Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation

Fragment-based drug discovery using NMR and x-ray crystallographic methods has proven utility but also non-trivial time, materials, and labor costs. Current computational fragment-based approaches circumvent these issues but suffer from limited representations of protein flexibility and solvation effects, leading to difficulties with rigorous ranking of fragment affinities. To overcome these limitations we describe an explicit solvent all-atom molecular dynamics methodology (SILCS: Site Identification by Ligand Competitive Saturation) that uses small aliphatic and aromatic molecules plus water molecules to map the affinity pattern of a protein for hydrophobic groups, aromatic groups, hydrogen bond donors, and hydrogen bond acceptors. By simultaneously incorporating ligands representative of all these functionalities, the method is an in silico free energy-based competition assay that generates three-dimensional probability maps of fragment binding (FragMaps) indicating favorable fragment∶protein interactions. Applied to the two-fold symmetric oncoprotein BCL-6, the SILCS method yields two-fold symmetric FragMaps that recapitulate the crystallographic binding modes of the SMRT and BCOR peptides. These FragMaps account both for important sequence and structure differences in the C-terminal halves of the two peptides and also the high mobility of the BCL-6 His116 sidechain in the peptide-binding groove. Such SILCS FragMaps can be used to qualitatively inform the design of small-molecule inhibitors or as scoring grids for high-throughput in silico docking that incorporate both an atomic-level description of solvation and protein flexibility

Molecular Dynamics Analysis of Apolipoprotein-D - Lipid Hydroperoxide Interactions: Mechanism for Selective Oxidation of Met-93

Background: Recent studies suggest reduction of radical-propagating fatty acid hydroperoxides to inert hydroxides by interaction with apolipoprotein-D (apoD) Met93 may represent an antioxidant function for apoD. The nature and structural consequences of this selective interaction are unknown. Methodology/Principal Findings: Herein we used molecular dynamics (MD) analysis to address these issues. Longtimescale simulations of apoD suggest lipid molecules are bound flexibly, with the molecules free to explore multiple conformations in a binding site at the entrance to the classical lipocalin ligand-binding pocket. Models of 5s- 12s- and 15s hydroperoxyeicosatetraenoic acids were created and the lipids found to wrap around Met93 thus providing a plausible mechanism by which eicosatetraenoic acids bearing hydroperoxides on different carbon atoms can interact with Met93 to yield Met93 sulfoxide (Met93SO). Simulations of glycosylated apoD indicated that a second solvent exposed Met at position 49 was shielded by a triantennerary N-glycan attached to Asn45 thereby precluding lipid interactions. MD simulations of apoD showed B-factors of the loop containing Met93SO were higher in the oxidized protein, indicating increased flexibility that is predicted to destabilize the protein and promote self-association. Conclusions/Significance: These studies provide novel insights into the mechanisms that may contribute to the antioxidant function of apoD and the structural consequences that result if Met93SO is not redox-cycled back to its native state

Free energy of binding of coiled-coil complexes with different electrostatic environments: the influence of force field polarisation and capping

Coiled-coils are well known protein–protein interaction motifs, with the leucine zipper region of activator protein-1 (AP-1) consisting of the c-Jun and c-Fos proteins being a typical example. Molecular dynamics (MD) simulations using the MM/GBSA method have been used to predict the free energy of interaction of these proteins. The influence of force field polarisation and capping on the predicted free energy of binding of complexes with different electrostatic environments (net charge) were investigated. Although both force field polarisation and peptide capping are important for the prediction of the absolute free energy of binding, peptide capping has the largest influence on the predicted free energy of binding. Polarisable simulations appear better suited to determine structural properties of the complexes of these proteins while non-polarisable simulations seem to give better predictions of the associated free energies of bindin

Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening

Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range

The Free Energy Landscape of Small Molecule Unbinding

The spontaneous dissociation of six small ligands from the active site of FKBP (the FK506 binding protein) is investigated by explicit water molecular dynamics simulations and network analysis. The ligands have between four (dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms, and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each ligand) are grouped according to a set of intermolecular distances into nodes of a network, and the direct transitions between them are the links. The network analysis reveals that the bound state consists of several subbasins, i.e., binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts. The dissociation kinetics show a simple (i.e., single-exponential) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state. The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site, which correspond to multiple pathways of dissociation. For the six small ligands of FKBP, the weaker the binding affinity the closer to the bound state (along the intermolecular distance) are the transition state structures, which is a new manifestation of Hammond behavior. Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution. Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape (both thermodynamics and kinetics) of ligand unbinding

SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

Modeling of negative Poisson’s ratio (auxetic) crystalline cellulose Iβ

Energy minimizations for unstretched and stretched cellulose models using an all-atom empirical force field (Molecular Mechanics) have been performed to investigate the mechanism for auxetic (negative Poisson’s ratio) response in crystalline cellulose Iβ from kraft cooked Norway spruce. An initial investigation to identify an appropriate force field led to a study of the structure and elastic constants from models employing the CVFF force field. Negative values of on-axis Poisson’s ratios nu31 and nu13 in the x1-x3 plane containing the chain direction (x3) were realized in energy minimizations employing a stress perpendicular to the hydrogen-bonded cellobiose sheets to simulate swelling in this direction due to the kraft cooking process. Energy minimizations of structural evolution due to stretching along the x3 chain direction of the ‘swollen’ (kraft cooked) model identified chain rotation about the chain axis combined with inextensible secondary bonds as the most likely mechanism for auxetic response

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed