Method Development: Efficient Computer Vision Based Algorithms

方法开发：基于高效计算机视觉的算法

• 批准号: 7965320
• 负责人: Ruth Nussinov
• 金额: $ 13.03万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7965320
• 关键词: Address; Algorithms; Arts; Binding; Binding Sites; Biological; Caliber; Catalysis; Catalytic Domain; Cells; Characteristics; Chemical Engineering; Classification; Collection; Complement; Computational Biology; Computer Simulation; Computer Vision Systems; Core Protein; Cytochrome P450; Data Set; Databases; Detection; Development; Dimensions; Docking; Electrolytes; Enzymes; Goals; Human; Integral Membrane Protein; Internet; Ion Channel; Libraries; Life; Ligands; Link; Location; Maps; Medial; Membrane; Membrane Proteins; Methodology; Methods; Modeling; Molecular; Monitor; Movement; Nature; Operative Surgical Procedures; Organism; Output; Pathway interactions; Performance; Phage Display; Pharmaceutical Preparations; Physiological Processes; Play; Protein Binding; Protein Fragment; Proteins; RNA; RNA Binding; Radial; Resources; Robotics; Role; Route; Scanning; Series; Shapes; Side; Site; Site Visit; Skeleton; Solutions; Specific qualifier value; Structure; Substrate Specificity; Surface; Techniques; Time; Update; Validation; Vertebral column; base; biological research; cofactor; combinatorial; comparative; computerized tools; data management; design; experience; flexibility; functional group; globular protein; instrument; macromolecule; method development; molecular dynamics; novel; nucleic acid structure; prevent; programs; protein folding; protein structure; research study; small molecule; tool; two-dimensional; web site

The uniqueness of our methodologies derives from viewing protein structures as collections of points (e.g., atom coordinates, or points describing molecular surfaces) in 3D space, disregarding the order of the residues on the chains. Such computer-vision and robotics-based algorithms enable comparisons of protein surfaces, interfaces, or protein cores without being limited by the sequential order. Since the last site visit, we have made substantial progress in the development of new algorithms. Some of these (docking, and binding site comparison and detection) have already been described above. To enumerate the methods we have developed since the last site visit: residue-based multiple protein structure comparison (MultiProt; multiple alignment of proteins in their secondary structure representation (MASS); multiple alignment of protein structures in the functional group representation and of their binding sites (MultiBind), and of protein-protein interfaces (MAPPIS); SiteEngine, which carries out small molecule and protein-binding site recognition and I2ISiteEngine, which carries out pairwise structural comparisons of interfaces; flexible alignment of protein structures (FlexProt; Rigid body docking (PatchDock); Flexible hinge-bending docking (FlexDock); Symmetry docking (SymmDock; Combinatorial docking for folding and multimolecular assembly (CombDock); Prediction of binding sites using phage display libraries (SiteLight); and MolAxis to detect channels and cavities in proteins in a highly efficient matter even if the diameter of these is very small. In addition, using these, two nonredundant datasets of protein-protein interfaces have been assembled. The methods are all highly efficient with state of-the-art capabilities. I have already discussed the docking methods, SiteEngine and MAPPIS (Multiple Alignment of Protein-Protein InterfaceS). Below I briefly describe FlexProt, MASS, MultiProt and MolAxis. Most methods for multiple alignment start from the pairwise alignment solutions. In contrast, MASS and MultiProt derive multiple alignments from simultaneous superpositions of input molecules. Further, both methods do not require that all input molecules participate in the alignment. Actually, they efficiently detect high scoring partial multiple alignments for all possible number of molecules in the input. MASS (Multiple Alignment by Secondary Structures) and MultiProt (Multiple Proteins) are fully automated highly efficient techniques to detect multiple structural alignments of protein structures and detect common geometrical cores between input molecules. Furthermore, both methods are sequence-order independent. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. MASS is capable of detecting nontopological structural motifs, where the secondary structures are arranged in a different order on the chains. Further, MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. We have demonstrated its ability to handle on the order of tens of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. MASS is available at http://bioinfo3d.cs.tau.ac.il/MASS/. MultiProt considers protein structures as represented by points in space, where the points are either the C-alpha coordinates or the C-alpha and atoms or geometric center of the side chain. MultiProt is available at http://bioinfo3d.cs.tau.ac.il/MultiProt/. We have illustrated the power of both methods on a range of applications. The order-independence allows application of MultiProt to binding sites and protein-protein interfaces, making MultiProt an extremely useful structural tool. MolAxis is a freely available, easy-to-use web server for identification of channels that connect buried cavities to the outside of macromolecules and for transmembrane (TM) channels in proteins. Biological channels are essential for physiological processes such as electrolyte and metabolite transport across membranes and enzyme catalysis, and can play a role in substrate specificity. Motivated by the importance of channel identification in macromolecules, we developed the MolAxis server. MolAxis implements state-of-the-art, accurate computational-geometry techniques that reduce the dimensions of the channel finding problem, rendering the algorithm extremely efficient. Given a protein or nucleic acid structure in the PDB format, the server outputs all possible channels that connect buried cavities to the outside of the protein or points to the main channel in TM proteins. For each channel, the gating residues and the narrowest radius termed 'bottleneck' are also given along with a full list of the lining residues and the channel surface in a 3D graphical representation. The users can manipulate advanced parameters and direct the channel search according to their needs. MolAxis is available as a web server or as a stand-alone program at http://bioinfo3d.cs.tau.ac.il/MolAxis. In addition, we have been developing methods to identify unpredefined tertiary structure of RNA using structural comparison techniques. We are applying it to the entire database of currently available RNA strucures (NMR and crystal) to derive a clustered nonredundant dataset or RNA tertiary structures; and to identify RNA binding sites on protein surfaces for extruded RNA bases in single stranded RNA. Channels and cavities play important roles in macromolecular functions, serving as access/exit routes for substrates/products, cofactor and drug binding, catalytic sites, and ligand/protein. In addition, channels formed by transmembrane (TM) proteins serve as transporters and ion channels. MolAxis is a new sensitive and fast tool for the identification and classification of channels and cavities of various sizes and shapes in macromolecules. MolAxis constructs corridors, which are pathways that represent probable routes taken by small molecules passing through channels. The outer medial axis of the molecule is the collection of points that have more than one closest atom. It is composed of two-dimensional surface patches and can be seen as a skeleton of the complement of the molecule. We have implemented in MolAxis a novel algorithm that uses state-of-the-art computational geometry techniques to approximate and scan a useful subset of the outer medial axis, thereby reducing the dimension of the problem and consequently rendering the algorithm extremely efficient. MolAxis is designed to identify channels that connect buried cavities to the outside of macromolecules and to identify TM channels in proteins. We apply MolAxis to enzyme cavities and TM proteins. We further utilize MolAxis to monitor channel dimensions along Molecular Dynamics trajectories of a human Cytochrome P450. MolAxis constructs high quality corridors for snapshots at picosecond time-scale intervals substantiating the gating mechanism in the 2e substrate access channel. We compare our results with previous tools in terms of accuracy, performance and underlying theoretical guarantees of finding the desired pathways. MolAxis is available on line as a web-server and as a stand alone easy-to-use program (http://bioinfo3d.cs.tau.ac.il/MolAxis/).