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/).

我们方法的独特性源于将蛋白质结构视为 3D 空间中点的集合（例如原子坐标或描述分子表面的点），而忽略链上残基的顺序。这种基于计算机视觉和机器人技术的算法能够比较蛋白质表面、界面或蛋白质核心，而不受顺序的限制。自上次现场访问以来，我们在新算法的开发方面取得了实质性进展。其中一些（对接、结合位点比较和检测）已在上面描述。列举自上次现场访问以来我们开发的方法：基于残基的多重蛋白质结构比较（MultiProt；蛋白质二级结构表示的多重比对（MASS）；功能组表示中蛋白质结构及其结合位点的多重比对（MultiBind）以及蛋白质-蛋白质界面的多重比对（MAPPIS）；SiteEngine，它进行小分子和蛋白质结合位点识别和 I2ISiteEngine，对接口进行两两结构比较；蛋白质结构的灵活对齐（FlexProt；刚体对接（PatchDock）；灵活铰链弯曲对接（FlexDock）；对称对接（SymmDock；用于折叠和多分子组装的组合对接（CombDock）；使用噬菌体展示库预测结合位点 （站点灯）； 和 MolAxis 可以高效地检测蛋白质中的通道和空腔，即使它们的直径非常小。此外，使用这些，组装了两个非冗余的蛋白质-蛋白质界面数据集。这些方法都非常高效，具有最先进的功能。我已经讨论过对接方法，SiteEngine和MAPPIS（蛋白质-蛋白质的多重比对） 接口S）。下面我简单介绍一下FlexProt、MASS、MultiProt和MolAxis。大多数多重比对方法都是从成对比对解决方案开始的。相比之下，MASS 和 MultiProt 从输入分子的同时叠加中得出多重排列。此外，两种方法都不要求所有输入分子都参与比对。事实上，他们有效地检测到所有的高分部分多重比对 输入中可能的分子数。 MASS（二级结构多重比对）和 MultiProt（多重蛋白质）是全自动高效技术，可检测蛋白质结构的多重结构比对并检测输入分子之间的共同几何核心。此外，这两种方法都是序列顺序无关的。 MASS 基于二级对齐，同时使用二级结构和原子表示。 利用二级结构信息有助于过滤掉噪声解决方案并实现效率和鲁棒性。 MASS 能够检测非拓扑结构基序，其中二级结构在链上以不同的顺序排列。此外，MASS 不仅能够检测所有输入分子共享的结构基序，还能够检测仅分子子集共享的基序。我们已经证明了其处理订单的能力 数十个分子，检测非拓扑基序并在输入的非预定义子集中找到具有生物学意义的比对。 MASS 可在 http://bioinfo3d.cs.tau.ac.il/MASS/ 获取。 MultiProt 将蛋白质结构视为由空间中的点表示，其中这些点要么是 C-alpha 坐标，要么是 C-alpha 和 侧链的原子或几何中心。 MultiProt 可从 http://bioinfo3d.cs.tau.ac.il/MultiProt/ 获取。我们已经在一系列应用中展示了这两种方法的强大功能。顺序无关性使得 MultiProt 能够应用于结合位点和蛋白质-蛋白质界面，使 MultiProt 成为非常有用的结构工具。 MolAxis 是一个免费提供、易于使用的 网络服务器，用于识别连接埋藏腔与大分子外部的通道以及蛋白质中的跨膜 (TM) 通道。生物通道对于电解质和代谢物跨膜运输以及酶催化等生理过程至关重要，并且可以在底物特异性中发挥作用。受大分子通道识别重要性的启发，我们开发了 MolAxis 服务器。 MolAxis 采用最先进、精确的计算几何技术，减少了通道查找问题的维度，使算法极其高效。给定 PDB 格式的蛋白质或核酸结构，服务器输出将埋藏腔连接到蛋白质外部或指向 TM 蛋白质中的主通道的所有可能通道。对于每个通道，门控残基和最窄半径称为 还以 3D 图形表示形式给出了“瓶颈”以及衬里残留物和通道表面的完整列表。用户可以根据需要操纵高级参数并直接进行频道搜索。 MolAxis 可作为 Web 服务器或独立程序使用，网址为 http://bioinfo3d.cs.tau.ac.il/MolAxis。此外，我们一直在开发方法来识别未预定义的 使用结构比较技术分析 RNA 的三级结构。我们将其应用于当前可用的 RNA 结构（NMR 和晶体）的整个数据库，以导出聚集的非冗余数据集或 RNA 三级结构；并鉴定蛋白质表面上单链 RNA 中挤出的 RNA 碱基的 RNA 结合位点。通道和空腔在大分子功能中发挥着重要作用，作为 底物/产物、辅因子和药物结合、催化位点和配体/蛋白质的进入/退出路径。此外，跨膜（TM）蛋白形成的通道充当转运蛋白和离子通道。 MolAxis 是一种灵敏、快速的新型工具，用于识别和分类大分子中各种尺寸和形状的通道和空腔。 MolAxis 建造走廊， 是代表小分子通过通道的可能路线的路径。分子的外中轴是具有多个最接近原子的点的集合。它由二维表面斑块组成，可以看作分子补体的骨架。我们在 MolAxis 中实现了一种新颖的算法，该算法使用最先进的计算几何技术来近似和扫描有用的 外中轴的子集，从而减少问题的维度，从而使算法极其高效。 MolAxis 旨在识别连接埋藏腔与大分子外部的通道，并识别蛋白质中的 TM 通道。我们将 MolAxis 应用于酶腔和 TM 蛋白。我们进一步利用 MolAxis 来监测沿分子动力学轨迹的通道尺寸 人类细胞色素 P450。 MolAxis 以皮秒时间尺度间隔构建高质量的快照走廊，证实了 2e 基质访问通道中的门控机制。 我们将我们的结果与以前的工具在准确性、性能和找到所需路径的基础理论保证方面进行比较。 MolAxis 可作为网络服务器和易于使用的独立程序在线使用 （http://bioinfo3d.cs.tau.ac.il/MolAxis/）。