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Gahlawat A, Singh A, Sandhu H, Garg P. CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm. J Cheminform 2024; 16:12. [PMID: 38291536 PMCID: PMC10829215 DOI: 10.1186/s13321-024-00803-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/13/2024] [Indexed: 02/01/2024] Open
Abstract
Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.
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Affiliation(s)
- Anuj Gahlawat
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160062, Punjab, India
| | - Anjali Singh
- Department of Computer Science, Kurukshetra University, Kurukshetra, Haryana, India
| | - Hardeep Sandhu
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160062, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160062, Punjab, India.
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Kim D, Lee M, Cho Y, Kim DS. Beta-complex versus Alpha-complex: Similarities and Dissimilarities. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:1686-1701. [PMID: 30334798 DOI: 10.1109/tvcg.2018.2873633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The beta-complex is a construct derived from the Voronoi diagram of spherical balls of arbitrary radii and has proven a powerful capability for proximity reasoning among spherical balls in three-dimensional space. Important applications related to molecular shapes in structural/computational molecular biology have been correctly, efficiently, and conveniently solved in the unified framework of the beta-complex and the Voronoi diagram. The beta-complex is a generalization of the ordinary alpha-complex. However, there are similarities and dissimilarities between the two complexes and it is necessary to correctly understand these similarities and dissimilarities to choose the right complex to solve application problems at hand. This paper presents the similarities and dissimilarities between these constructs and illustrates the consequence of the dissimilarity in application problems from both theoretical and practical points of view using examples of atomic arrangements.
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Kawabata T. Detection of cave pockets in large molecules: Spaces into which internal probes can enter, but external probes from outside cannot. Biophys Physicobiol 2019; 16:391-406. [PMID: 31984193 PMCID: PMC6975925 DOI: 10.2142/biophysico.16.0_391] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 08/27/2019] [Indexed: 12/01/2022] Open
Abstract
Geometric features of macromolecular shapes are important for binding with other molecules. Kawabata, T. and Go, N. (2007) defined a pocket as a space into which a small probe can enter, but a large probe cannot. In 2010, mathematical morphology (MM) was introduced to provide a more rigorous definition, and the program GHECOM was developed using the grid-based representation of molecules. This method was simple, but effective in finding the binding sites of small compounds on protein surfaces. Recently, many 3D structures of large macromolecules have been determined to contain large internal hollow spaces. Identification and size estimation of these spaces is important for characterizing their function and stability. Therefore, we employ the MM definition of pocket proposed by Manak, M. (2019)—a space into which an internal probe can enter, but an external probe cannot enter from outside of the macromolecules. This type of space is called a “cave pocket”, and is identified through molecular grid-representation. We define a “cavity” as a space into which a probe can enter, but cannot escape to the outside. Three types of spaces: cavity, pocket, and cave pocket were compared both theoretically and numerically. We proved that a cave pocket includes a pocket, and it is equal to a pocket if no cavity is found. We compared the three types of spaces for a variety of molecules with different-sized spherical probes; cave pockets were more sensitive than pockets for finding almost closed internal holes, allowing for more detailed representations of internal surfaces than cavities provide.
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Affiliation(s)
- Takeshi Kawabata
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
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Simões T, Lopes D, Dias S, Fernandes F, Pereira J, Jorge J, Bajaj C, Gomes A. Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2017; 36:643-683. [PMID: 29520122 PMCID: PMC5839519 DOI: 10.1111/cgf.13158] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.
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Affiliation(s)
- Tiago Simões
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - Sérgio Dias
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - João Pereira
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Joaquim Jorge
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | | | - Abel Gomes
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
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Liu H, Chen Q. Computational protein design for given backbone: recent progresses in general method-related aspects. Curr Opin Struct Biol 2016; 39:89-95. [PMID: 27348345 DOI: 10.1016/j.sbi.2016.06.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 05/18/2016] [Accepted: 06/15/2016] [Indexed: 10/21/2022]
Abstract
To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.
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Affiliation(s)
- Haiyan Liu
- School of Life Sciences, University of Science and Technology of China, China; Hefei National Laboratory for Physical Sciences at the Microscales, China; Collaborative Innovation Center of Chemistry for Life Sciences, Hefei, Anhui 230027, China; Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
| | - Quan Chen
- School of Life Sciences, University of Science and Technology of China, China
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Pavelka A, Sebestova E, Kozlikova B, Brezovsky J, Sochor J, Damborsky J. CAVER: Algorithms for Analyzing Dynamics of Tunnels in Macromolecules. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:505-517. [PMID: 27295634 DOI: 10.1109/tcbb.2015.2459680] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The biological function of a macromolecule often requires that a small molecule or ion is transported through its structure. The transport pathway often leads through void spaces in the structure. The properties of transport pathways change significantly in time; therefore, the analysis of a trajectory from molecular dynamics rather than of a single static structure is needed for understanding the function of pathways. The identification and analysis of transport pathways are challenging because of the high complexity and diversity of macromolecular shapes, the thermal motion of their atoms, and the large amount of conformations needed to properly describe conformational space of protein structure. In this paper, we describe the principles of the CAVER 3.0 algorithms for the identification and analysis of properties of transport pathways both in static and dynamic structures. Moreover, we introduce the improved clustering solution for finding tunnels in macromolecules, which is included in the latest CAVER 3.02 version. Voronoi diagrams are used to identify potential pathways in each snapshot of a molecular dynamics trajectory and clustering is then used to find the correspondence between tunnels from different snapshots. Furthermore, the geometrical properties of pathways and their evolution in time are computed and visualized.
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Kim JK, Cho Y, Lee M, Laskowski RA, Ryu SE, Sugihara K, Kim DS. BetaCavityWeb: a webserver for molecular voids and channels. Nucleic Acids Res 2015; 43:W413-8. [PMID: 25904629 PMCID: PMC4489219 DOI: 10.1093/nar/gkv360] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 04/07/2015] [Indexed: 11/24/2022] Open
Abstract
Molecular cavities, which include voids and channels, are critical for molecular function. We present a webserver, BetaCavityWeb, which computes these cavities for a given molecular structure and a given spherical probe, and reports their geometrical properties: volume, boundary area, buried area, etc. The server's algorithms are based on the Voronoi diagram of atoms and its derivative construct: the beta-complex. The correctness of the computed result and computational efficiency are both mathematically guaranteed. BetaCavityWeb is freely accessible at the Voronoi Diagram Research Center (VDRC) (http://voronoi.hanyang.ac.kr/betacavityweb).
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Affiliation(s)
- Jae-Kwan Kim
- Vorononi Diagram Research Center, Hanyang University, Korea
| | - Youngsong Cho
- Vorononi Diagram Research Center, Hanyang University, Korea
| | - Mokwon Lee
- School of Mechanical Engineering, Hanyang University, Korea
| | | | - Seong Eon Ryu
- Department of Bioengineering, Hanyang University, Korea
| | - Kokichi Sugihara
- Meiji Institute for Advanced Study of Mathematical Sciences, Tokyo, Japan
| | - Deok-Soo Kim
- Vorononi Diagram Research Center, Hanyang University, Korea School of Mechanical Engineering, Hanyang University, Korea
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Kim JK, Won CI, Cha J, Lee K, Kim DS. Optimal ligand descriptor for pocket recognition based on the Beta-shape. PLoS One 2015; 10:e0122787. [PMID: 25835497 PMCID: PMC4383629 DOI: 10.1371/journal.pone.0122787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 02/17/2015] [Indexed: 12/20/2022] Open
Abstract
Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.
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Affiliation(s)
- Jae-Kwan Kim
- Voronoi Diagram Research Center, Hanyang University, Seoul, Korea
| | - Chung-In Won
- Voronoi Diagram Research Center, Hanyang University, Seoul, Korea
| | - Jehyun Cha
- School of Mechanical Engineering, Hanyang University, Seoul, Korea
| | - Kichun Lee
- Department of Industrial Engineering, Hanyang University, Seoul, Korea
| | - Deok-Soo Kim
- Voronoi Diagram Research Center, Hanyang University, Seoul, Korea
- School of Mechanical Engineering, Hanyang University, Seoul, Korea
- * E-mail:
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