simulation and found three low micromolar inhibitors, including one with a novel chemical scaffold that we expanded to four derivative compounds. Binding affinities were characterized by intrinsic fluorescence quenching assays, isothermal titration calorimetry, and the analysis of melting curves. The predicted binding mode was confirmed by X-ray crystallography. Finally, the compounds significantly inhibited the viability of Mtb in infected THP-1 macrophages.

Specificity and Reactivity of Mycobacterium tuberculosis Serine/Threonine Kinases PknG and PknB.

Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis, has 11 eukaryotic-like serine/threonine protein kinases, which play essential roles in cell growth, signal transduction, and pathogenesis. Protein kinase G (PknG) regulates the carbon and nitrogen metabolism by phosphorylation of the glycogen accumulation regulator (GarA) protein at Thr21. Protein kinase B (PknB) is involved in cell wall synthesis and cell shape, as well as phosphorylates GarA but at Thr22. While PknG seems to be constitutively activated and recognition of GarA requires phosphorylation in its unstructured tail, PknB activation is triggered by phosphorylation of its activation loop, which allows binding of the forkhead-associated domain of GarA. In the present work, we used molecular dynamics and quantum-mechanics/molecular mechanics simulations of the catalytically competent complex and kinase activity assays to understand PknG/PknB specificity and reactivity toward GarA. Two hydrophobic residues in GarA, Val24 and Phe25, seem essential for PknG binding and allow specificity for Thr21 phosphorylation. On the other hand, phosphorylated residues in PknB bind Arg26 in GarA and regulate its specificity for Thr22. We also provide a detailed analysis of the free energy profile for the phospho-transfer reaction and show why PknG has a constitutively active conformation not requiring priming phosphorylation in contrast to PknB. Our results provide new insights into these two key enzymes relevant for Mtb and the mechanisms of serine/threonine phosphorylation in bacteria.

Impact of DNA methylation on 3D genome structure.

Determining the effect of DNA methylation on chromatin structure and function in higher organisms is challenging due to the extreme complexity of epigenetic regulation. We studied a simpler model system, budding yeast, that lacks DNA methylation machinery making it a perfect model system to study the intrinsic role of DNA methylation in chromatin structure and function. We expressed the murine DNA methyltransferases in Saccharomyces cerevisiae and analyzed the correlation between DNA methylation, nucleosome positioning, gene expression and 3D genome organization. Despite lacking the machinery for positioning and reading methylation marks, induced DNA methylation follows a conserved pattern with low methylation levels at the 5' end of the gene increasing gradually toward the 3' end, with concentration of methylated DNA in linkers and nucleosome free regions, and with actively expressed genes showing low and high levels of methylation at transcription start and terminating sites respectively, mimicking the patterns seen in mammals. We also see that DNA methylation increases chromatin condensation in peri-centromeric regions, decreases overall DNA flexibility, and favors the heterochromatin state. Taken together, these results demonstrate that methylation intrinsically modulates chromatin structure and function even in the absence of cellular machinery evolved to recognize and process the methylation signal.

Biased Docking for Protein-Ligand Pose Prediction.

The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.

Sequence-dependent structural properties of B-DNA: what have we learned in 40 years?

The structure of B-DNA, the physiological form of the DNA molecule, has been a central topic in biology, chemistry and physics. Far from uniform and rigid, the double helix was revealed as a flexible and structurally polymorphic molecule. Conformational changes that lead to local and global changes in the helix geometry are mediated by a complex choreography of base and backbone rearrangements affecting the ability of the B-DNA to recognize ligands and consequently on its functionality. In this sense, the knowledge obtained from the sequence-dependent structural properties of B-DNA has always been thought crucial to rationalize how ligands and, most notably, proteins recognize B-DNA and modulate its activity, i.e. the structural basis of gene regulation. Honouring the anniversary of the first high-resolution X-ray structure of a B-DNA molecule, in this contribution, we present the most important discoveries of the last 40 years on the sequence-dependent structural and dynamical properties of B-DNA, from the early beginnings to the current frontiers in the field.

Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening.

Virtual screening of large compound databases, looking for potential ligands of a target protein, is a major tool in computer-aided drug discovery. Throughout the years, different techniques such as similarity searching, pharmacophore matching, or molecular docking have been applied with the aim of finding hit compounds showing appreciable affinity. Molecular dynamics simulations in mixed solvents have been shown to identify hot spots relevant for protein-drug interaction, and implementations based on this knowledge were developed to improve pharmacophore matching of small molecules, binding free-energy estimations, and docking performance in terms of pose prediction. Here, we proved in a retrospective manner that cosolvent-derived pharmacophores from molecular dynamics (solvent sites) improve the performance of docking-based virtual screening campaigns. We applied a biased docking scheme based on solvent sites to nine relevant target proteins that have a set of known ligands or actives and compounds that are, presumably, nonbinders (decoys). Our results show improvement in virtual screening performance compared to traditional docking programs both at a global level, with up to 35% increase in areas under the receiver operating characteristic curve, and in early stages, with up to a 7-fold increase in enrichment factors at 1%. However, the improvement in pose prediction of actives was less profound. The presented application makes use of the AutoDock Bias method and is the only cosolvent-derived pharmacophore technique that employs its knowledge both in the ligand conformational search algorithm and the final affinity scoring for virtual screening purposes.

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.

SUMMARY: The performance of docking calculations can be improved by tuning parameters for the system of interest, e.g. biasing the results towards the formation of relevant protein-ligand interactions, such as known ligand pharmacophore or interaction sites derived from cosolvent molecular dynamics. AutoDock Bias is a straightforward and easy to use script-based method that allows the introduction of different types of user-defined biases for fine-tuning AutoDock4 docking calculations. AVAILABILITY AND IMPLEMENTATION: AutoDock Bias is distributed with MGLTools (since version 1.5.7), and freely available on the web at http://ccsb.scripps.edu/mgltools/ or http://autodockbias.wordpress.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Solvents to Fragments to Drugs: MD Applications in Drug Design.

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 µs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

WATCLUST: a tool for improving the design of drugs based on protein-water interactions.

MOTIVATION: Water molecules are key players for protein folding and function. On the protein surface, water is not placed randomly, but display instead a particular structure evidenced by the presence of specific water sites (WS). These WS can be derived and characterized using explicit water Molecular Dynamics simulations, providing useful information for ligand binding prediction and design. Here we present WATCLUST, a WS determination and analysis tool running on the VMD platform. The tool also allows direct transfer of the WS information to Autodock program to perform biased docking. AVAILABILITY AND IMPLEMENTATION: The WATCLUST plugin and documentation are freely available at http://sbg.qb.fcen.uba.ar/watclust/. CONTACT: marcelo@qi.fcen.uba.ar, adrian@qi.fcen.uba.ar.