Adaptive GPU-accelerated force calculation for interactive rigid molecular docking using haptics

Molecular docking systems model and simulate in silico the interactions of intermolecular binding. Haptics-assisted docking enables the user to interact with the simulation via their sense of touch but a stringent time constraint on the computation of forces is imposed due to the sensitivity of the human haptic system. To simulate high fidelity smooth and stable feedback the haptic feedback loop should run at rates of 500 Hz to 1 kHz. We present an adaptive force calculation approach that can be executed in parallel on a wide range of Graphics Processing Units (GPUs) for interactive haptics-assisted docking with wider applicability to molecular simulations. Prior to the interactive session either a regular grid or an octree is selected according to the available GPU memory to determine the set of interatomic interactions within a cutoff distance. The total force is then calculated from this set. The approach can achieve force updates in less than 2 ms for molecular structures comprising hundreds of thousands of atoms each, with performance improvements of up to 90 times the speed of current CPU-based force calculation approaches used in interactive docking. Furthermore, it overcomes several computational limitations of previous approaches such as pre-computed force grids, and could potentially be used to model receptor flexibility at haptic refresh rates

Chemical structure matching using correlation matrix memories

This paper describes the application of the Relaxation By Elimination (RBE) method to matching the 3D structure of molecules in chemical databases within the frame work of binary correlation matrix memories. The paper illustrates that, when combined with distributed representations, the method maps well onto these networks, allowing high performance implementation in parallel systems. It outlines the motivation, the neural architecture, the RBE method and presents some results of matching small molecules against a database of 100,000 models

Computation of protein geometry and its applications: Packing and function prediction

This chapter discusses geometric models of biomolecules and geometric constructs, including the union of ball model, the weigthed Voronoi diagram, the weighted Delaunay triangulation, and the alpha shapes. These geometric constructs enable fast and analytical computaton of shapes of biomoleculres (including features such as voids and pockets) and metric properties (such as area and volume). The algorithms of Delaunay triangulation, computation of voids and pockets, as well volume/area computation are also described. In addition, applications in packing analysis of protein structures and protein function prediction are also discussed.Comment: 32 pages, 9 figure

Simulating molecular docking with haptics

Intermolecular binding underlies various metabolic and regulatory processes of the cell, and the therapeutic and pharmacological properties of drugs. Molecular docking systems model and simulate these interactions in silico and allow the study of the binding process. In molecular docking, haptics enables the user to sense the interaction forces and intervene cognitively in the docking process. Haptics-assisted docking systems provide an immersive virtual docking environment where the user can interact with the molecules, feel the interaction forces using their sense of touch, identify visually the binding site, and guide the molecules to their binding pose. Despite a forty-year research e�ort however, the docking community has been slow to adopt this technology. Proprietary, unreleased software, expensive haptic hardware and limits on processing power are the main reasons for this. Another signi�cant factor is the size of the molecules simulated, limited to small molecules. The focus of the research described in this thesis is the development of an interactive haptics-assisted docking application that addresses the above issues, and enables the rigid docking of very large biomolecules and the study of the underlying interactions. Novel methods for computing the interaction forces of binding on the CPU and GPU, in real-time, have been developed. The force calculation methods proposed here overcome several computational limitations of previous approaches, such as precomputed force grids, and could potentially be used to model molecular exibility at haptic refresh rates. Methods for force scaling, multipoint collision response, and haptic navigation are also reported that address newfound issues, particular to the interactive docking of large systems, e.g. force stability at molecular collision. The i ii result is a haptics-assisted docking application, Haptimol RD, that runs on relatively inexpensive consumer level hardware, (i.e. there is no need for specialized/proprietary hardware)

Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites

<p>Abstract</p> <p>Background</p> <p>Protein surfaces comprise only a fraction of the total residues but are the most conserved functional features of proteins. Surfaces performing identical functions are found in proteins absent of any sequence or fold similarity. While biochemical activity can be attributed to a few key residues, the broader surrounding environment plays an equally important role.</p> <p>Results</p> <p>We describe a methodology that attempts to optimize two components, global shape and local physicochemical texture, for evaluating the similarity between a pair of surfaces. Surface shape similarity is assessed using a three-dimensional object recognition algorithm and physicochemical texture similarity is assessed through a spatial alignment of conserved residues between the surfaces. The comparisons are used in tandem to efficiently search the Global Protein Surface Survey (GPSS), a library of annotated surfaces derived from structures in the PDB, for studying evolutionary relationships and uncovering novel similarities between proteins.</p> <p>Conclusion</p> <p>We provide an assessment of our method using library retrieval experiments for identifying functionally homologous surfaces binding different ligands, functionally diverse surfaces binding the same ligand, and binding surfaces of ubiquitous and conformationally flexible ligands. Results using surface similarity to predict function for proteins of unknown function are reported. Additionally, an automated analysis of the ATP binding surface landscape is presented to provide insight into the correlation between surface similarity and function for structures in the PDB and for the subset of protein kinases.</p

3D-PP: A tool for discovering conserved three-dimensional protein patterns

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.Peer ReviewedPostprint (published version

Complex networks theory for analyzing metabolic networks

One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects