1
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Tolokh IS, Folescu DE, Onufriev AV. Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains. J Phys Chem B 2024; 128:5855-5873. [PMID: 38860842 PMCID: PMC11194828 DOI: 10.1021/acs.jpcb.4c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/12/2024]
Abstract
The current practical "workhorses" of the atomistic implicit solvation─the Poisson-Boltzmann (PB) and generalized Born (GB) models─face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE ∼ 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.
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Affiliation(s)
- Igor S. Tolokh
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Dan E. Folescu
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Mathematics, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Alexey V. Onufriev
- Department
of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center
for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
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2
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H S S, V G, T M N, Setlur AS, K C, Kumar J, Niranjan V. Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches. J Biomol Struct Dyn 2023:1-20. [PMID: 38116745 DOI: 10.1080/07391102.2023.2294177] [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: 08/10/2023] [Accepted: 12/01/2023] [Indexed: 12/21/2023]
Abstract
This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sowmya H S
- Bangalore Bio-innovation Centre (BBC), Helix Biotech Park, Electronic City Phase-I, Bangalore, Karnataka, India
| | - Guruprasad V
- Homeopathic medical college and Hospital Bangalore, Bangalore, Karnataka, India
| | - Ningaraju T M
- University of Agricultural science Bangalore, Bangalore, Karnataka, India
| | - Anagha S Setlur
- Department of Biotechnology, RV College of Engineering, Bangalore, Karnataka, India
| | - Chandrashekar K
- Department of Biotechnology, RV College of Engineering, Bangalore, Karnataka, India
| | - Jitendra Kumar
- Biotechnology Industry Research Assistance Council (BIRAC), CGO complex Lodhi Road, New Delhi, India
| | - Vidya Niranjan
- Department of Biotechnology, RV College of Engineering, Bangalore, Karnataka, India
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3
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Case D, Aktulga HM, Belfon K, Cerutti DS, Cisneros GA, Cruzeiro VD, Forouzesh N, Giese TJ, Götz AW, Gohlke H, Izadi S, Kasavajhala K, Kaymak MC, King E, Kurtzman T, Lee TS, Li P, Liu J, Luchko T, Luo R, Manathunga M, Machado MR, Nguyen HM, O’Hearn KA, Onufriev AV, Pan F, Pantano S, Qi R, Rahnamoun A, Risheh A, Schott-Verdugo S, Shajan A, Swails J, Wang J, Wei H, Wu X, Wu Y, Zhang S, Zhao S, Zhu Q, Cheatham TE, Roe DR, Roitberg A, Simmerling C, York DM, Nagan MC, Merz KM. AmberTools. J Chem Inf Model 2023; 63:6183-6191. [PMID: 37805934 PMCID: PMC10598796 DOI: 10.1021/acs.jcim.3c01153] [Citation(s) in RCA: 160] [Impact Index Per Article: 160.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 10/10/2023]
Abstract
AmberTools is a free and open-source collection of programs used to set up, run, and analyze molecular simulations. The newer features contained within AmberTools23 are briefly described in this Application note.
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Affiliation(s)
- David
A. Case
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Hasan Metin Aktulga
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Kellon Belfon
- FOG
Pharmaceuticals Inc., Cambridge 02140, Massachusetts, United States
| | - David S. Cerutti
- Psivant, 451 D Street, Suite 205, Boston 02210, Massachusetts, United States
| | - G. Andrés Cisneros
- Department
of Physics, Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson 75801, Texas, United States
| | - Vinícius
Wilian D. Cruzeiro
- Department
of Chemistry and The PULSE Institute, Stanford
University, Stanford 94305, California, United States
| | - Negin Forouzesh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Timothy J. Giese
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Andreas W. Götz
- San
Diego Supercomputer Center, University of
California San Diego, La Jolla 92093-0505, California, United States
| | - Holger Gohlke
- Institute
for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Saeed Izadi
- Pharmaceutical
Development, Genentech, Inc., South San Francisco 94080, California, United
States
| | - Koushik Kasavajhala
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Mehmet C. Kaymak
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Edward King
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Tom Kurtzman
- Ph.D.
Programs in Chemistry, Biochemistry, and Biology, The Graduate Center of the City University of New York, 365 Fifth Avenue, New York 10016, New York, United States
- Department
of Chemistry, Lehman College, 250 Bedford Park Blvd West, Bronx 10468, New York, United States
| | - Tai-Sung Lee
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Pengfei Li
- Department
of Chemistry and Biochemistry, Loyola University
Chicago, Chicago 60660, Illinois, United States
| | - Jian Liu
- Beijing
National Laboratory for Molecular Sciences, Institute of Theoretical
and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Tyler Luchko
- Department
of Physics and Astronomy, California State
University, Northridge, Northridge 91330, California, United States
| | - Ray Luo
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Madushanka Manathunga
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | | | - Hai Minh Nguyen
- Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway 08854, New Jersey, United States
| | - Kurt A. O’Hearn
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Alexey V. Onufriev
- Departments
of Computer Science and Physics, Virginia
Tech, Blacksburg 24061, Virginia, United
States
| | - Feng Pan
- Department
of Statistics, Florida State University, Tallahassee 32304, Florida, United States
| | - Sergio Pantano
- Institut Pasteur de Montevideo, Montevideo 11400, Uruguay
| | - Ruxi Qi
- Cryo-EM
Center, Southern University of Science and
Technology, Shenzhen 518055, China
| | - Ali Rahnamoun
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing 48824-1322, Michigan, United States
| | - Ali Risheh
- Department
of Computer Science, California State University, Los Angeles 90032, California, United States
| | - Stephan Schott-Verdugo
- Institute
of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Akhil Shajan
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
| | - Jason Swails
- Entos, 4470 W Sunset
Blvd, Suite 107, Los Angeles 90027, California, United States
| | - Junmei Wang
- Department
of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh 15261, Pennsylvania, United States
| | - Haixin Wei
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Xiongwu Wu
- Laboratory
of Computational Biology, NHLBI, NIH, Bethesda 20892, Maryland, United States
| | - Yongxian Wu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Shi Zhang
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Shiji Zhao
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
- Nurix Therapeutics, Inc., San Francisco 94158, California, United States
| | - Qiang Zhu
- Departments
of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering,
Materials Science and Engineering, and Biomedical Engineering, Graduate
Program in Chemical and Materials Physics, University of California, Irvine 92697, California, United States
| | - Thomas E. Cheatham
- Department
of Medicinal Chemistry, The University of
Utah, 30 South 2000 East, Salt Lake City 84112, Utah, United
States
| | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda 20892, Maryland, United States
| | - Adrian Roitberg
- Department
of Chemistry, The University of Florida, 440 Leigh Hall, Gainesville 32611-7200, Florida, United States
| | - Carlos Simmerling
- Laufer
Center for Physical and Quantitative Biology, Department of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Darrin M. York
- Laboratory
for Biomolecular Simulation Research, Institute for Quantitative Biomedicine
and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway 08854, New Jersey, United States
| | - Maria C. Nagan
- Department
of Chemistry, Stony Brook University, Stony Brook 11794, New York, United States
| | - Kenneth M. Merz
- Department
of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824-1322, Michigan, United States
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4
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Sagar D, Risheh A, Sheikh N, Forouzesh N. Physics-Guided Deep Generative Model for New Ligand Discovery. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2023; 2023:10.1145/3584371.3613067. [PMID: 38706556 PMCID: PMC11067829 DOI: 10.1145/3584371.3613067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Structure-based drug discovery aims to identify small molecules that can attach to a specific target protein and change its functionality. Recently, deep learning has shown great promise in generating drug-like molecules with specific biochemical features and conditioned with structural features. However, they usually fail to incorporate an essential factor: the underlying physics which guides molecular formation and binding in real-world scenarios. In this work, we describe a physics-guided deep generative model for new ligand discovery, conditioned not only on the binding site but also on physics-based features that describe the binding mechanism between a receptor and a ligand. The proposed hybrid model has been tested on large protein-ligand complexes and small host-guest systems. Using the top-N methodology, on average more than 75% of the generated structures by our hybrid model were stronger binders than the original reference ligand. All of them had higher ΔGbind (affinity) values than the ones generated by the previous state-of-the-art method by an average margin of 1.88 kcal/mol. The visualization of the top-5 ligands generated by the proposed physics-guided model and the reference deep learning model demonstrate more feasible conformations and orientations by the former. The future directions include training and testing the hybrid model on larger datasets, adding more relevant physics-based features, and interpreting the deep learning outcomes from biophysical perspectives.
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Affiliation(s)
- Dikshant Sagar
- Department of Computer Science, California State University, Los Angeles, Los Angeles, California, USA
| | - Ali Risheh
- Department of Computer Science, California State University, Los Angeles, Los Angeles, California, USA
| | - Nida Sheikh
- Department of Computer Science, California State University, Los Angeles Los Angeles, California, USA
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, Los Angeles, California, USA
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