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Subtle.Nets.Finder: finely tuned interaction networks in DNA/RNA/protein complexes. J Mol Model 2017; 23:84. [DOI: 10.1007/s00894-017-3262-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 01/30/2017] [Indexed: 10/20/2022]
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2
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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3
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Yilmazer ND, Korth M. Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods. Int J Mol Sci 2016; 17:ijms17050742. [PMID: 27196893 PMCID: PMC4881564 DOI: 10.3390/ijms17050742] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 04/18/2016] [Accepted: 05/03/2016] [Indexed: 11/16/2022] Open
Abstract
We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.
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Affiliation(s)
- Nusret Duygu Yilmazer
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
| | - Martin Korth
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
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4
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Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands. Mol Divers 2015; 20:421-38. [PMID: 26553204 DOI: 10.1007/s11030-015-9648-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 11/02/2015] [Indexed: 10/22/2022]
Abstract
A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.
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Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
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Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
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Yilmazer ND, Heitel P, Schwabe T, Korth M. Benchmark of electronic structure methods for protein–ligand interactions based on high-level reference data. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2015. [DOI: 10.1142/s0219633615400015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The accurate prediction of the strength of protein–ligand interactions is a very difficult problem despite impressive advances in the field of biomolecular modeling. There are good reasons to believe that quantum mechanical methods can help with this task, but the application of such methods in the context of scoring is still in its infancy. Here we benchmark several wave function theory (WFT), density functional theory (DFT) and semiempirical quantum mechanical (SQM) approaches against high-level theoretical references for realistic test cases. Based on our findings for systematically generated model systems of real protein/ligand complexes from the PDB-bind database, we can recommend SCS-MP2 and B2-PLYP-D3 as reference methods, TPSS-D3+Dabc/def-TZVPP as the best DFT approach and PM6-DH+ as a fast and accurate alternative to full ab initio treatments.
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Affiliation(s)
- Nusret Duygu Yilmazer
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany
| | - Pascal Heitel
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany
| | - Tobias Schwabe
- Center for Bioinformatics and Institute of Physical Chemistry, University of Hamburg, Bundesstraße 43, 20146 Hamburg, Germany
| | - Martin Korth
- Institute for Theoretical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany
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Kantardjiev AA. irGPU.proton.Net: Irregular strong charge interaction networks of protonatable groups in protein molecules--a GPU solver using the fast multipole method and statistical thermodynamics. J Comput Chem 2015; 36:689-93. [PMID: 25650055 DOI: 10.1002/jcc.23842] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 12/04/2014] [Accepted: 12/24/2014] [Indexed: 11/08/2022]
Abstract
MOTIVATION A cluster of strongly interacting ionization groups in protein molecules with irregular ionization behavior is suggestive for specific structure-function relationship. However, their computational treatment is unconventional (e.g., lack of convergence in naive self-consistent iterative algorithm). The stringent evaluation requires evaluation of Boltzmann averaged statistical mechanics sums and electrostatic energy estimation for each microstate. SUMMARY irGPU: Irregular strong interactions in proteins--a GPU solver is novel solution to a versatile problem in protein biophysics--atypical protonation behavior of coupled groups. The computational severity of the problem is alleviated by parallelization (via GPU kernels) which is applied for the electrostatic interaction evaluation (including explicit electrostatics via the fast multipole method) as well as statistical mechanics sums (partition function) estimation. Special attention is given to the ease of the service and encapsulation of theoretical details without sacrificing rigor of computational procedures. irGPU is not just a solution-in-principle but a promising practical application with potential to entice community into deeper understanding of principles governing biomolecule mechanisms.
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Affiliation(s)
- Alexander A Kantardjiev
- Institute of Organic Chemistry, Nuclear Magnetic Resonance Lab, Bulgarian Academy of Sciences, Reduta, ul. Acad Bontchev Street, Sofia, 1000, Bulgaria
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Zhang X, Wong SE, Lightstone FC. Toward Fully Automated High Performance Computing Drug Discovery: A Massively Parallel Virtual Screening Pipeline for Docking and Molecular Mechanics/Generalized Born Surface Area Rescoring to Improve Enrichment. J Chem Inf Model 2014; 54:324-37. [DOI: 10.1021/ci4005145] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Xiaohua Zhang
- Biosciences and Biotechnology
Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, California 94550
| | - Sergio E. Wong
- Biosciences and Biotechnology
Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, California 94550
| | - Felice C. Lightstone
- Biosciences and Biotechnology
Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, California 94550
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Ilatovskiy AV, Abagyan R, Kufareva I. Quantum Mechanics Approaches to Drug Research in the Era of Structural Chemogenomics. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2013; 113:1669-1675. [PMID: 25414519 PMCID: PMC4235788 DOI: 10.1002/qua.24400] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The rapid growth of the available crystallographic information about proteins and binding pockets creates remarkable opportunities for enriching the drug research pipelines with computational prediction of novel protein-ligand interactions. While ab initio quantum mechanical approaches are known to provide unprecedented accuracy in structure-based binding energy calculations, they are limited to only small systems of dozens of atoms. In the structural chemogenomics era, it is critical that new approaches are developed that enable application of QM methodologies to non-covalent interactions in systems as large as protein-ligand complexes and conformational ensembles. This perspective highlights recent advances towards bridging the gap between high accuracy and high volume computations in drug research.
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Affiliation(s)
- Andrey V. Ilatovskiy
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA, 92093
- Division of Molecular and Radiation Biophysics, Konstantinov Petersburg Nuclear Physics Institute, NRC Kurchatov Institute, Gatchina, Russia, 188300
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA, 92093
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, USA, 92093
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