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Martí-Centelles V, Piskorz TK, Duarte F. CageCavityCalc ( C3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages. J Chem Inf Model 2024. [PMID: 38980812 DOI: 10.1021/acs.jcim.4c00355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Organic(porous) and metal-organic cages are promising biomimetic platforms with diverse applications spanning recognition, sensing, and catalysis. The key to the emergence of these functions is the presence of well-defined inner cavities capable of binding a wide range of guest molecules and modulating their properties. However, despite the myriad cage architectures currently available, the rational design of structurally diverse and functional cages with specific host-guest properties remains challenging. Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce CageCavityCalc (C3), a Python-based tool for calculating the cavity size of molecular cages. The code is available on GitHub at https://github.com/VicenteMartiCentelles/CageCavityCalc. C3 utilizes a novel algorithm that enables the rapid calculation of cavity sizes for a wide range of molecular structures and porous systems. Moreover, C3 facilitates easy visualization of the computed cavity size alongside hydrophobic and electrostatic potentials, providing insights into host-guest interactions within the cage. Furthermore, the calculated cavity can be visualized using widely available visualization software, such as PyMol, VMD, or ChimeraX. To enhance user accessibility, a PyMol plugin has been created, allowing nonspecialists to use this tool without requiring computer programming expertise. We anticipate that the deployment of this computational tool will significantly streamline cage cavity calculations, thereby accelerating the discovery of functional cages.
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Affiliation(s)
- Vicente Martí-Centelles
- Instituto Interuniversitario de Investigación de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Universitat Politècnica de València, Universitat de València, Camino de Vera s/n, Valencia 46022, Spain
- CIBER de Bioingeniería Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid 28029, Spain
- Departamento de Química, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | - Tomasz K Piskorz
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, U.K
| | - Fernanda Duarte
- Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, U.K
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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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Guerra JVDS, Ribeiro-Filho HV, Jara GE, Bortot LO, Pereira JGDC, Lopes-de-Oliveira PS. pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science. BMC Bioinformatics 2021; 22:607. [PMID: 34930115 PMCID: PMC8685811 DOI: 10.1186/s12859-021-04519-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/07/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. RESULTS pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder's capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. CONCLUSIONS We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.
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Affiliation(s)
- João Victor da Silva Guerra
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil. .,Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, SP, Brazil.
| | - Helder Veras Ribeiro-Filho
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil
| | - Gabriel Ernesto Jara
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil
| | - Leandro Oliveira Bortot
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil
| | - José Geraldo de Carvalho Pereira
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil
| | - Paulo Sérgio Lopes-de-Oliveira
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Biosciences National Laboratory (LNBio), R. Giuseppe Máximo Scolfaro, 10000 - Bosque das Palmeiras, Campinas, SP, 13083-100, Brazil. .,Graduate Program in Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, University of Campinas, Campinas, SP, Brazil.
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Feng L, Wang F, Zhang J, Tang Y, Zhao J, Zhou L, Wang J, Guo D, Singh AK. Particle-based calculation and visualization of protein cavities using SES models. IEEE J Biomed Health Inform 2021; 26:2447-2457. [PMID: 34843433 DOI: 10.1109/jbhi.2021.3130897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The analysis of molecular cavities, where ligands interact with protein structures, plays a critical role in protein structure-based drug design. However, it is a challenge because of the ambiguous definition of the cavity boundaries in most cavity detection methods. The cavities are mostly calculated by input parameters, which are difficult for users to visualize cavities in interactive ways. In this paper, we propose a novel method for the interactive exploration of cavity calculation and visualization. Firstly, the proposed method combines the two solvent-excluded surfaces (SES) models of a given protein to define the boundaries and provides cavity emission points. Secondly, the system provides a user-guided interactive method to allow users to select cavities by simply clicking operations and to track the cavity identify and filling process based on position constraints. Finally, the selected cavities are represented with the colorful depth perception method. Experiments show that our work can effectively identify and calculate cavities.
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 2021; 35:737-750. [PMID: 34050420 DOI: 10.1007/s10822-021-00390-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
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Ricci M, Roscioni OM, Querciagrossa L, Zannoni C. MOLC. A reversible coarse grained approach using anisotropic beads for the modelling of organic functional materials. Phys Chem Chem Phys 2019; 21:26195-26211. [PMID: 31755499 DOI: 10.1039/c9cp04120f] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
We describe the development and implementation of a coarse grained (CG) modelling approach where complex organic molecules, and particularly the π-conjugated ones often employed in organic electronics, are modelled in terms of connected sets of attractive-repulsive biaxial Gay-Berne ellipsoidal beads. The CG model is aimed at reproducing realistically large scale morphologies (e.g. up to 100 nm thick films) for the materials involved, while being able to generate, with a back-mapping procedure, atomistic coordinates suitable, with limited effort, to be applied for charge transport calculations. Detailed methodology and an application to the common hole transporter material α-NPD are provided.
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Affiliation(s)
- Matteo Ricci
- Dipartimento di Chimica Industriale "Toso Montanari" and INSTM, Università di Bologna, Viale Risorgimento 4, IT-40136 Bologna, Italy.
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Chen Z, Zhang X, Peng C, Wang J, Xu Z, Chen K, Shi J, Zhu W. D3Pockets: A Method and Web Server for Systematic Analysis of Protein Pocket Dynamics. J Chem Inf Model 2019; 59:3353-3358. [DOI: 10.1021/acs.jcim.9b00332] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Zhaoqiang Chen
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xinben Zhang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Cheng Peng
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Jinan Wang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Kaixian Chen
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao 266237, China
| | - Jiye Shi
- UCB Biopharma SPRL, Chemin du Foriest, Braine-l’ Alleud B-1420, Belgium
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao 266237, China
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