Method Development: Efficient Computer Vision Based Algorithms

方法开发：基于高效计算机视觉的算法

基本信息

批准号：
7733033
负责人：
Ruth Nussinov
金额：
$ 16.83万
依托单位：
DIVISION OF BASIC SCIENCES - NCI
依托单位国家：
美国
项目类别：
财政年份：
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/7733033
关键词：
Address Algorithms Amino Acids Arts Binding Binding Sites Biological Caliber Catalysis Cells Characteristics Chemical Engineering Collection Computational Biology Computer Simulation Computer Vision Systems Core Protein Data Set Databases Decompression Sickness Detection Development Dimensions Docking Electrolytes Enzymes Goals Graph Integral Membrane Protein Internet Knowledge Libraries Life Ligands Link Location Maps Membrane Membrane Proteins Methodology Methods Modeling Molecular Motion Movement Nature Numbers Operative Surgical Procedures Organism Output Phage Display Physiological Processes Play Pliability Protein Binding Protein Fragment Proteins RNA RNA Binding Range Resources Robotics Role Score Series Side Site Site Visit Solutions Specific qualifier value Standards of Weights and Measures Structural Protein Structure Substrate Specificity Surface Techniques Time Update Validation Vertebral column base biological research combinatorial comparative computerized tools data management experience functional group globular protein instrument macromolecule method development molecular dynamics novel nucleic acid structure prevent programs protein folding protein structure radius bone structure research study small molecule tool

项目摘要

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. FlexProt is a novel technique for the alignment of flexible proteins. Unlike all previous algorithms to solve structural comparisons allowing hinge-bending motions, FlexProt does not require an a priori knowledge of the location of the hinge(s). FlexProt carries out the flexible alignment superimposing the matching rigid subpart pairs, and detects the flexible hinge regions simultaneously. Protein structural analysis requires algorithms that can deal with molecular flexibility. FlexProt efficiently detects maximal congruent rigid fragments in both molecules. Transforming the task into a graph theoretic problem, it calculates the optimal arrangement of previously detected maximal congruent rigid fragments. FlexProt performs a structural comparison of a pair of proteins 300 amino acids long in about seven seconds on a standard desktop PC. FlexProt can be accessed via the web at bioinfo3d.cs.tau.ac.il/FlexProt/. 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.

我们方法学的独特性来自将蛋白质结构视为3D空间中点（例如原子坐标或描述分子表面的点的集合）的集合，无视链上残基的顺序。这种计算机视觉和基于机器人技术的算法可以比较蛋白质表面，界面或蛋白质核，而不会受到顺序顺序的限制。自上次访问以来，我们在新算法的开发方面取得了重大进展。上面已经描述了其中的一些（对接和结合位点的比较和检测）。 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，可以对界面进行成对的结构比较；（Sitelight）;即使直径很小，也可以在高效物质中检测蛋白质中的通道和空腔。此外，使用这些，已经组装了两个蛋白质 - 蛋白质接口的非冗余数据集。这些方法都具有高效的，具有状态的功能。我已经讨论了对接方法，Site Engine和Mappis（蛋白质 - 蛋白质接口的多重比对）。在下面，我简要描述了Flexprot，质量，多个和Molaxis。大多数用于多个比对的方法从成对对齐解决方案开始。相比之下，质量和多个从输入分子的同时叠加得出的多个比对。此外，两种方法都不要求所有输入分子都参与对齐。实际上，他们有效地检测到输入中所有可能数量的分子的高分部分对齐。质量（二级结构的多重比对）和多个（多个蛋白质）是完全自动化的高效技术，可以检测蛋白质结构的多个结构比对，并检测输入分子之间的常见几何核。此外，这两种方法都是序列独立的。质量基于使用二级结构和原子表示的两级比对。利用二级结构信息有助于滤除嘈杂的解决方案并实现效率和鲁棒性。质量能够检测非人体学结构基序，其中二级结构在链上以不同的顺序排列。此外，质量不仅能够检测所有输入分子共享的结构基序，而且还可以检测出仅由分子子集共享的基序。我们已经证明了其处理数十个分子，检测非血管学基序并在输入的非预定子集中找到生物学上有意义的比对的能力。质量可从http://bioinfo3d.cs.tau.ac.il/mass/获得。 Multiprot认为蛋白质结构由空间中的点表示，其中这些点是C-Alpha坐标或侧链的C-Alpha和Atoms或几何中心。 Multiprot可在http://bioinfo3d.cs.tau.ac.il/multiprot/上获得。我们已经在一系列应用程序上说明了这两种方法的功能。订单独立性允许将多功能电机应用于结合位点和蛋白质 - 蛋白质界面，从而使Multiprot成为非常有用的结构工具。 Flexprot是一种用于对齐柔性蛋白质的新技术。与所有以前的算法求解允许铰链弯曲运动的结构比较的算法不同，FlexProt不需要先验了解铰链位置。 FlexProt执行柔性比对叠加匹配的刚性子部分对，并同时检测柔性铰链区域。蛋白质结构分析需要可以处理分子柔韧性的算法。 FlexProt有效地检测两个分子中最大的一致性刚性碎片。将任务转换为图理论问题，它计算了先前检测到的最大一致性刚性片段的最佳布置。 FlexProt在标准台式PC上大约七秒钟内长达七秒钟的一对蛋白质对一对蛋白质的结构比较。可以通过bioinfo3d.cs.tau.ac.il/flexprot/网络访问FlexProt。 Molaxis是一款可自由使用的，易于使用的Web服务器，用于识别将埋藏腔连接到大分子外部和蛋白质中的跨膜（TM）通道的通道。生物学通道对于跨膜和酶催化的生理过程至关重要，并且可以在底物特异性中发挥作用。由于大分子中通道识别的重要性，我们开发了Molaxis服务器。 Molaxis实现了最先进的，准确的计算几何技术，从而减少了通道发现问题的尺寸，从而使该算法非常有效。给定PDB格式中的蛋白质或核酸结构，服务器输出所有可能将埋入腔与蛋白质外部或指向TM蛋白中的主要通道的通道。对于每个通道，还给出了门控残基和最狭窄的半径，称为“瓶颈”，并在3D图形表示中列出衬里残基和通道表面的完整列表。用户可以操纵高级参数并根据其需求指导频道搜索。 Molaxis可作为Web服务器或独立程序提供，网址为http://bioinfo3d.cs.tau.ac.il/molaxis。此外，我们一直在开发使用结构比较技术来识别RNA未定义的三级结构的方法。我们将其应用于当前可用的RNA诱导液（NMR和晶体）的整个数据库中，以得出簇的非冗余数据集或RNA第三级结构；并鉴定RNA结合位点在蛋白质表面上的单个链RNA中挤出的RNA碱基的结合位点。