1
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Wei L, Li X, Bai Q, Kang J, Song J, Zhu S, Shen L, Wang H, Zhu C, Fang W. The performance of OPC and OPC3 water models in predictions of 2D structures under nanoconfinement. J Chem Phys 2024; 160:164504. [PMID: 38661199 DOI: 10.1063/5.0202518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024] Open
Abstract
Nanoconfined water plays an important role in broad fields of science and engineering. Classical molecular dynamics (MD) simulations have been widely used to investigate water phases under nanoconfinement. The key ingredient of MD is the force field. In this study, we systematically investigated the performance of a recently introduced family of globally optimal water models, OPC and OPC3, and TIP4P/2005 in describing nanoconfined two-dimensional (2D) water ice. Our studies show that the melting points of the monolayer square ice (MSI) of all three water models are higher than the melting points of the corresponding bulk ice Ih. Under the same conditions, the melting points of MSI of OPC and TIP4P/2005 are the same and are ∼90 K lower than that of the OPC3 water model. In addition, we show that OPC and TIP4P/2005 water models are able to form a bilayer AA-stacked structure and a trilayer AAA-stacked structure, which are not the cases for the OPC3 model. Considering the available experimental data and first-principles simulations, we consider the OPC water model as a potential water model for 2D water ice MD studies.
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Affiliation(s)
- Laiyang Wei
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Xiaojiao Li
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Qi Bai
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Jing Kang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Jueying Song
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Shuang Zhu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Lin Shen
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Huan Wang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, People's Republic of China
| | - Chongqin Zhu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Weihai Fang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, People's Republic of China
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2
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Bass L, Elder LH, Folescu DE, Forouzesh N, Tolokh IS, Karpatne A, Onufriev AV. Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment. J Chem Theory Comput 2024; 20:396-410. [PMID: 38149593 PMCID: PMC10950260 DOI: 10.1021/acs.jctc.3c00981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.
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Affiliation(s)
- Lewis Bass
- Department of Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Luke H Elder
- 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
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, California 90032, United States
| | - Igor S Tolokh
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Anuj Karpatne
- Department of Computer Science, 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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3
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Wang X, Wang Y, Guo M, Wang X, Li Y, Zhang JZH. Assessment of an Electrostatic Energy-Based Charge Model for Modeling the Electrostatic Interactions in Water Solvent. J Chem Theory Comput 2023; 19:6294-6312. [PMID: 37656610 DOI: 10.1021/acs.jctc.3c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
The protein force field based on the restrained electrostatic potential (RESP) charges has limitations in accurately describing hydrogen bonding interactions in proteins. To address this issue, we propose an alternative approach called the electrostatic energy-based charges (EEC) model, which shows improved performance in describing electrostatic interactions (EIs) of hydrogen bonds in proteins. In this study, we further investigate the performance of the EEC model in modeling EIs in water solvent. Our findings demonstrate that the fixed EEC model can effectively reproduce the quantum mechanics/molecular mechanics (QM/MM)-calculated EIs between a water molecule and various water solvent environments. However, to achieve the same level of computational accuracy, the electrostatic potential (ESP) charge model needs to fluctuate according to the electrostatic environment. Our analysis indicates that the requirement for charge adjustments depends on the specific mathematical and physical representation of EIs as a function of the environment for deriving charges. By comparing with widely used empirical water models calibrated to reproduce experimental properties, we confirm that the performance of the EEC model in reproducing QM/MM EIs is similar to that of general purpose TIP4P-like water models such as TIP4P-Ew and TIP4P/2005. When comparing the computed 10,000 distinct EI values within the range of -40 to 0 kcal/mol with the QM/MM results calculated at the MP2/aug-cc-pVQZ/TIP3P level, we noticed that the mean unsigned error (MUE) for the EEC model is merely 0.487 kcal/mol, which is remarkably similar to the MUE values of the TIP4P-Ew (0.63 kcal/mol) and TIP4P/2005 (0.579 kcal/mol) models. However, both the RESP method and the TIP3P model exhibit a tendency to overestimate the EIs, as evidenced by their higher MUE values of 1.761 and 1.293 kcal/mol, respectively. EEC-based molecular dynamics simulations have demonstrated that, when combined with appropriate van der Waals parameters, the EEC model can closely reproduce oxygen-oxygen radial distribution function and density of water, showing a remarkable similarity to the well-established TIP4P-like empirical water models. Our results demonstrate that the EEC model has the potential to build force fields with comparable accuracy to more sophisticated empirical TIP4P-like water models.
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Affiliation(s)
- Xianwei Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Yiying Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Man Guo
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Xuechao Wang
- College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
| | - Yang Li
- College of Information Science and Engineering, Shandong Agricultural University, Tai'an, Shandong 271018, China
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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4
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Roy R, Poddar S, Kar P. Comparison of the conformational dynamics of an N-glycan in implicit and explicit solvents. Carbohydr Res 2022; 522:108700. [DOI: 10.1016/j.carres.2022.108700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/28/2022]
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5
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Cain S, Risheh A, Forouzesh N. A Physics-Guided Neural Network for Predicting Protein–Ligand Binding Free Energy: From Host–Guest Systems to the PDBbind Database. Biomolecules 2022; 12:biom12070919. [PMID: 35883475 PMCID: PMC9312865 DOI: 10.3390/biom12070919] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/26/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022] Open
Abstract
Calculation of protein–ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, on the other hand, are often accurate yet not fully interpretable and also likely to be overfitted. In this research, we explore the application of Theory-Guided Data Science in studying protein–ligand binding. A hybrid model is introduced by integrating Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). The proposed physics-data model is tested on a dataset of 368 complexes from the PDBbind refined set and 72 host–guest systems. Results demonstrate that the proposed Physics-Guided Neural Network can successfully improve the “accuracy” of the pure data-driven model. In addition, the “interpretability” and “transferability” of our model have boosted compared to the purely data-driven model. Further analyses include evaluating model robustness and understanding relationships between the physical features.
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Affiliation(s)
- Sahar Cain
- Department of Computer Science, California State University, Los Angeles, CA 90032, USA;
| | - Ali Risheh
- Department of Computer Engineering, Amirkabir University of Technology, Tehran 15914, Iran;
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, CA 90032, USA;
- Correspondence:
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6
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Bogunia M, Makowski M. Influence of Ionic Strength on Hydrophobic Interactions in Water: Dependence on Solute Size and Shape. J Phys Chem B 2020; 124:10326-10336. [PMID: 33147018 PMCID: PMC7681779 DOI: 10.1021/acs.jpcb.0c06399] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
![]()
Hydrophobicity is a phenomenon of
great importance in biology,
chemistry, and biochemistry. It is defined as the interaction between
nonpolar molecules or groups in water and their low solubility. Hydrophobic
interactions affect many processes in water, for example, complexation,
surfactant aggregation, and coagulation. These interactions play a
pivotal role in the formation and stability of proteins or biological
membranes. In the present study, we assessed the effect of ionic strength,
solute size, and shape on hydrophobic interactions between pairs of
nonpolar particles. Pairs of methane, neopentane, adamantane, fullerene,
ethane, propane, butane, hexane, octane, and decane were simulated
by molecular dynamics in AMBER 16.0 force field. As a solvent, TIP3P
and TIP4PEW water models were used. Potential of mean force (PMF)
plots of these dimers were determined at four values of ionic strength,
0, 0.04, 0.08, and 0.40 mol/dm3, to observe its impact
on hydrophobic interactions. The characteristic shape of PMFs with
three extrema (contact minimum, solvent-separated minimum, and desolvation
maximum) was observed for most of the compounds for hydrophobic interactions.
Ionic strength affected hydrophobic interactions. We observed a tendency
to deepen contact minima with an increase in ionic strength value
in the case of spherical and spheroidal molecules. Additionally, two-dimensional
distribution functions describing water density and average number
of hydrogen bonds between water molecules were calculated in both
water models for adamantane and hexane. It was observed that the density
of water did not significantly change with the increase in ionic strength,
but the average number of hydrogen bonds changed. The latter tendency
strongly depends on the water model used for simulations.
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Affiliation(s)
- Małgorzata Bogunia
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Mariusz Makowski
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
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7
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Xiong Y, Shabane PS, Onufriev AV. Melting Points of OPC and OPC3 Water Models. ACS OMEGA 2020; 5:25087-25094. [PMID: 33043187 PMCID: PMC7542584 DOI: 10.1021/acsomega.0c02638] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 08/27/2020] [Indexed: 06/11/2023]
Abstract
A recently introduced family of globally optimal water models, OPC, has shown promise in a variety of biomolecular simulations, but properties of these water models outside of the liquid phase remain mostly unexplored. Here, we contribute to filling the gap by reporting melting temperatures of ice I h of OPC and OPC3 water models. Through the direct coexistence method, which we make available in the AMBER package, the melting points of OPC and OPC3 are estimated as 242 and 210 K, similar to TIP4P-Ew and SPC/E models, respectively, and appreciably below the experimental value of 273.15 K under 1 bar pressure. Water models of the OPC family were optimized to best reproduce water properties in the liquid phase where these models offer noteworthy accuracy advantages over many models of previous generations. It is not surprising that the accuracy of OPC models in describing the phase transition to the solid state does not appear to offer similar improvements. The new anisotropic barostat option implemented in AMBER may benefit system preparation and simulation outside of the direct coexistence applications, such as modeling of membranes or very long DNA strands.
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Affiliation(s)
- Yeyue Xiong
- Department
of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg 24061-0131, United States
| | | | - Alexey V. Onufriev
- Department
of Physics, Virginia Tech, Blacksburg 24061-0131, United States
- Department
of Computer Science, Virginia Tech Department of Physics, Virginia Tech, Blacksburg 24061-0131, United States
- Center
for Soft Matter and Biological Physics, Virginia Tech, Blacksburg 24061-0131, United States
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8
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Forouzesh N, Mukhopadhyay A, Watson LT, Onufriev AV. Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding. J Chem Theory Comput 2020; 16:4669-4684. [PMID: 32450041 PMCID: PMC8594251 DOI: 10.1021/acs.jctc.0c00142] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accuracy of protein-ligand binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a global multidimensional optimization pipeline is developed to find optimal atomic radii specifically for protein-ligand binding calculations in implicit solvent. The computational pipeline has these three key components: (1) a massively parallel implementation of a deterministic global optimization algorithm (VTDIRECT95), (2) an accurate yet reasonably fast generalized Born implicit solvent model (GBNSR6), and (3) a novel robustness metric that helps distinguish between nearly degenerate local minima via a postprocessing step of the optimization. A graph-based "kT-connectivity" approach to explore and visualize the multidimensional energy landscape is proposed: local minima that can be reached from the global minimum without exceeding a given energy threshold (kT) are considered to be connected. As an illustration of the capabilities of the optimization pipeline, we apply it to find a global optimum in the space of just five radii: four atomic (O, H, N, and C) radii and water probe radius. The optimized radii, ρW = 1.37 Å, ρC = 1.40 Å, ρH = 1.55 Å, ρN = 2.35 Å, and ρO = 1.28 Å, lead to a closer agreement of electrostatic binding free energies with the explicit solvent reference than two commonly used sets of radii previously optimized for small molecules. At the same time, the ability of the optimizer to find the global optimum reveals fundamental limits of the common two-dielectric implicit solvation model: the computed electrostatic binding free energies are still almost 4 kcal/mol away from the explicit solvent reference. The proposed computational approach opens the possibility to further improve the accuracy of practical computational protocols for binding free energy calculations.
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Affiliation(s)
- Negin Forouzesh
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Abhishek Mukhopadhyay
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Layne T Watson
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Mathematics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Department of Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute & State University, Blacksburg, Virginia 24061, United States
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9
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Structure-based screening and validation of potential dengue virus inhibitors through classical and QM/MM affinity estimation. J Mol Graph Model 2019; 90:128-143. [PMID: 31082639 DOI: 10.1016/j.jmgm.2019.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 04/19/2019] [Accepted: 04/19/2019] [Indexed: 11/22/2022]
Abstract
The recurrent outbreaks of dengue virus around the globe represent a huge challenge for governments and public health organizations. With the rapid growth and ease of transportation, dengue disease continues to spread, placing more of the world population under constant threat. Despite decades of research efforts, no effective small molecule antivirals are available against dengue virus. With the efficacy of the recently developed vaccine to be determined, there is an urgent unmet need for small molecule dengue virus treatments. In the current study, we employed state-of-the-art molecular modelling simulations to identify novel inhibitors of the dengue virus envelope protein. The binding modes of all compounds within the conserved β-OctylGlucoside (β-OG) pocket were studied using a combination of docking, molecular dynamics simulations and binding free energy calculations. Here, we describe ten new compounds that significantly reduce production of dengue virus as determined using standard cell-based virological assays. Moreover, we present a comprehensive structural analysis of the identified hits, focusing on their electrostatic and lipophilic binding energy contributions. Finally, we highlight the effect of the desolvation penalty in limiting the activity of some of these compounds. The data presented here paves the way toward rationally designing selective and potent novel inhibitors against dengue virus.
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10
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Abstract
It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.
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Affiliation(s)
- Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA;
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA;
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11
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Anandakrishnan R, Izadi S, Onufriev AV. Why Computed Protein Folding Landscapes Are Sensitive to the Water Model. J Chem Theory Comput 2018; 15:625-636. [DOI: 10.1021/acs.jctc.8b00485] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ramu Anandakrishnan
- Department of Biomedical Sciences, Edward Via College of Osteopathic Medicine, Blacksburg, Virginia 24060, United States
| | - Saeed Izadi
- Early Stage Pharmaceutical Development, Genentech Inc., South San Francisco, California 94080, United States
| | - Alexey V. Onufriev
- Department of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
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12
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Tolokh IS, Thomas DG, Onufriev AV. Explicit ions/implicit water generalized Born model for nucleic acids. J Chem Phys 2018; 148:195101. [PMID: 30307229 DOI: 10.1063/1.5027260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The ion atmosphere around highly charged nucleic acid molecules plays a significant role in their dynamics, structure, and interactions. Here we utilized the implicit solvent framework to develop a model for the explicit treatment of ions interacting with nucleic acid molecules. The proposed explicit ions/implicit water model is based on a significantly modified generalized Born (GB) model and utilizes a non-standard approach to define the solute/solvent dielectric boundary. Specifically, the model includes modifications to the GB interaction terms for the case of multiple interacting solutes-disconnected dielectric boundary around the solute-ion or ion-ion pairs. A fully analytical description of all energy components for charge-charge interactions is provided. The effectiveness of the approach is demonstrated by calculating the potential of mean force for Na+-Cl- ion pair and by carrying out a set of Monte Carlo (MC) simulations of mono- and trivalent ions interacting with DNA and RNA duplexes. The monovalent (Na+) and trivalent (CoHex3+) counterion distributions predicted by the model are in close quantitative agreement with all-atom explicit water molecular dynamics simulations used as reference. Expressed in the units of energy, the maximum deviations of local ion concentrations from the reference are within k B T. The proposed explicit ions/implicit water GB model is able to resolve subtle features and differences of CoHex distributions around DNA and RNA duplexes. These features include preferential CoHex binding inside the major groove of the RNA duplex, in contrast to CoHex biding at the "external" surface of the sugar-phosphate backbone of the DNA duplex; these differences in the counterion binding patters were earlier shown to be responsible for the observed drastic differences in condensation propensities between short DNA and RNA duplexes. MC simulations of CoHex ions interacting with the homopolymeric poly(dA·dT) DNA duplex with modified (de-methylated) and native thymine bases are used to explore the physics behind CoHex-thymine interactions. The simulations suggest that the ion desolvation penalty due to proximity to the low dielectric volume of the methyl group can contribute significantly to CoHex-thymine interactions. Compared to the steric repulsion between the ion and the methyl group, the desolvation penalty interaction has a longer range and may be important to consider in the context of methylation effects on DNA condensation.
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Affiliation(s)
- Igor S Tolokh
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Dennis G Thomas
- Computational Biology, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, USA
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13
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Izadi S, Harris RC, Fenley MO, Onufriev AV. Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies. J Chem Theory Comput 2018; 14:1656-1670. [PMID: 29378399 DOI: 10.1021/acs.jctc.7b00886] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ Gel) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ Gel estimates varies widely between different GB models and also across different types of biomolecular complexes, with R2 correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R2 = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ Gel. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.
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Affiliation(s)
- Saeed Izadi
- Early Stage Pharmaceutical Development , Genentech Inc. , 1 DNA Way , South San Francisco , California 94080 , United States
| | - Robert C Harris
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
| | - Marcia O Fenley
- Institute of Molecular Biophysics , Florida State University , Tallahassee , Florida 32306-3408 , United States
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14
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Chakravorty A, Jia Z, Li L, Zhao S, Alexov E. Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling. J Chem Theory Comput 2018; 14:1020-1032. [PMID: 29350933 PMCID: PMC9885857 DOI: 10.1021/acs.jctc.7b00756] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Typically, the ensemble average polar component of solvation energy (ΔGpolarsolv) of a macromolecule is computed using molecular dynamics (MD) or Monte Carlo (MC) simulations to generate conformational ensemble and then single/rigid conformation solvation energy calculation is performed on each snapshot. The primary objective of this work is to demonstrate that Poisson-Boltzmann (PB)-based approach using a Gaussian-based smooth dielectric function for macromolecular modeling previously developed by us (Li et al. J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) can reproduce that ensemble average (ΔGpolarsolv) of a protein from a single structure. We show that the Gaussian-based dielectric model reproduces the ensemble average ΔGpolarsolv(⟨ΔGpolarsolv⟩) from an energy-minimized structure of a protein regardless of the minimization environment (structure minimized in vacuo, implicit or explicit waters, or crystal structure); the best case, however, is when it is paired with an in vacuo-minimized structure. In other minimization environments (implicit or explicit waters or crystal structure), the traditional two-dielectric model can still be selected with which the model produces correct solvation energies. Our observations from this work reflect how the ability to appropriately mimic the motion of residues, especially the salt bridge residues, influences a dielectric model's ability to reproduce the ensemble average value of polar solvation free energy from a single in vacuo-minimized structure.
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Affiliation(s)
- Arghya Chakravorty
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Zhe Jia
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Lin Li
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA
| | - Shan Zhao
- Departement of Mathematics, College of Arts and Sciences, University of Alabama, Tuscaloosa, Alabama 35487, USA
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA.,Corresponding Author Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634, USA.
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15
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Boonamnaj P, Sompornpisut P. Insight into the Role of the Hv1 C-Terminal Domain in Dimer Stabilization. J Phys Chem B 2018; 122:1037-1048. [PMID: 29290112 DOI: 10.1021/acs.jpcb.7b08669] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The voltage-gated proton-selective channel (Hv1) conducts protons in response to changes in membrane potential. The Hv1 protein forms dimers in the membrane. Crystal structures of Hv1 channels have revealed that the primary contacts between the two monomers are in the C-terminal domain (CTD), which forms a coiled-coil structure. The role of Hv1-CTD in channel assembly and activity is not fully understood. Here, molecular dynamics (MD) simulations of full-length and truncated CTD models of human and mouse Hv1 channels reveal a strong contribution of the CTD to the packing of the transmembrane domains. Simulations of the CTD models highlight four fundamental interactions of the key residues contributing to dimer stability. These include salt bridges, hydrophobic interactions, hydrogen bonds, and a disulfide bond across the dimer interface. At neutral pH, salt-bridge interactions increase dimer stability and the dimer becomes less stable at acidic pH. Hydrophobic core packing of the heptad pattern is important for stability, as shown by favorable nonpolar binding free energies rather than by electrostatic components. Moreover, free-energy calculations indicate that a more uniform hydrophobic core in the coiled-coil structure of the Hv1-NIN, a channel carrying the triple mutation M234N-N235I-V236N, leads to an increase in dimer stability with respect to the wild-type. A Cys disulfide bond has a strong impact on dimer stability by holding the dimer together and facilitating the interactions described above. These results are consistent with dissociative temperatures and energy barriers of dimer dissociation obtained from the temperature-accelerated MD.
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Affiliation(s)
- Panisak Boonamnaj
- Department of Chemistry, Faculty of Science, Chulalongkorn University , Bangkok 10330, Thailand
| | - Pornthep Sompornpisut
- Department of Chemistry, Faculty of Science, Chulalongkorn University , Bangkok 10330, Thailand
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16
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Zhou HX, Pang X. Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation. Chem Rev 2018; 118:1691-1741. [PMID: 29319301 DOI: 10.1021/acs.chemrev.7b00305] [Citation(s) in RCA: 454] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.
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Affiliation(s)
- Huan-Xiang Zhou
- Department of Chemistry and Department of Physics, University of Illinois at Chicago , Chicago, Illinois 60607, United States.,Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
| | - Xiaodong Pang
- Department of Physics and Institute of Molecular Biophysics, Florida State University , Tallahassee, Florida 32306, United States
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17
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Onufriev AV, Izadi S. Water models for biomolecular simulations. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2017. [DOI: 10.1002/wcms.1347] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Alexey V. Onufriev
- Department of Physics; Virginia Tech; Blacksburg VA USA
- Department of Computer Science; Virginia Tech; Blacksburg VA USA
- Center for Soft Matter and Biological Physics; Virginia Tech; Blacksburg VA USA
| | - Saeed Izadi
- Early Stage Pharmaceutical Development; Genentech Inc.; South San Francisco, CA USA
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18
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Bu B, Tong X, Li D, Hu Y, He W, Zhao C, Hu R, Li X, Shao Y, Liu C, Zhao Q, Ji B, Diao J. N-Terminal Acetylation Preserves α-Synuclein from Oligomerization by Blocking Intermolecular Hydrogen Bonds. ACS Chem Neurosci 2017; 8:2145-2151. [PMID: 28741930 DOI: 10.1021/acschemneuro.7b00250] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The abnormal aggregation of α-synuclein (α-Syn) is closely associated with Parkinson's disease. Different post-translational modifications of α-Syn have been identified and contribute distinctly in α-Syn aggregation and cytotoxicity. Recently, α-Syn was reported to be N-terminally acetylated in cells, yet the functional implication of this modification, especially in α-Syn oligomerization, remains unclear. By using a solid-state nanopore system, we found that N-terminal acetylation can significantly decrease α-Syn oligomerization. Replica-exchange molecular dynamics simulations further revealed that addition of an acetyl group at the N-terminus disrupts intermolecular hydrogen bonds, which slows down the initial α-Syn oligomerization. Our finding highlights the essential role of N-terminal acetylation of α-Syn in preserving its native conformation against pathological aggregation.
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Affiliation(s)
- Bing Bu
- Biomechanics
and Biomaterials Laboratory, Department of Applied Mechanics, Beijing Institute of Technology, Beijing 100081, China
| | - Xin Tong
- State
Key Laboratory for Mesoscopic Physics and Electron Microscopy Laboratory,
School of Physics, Peking University, Beijing 100871, China
| | - Dechang Li
- Biomechanics
and Biomaterials Laboratory, Department of Applied Mechanics, Beijing Institute of Technology, Beijing 100081, China
| | - Yachong Hu
- Department
of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267, United States
- Key
Laboratory of Biomedical Information Engineering of the Ministry of
Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Wangxiao He
- Key
Laboratory of Biomedical Information Engineering of the Ministry of
Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Chunyu Zhao
- Interdisciplinary
Research Center on Biology and Chemistry, Shanghai Institute of Organic
Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Rui Hu
- State
Key Laboratory for Mesoscopic Physics and Electron Microscopy Laboratory,
School of Physics, Peking University, Beijing 100871, China
| | - Xiaoqing Li
- State
Key Laboratory for Mesoscopic Physics and Electron Microscopy Laboratory,
School of Physics, Peking University, Beijing 100871, China
| | - Yongping Shao
- Key
Laboratory of Biomedical Information Engineering of the Ministry of
Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Cong Liu
- Interdisciplinary
Research Center on Biology and Chemistry, Shanghai Institute of Organic
Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Qing Zhao
- State
Key Laboratory for Mesoscopic Physics and Electron Microscopy Laboratory,
School of Physics, Peking University, Beijing 100871, China
| | - Baohua Ji
- Biomechanics
and Biomaterials Laboratory, Department of Applied Mechanics, Beijing Institute of Technology, Beijing 100081, China
| | - Jiajie Diao
- Department
of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267, United States
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19
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Forouzesh N, Izadi S, Onufriev AV. Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies. J Chem Inf Model 2017; 57:2505-2513. [DOI: 10.1021/acs.jcim.7b00192] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Saeed Izadi
- Early Stage Pharmaceutical
Development, Genentech Inc., 1 DNA
Way, South San Francisco, California 94080, United States
| | - Alexey V. Onufriev
- Center
for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States
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20
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Setny P, Dudek A. Explicit Solvent Hydration Benchmark for Proteins with Application to the PBSA Method. J Chem Theory Comput 2017; 13:2762-2776. [PMID: 28498675 DOI: 10.1021/acs.jctc.7b00247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Explicit and implicit solvent models have a proven record of delivering hydration free energies of small, druglike solutes in reasonable agreement with experiment. Hydration of macromolecules, such as proteins, is to a large extent uncharted territory, with few results shedding light on quantitative consistency between different solvent models, let alone their ability to reproduce real water. In this work, based on extensive explicit solvent simulations employing TIP3P and SPC/E water models we analyze hydration free energy changes between fixed conformations of 5 diverse proteins, including large multidomain structures. For the two solvent models we find better agreement in electrostatic rather than nonpolar contributions (RMSE of 2.3 and 2.7 kcal/mol, respectively), even though absolute values of the latter are typically an order of magnitude smaller. We also highlight the importance of finite size corrections to relative protein hydration free energies, which turn out to be rather large, on the order of several kcal/mol, and are necessary for proper interpretation of results obtained under periodic boundary conditions. We further compare gathered data with predictions of the implicit solvent approach based on the Poisson equation and the surface or volume based nonpolar term. We find definitely lesser consistency than between the two explicit models (RMSE between implicit and TIP3 results of 11.3 and 8.4 kcal/mol for electrostatic and nonpolar contributions, respectively). In the process we determine the value of the protein dielectric constant and the geometric model for the dielectric boundary that provide for the best agreement. Finally, we evaluate the usefulness of surface and volume based models of nonpolar contributions to hydration free energy of large biomolecules.
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Affiliation(s)
- Piotr Setny
- Centre of New Technologies, University of Warsaw , Banacha 2c, 02-097 Warsaw, Poland
| | - Anita Dudek
- Centre of New Technologies, University of Warsaw , Banacha 2c, 02-097 Warsaw, Poland
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21
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Dubey V, Prasanna X, Sengupta D. Estimating the Lipophobic Contributions in Model Membranes. J Phys Chem B 2017; 121:2111-2120. [DOI: 10.1021/acs.jpcb.6b09863] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Vikas Dubey
- Physical Chemistry Division, National Chemical Laboratory, Pune 411008, India
| | - Xavier Prasanna
- Physical Chemistry Division, National Chemical Laboratory, Pune 411008, India
| | - Durba Sengupta
- Physical Chemistry Division, National Chemical Laboratory, Pune 411008, India
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22
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Katkova EV, Onufriev AV, Aguilar B, Sulimov VB. Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding. J Mol Graph Model 2016; 72:70-80. [PMID: 28064081 DOI: 10.1016/j.jmgm.2016.12.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/07/2016] [Accepted: 12/15/2016] [Indexed: 11/18/2022]
Abstract
In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87-0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82-0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65-0.99 and 0.76-0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953-0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes.
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Affiliation(s)
- E V Katkova
- Dimonta, Ltd., Nagornaya Street 15, Bldg 8, Moscow, 117186, Russia; Research Computer Center, Lomonosov Moscow State University, Leninskie Gory 1,Bldg 4, Moscow, 119992, Russia.
| | - A V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA, USA
| | - B Aguilar
- Institute for Systems Biology, Seattle, WA, USA
| | - V B Sulimov
- Dimonta, Ltd., Nagornaya Street 15, Bldg 8, Moscow, 117186, Russia; Research Computer Center, Lomonosov Moscow State University, Leninskie Gory 1,Bldg 4, Moscow, 119992, Russia
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23
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Izadi S, Anandakrishnan R, Onufriev AV. Implicit Solvent Model for Million-Atom Atomistic Simulations: Insights into the Organization of 30-nm Chromatin Fiber. J Chem Theory Comput 2016; 12:5946-5959. [PMID: 27748599 PMCID: PMC5649046 DOI: 10.1021/acs.jctc.6b00712] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.
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Affiliation(s)
- Saeed Izadi
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
| | - Ramu Anandakrishnan
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Biomedical Engineering and Mechanics, ‡Biomedical Division, Edward Via College of Osteopathic Medicine, ¶Department of Computer Science and Physics, and §Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University , Blacksburg, Virginia 24061, United States
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24
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Chakavorty A, Li L, Alexov E. Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols. J Comput Chem 2016; 37:2495-507. [PMID: 27546093 PMCID: PMC5030180 DOI: 10.1002/jcc.24475] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 08/03/2016] [Accepted: 08/06/2016] [Indexed: 01/11/2023]
Abstract
Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Arghya Chakavorty
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, 29634
| | - Lin Li
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, 29634
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, 29634.
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25
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Izadi S, Onufriev AV. Accuracy limit of rigid 3-point water models. J Chem Phys 2016; 145:074501. [PMID: 27544113 PMCID: PMC4991989 DOI: 10.1063/1.4960175] [Citation(s) in RCA: 159] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Accepted: 07/19/2016] [Indexed: 11/14/2022] Open
Abstract
Classical 3-point rigid water models are most widely used due to their computational efficiency. Recently, we introduced a new approach to constructing classical rigid water models [S. Izadi et al., J. Phys. Chem. Lett. 5, 3863 (2014)], which permits a virtually exhaustive search for globally optimal model parameters in the sub-space that is most relevant to the electrostatic properties of the water molecule in liquid phase. Here we apply the approach to develop a 3-point Optimal Point Charge (OPC3) water model. OPC3 is significantly more accurate than the commonly used water models of same class (TIP3P and SPCE) in reproducing a comprehensive set of liquid bulk properties, over a wide range of temperatures. Beyond bulk properties, we show that OPC3 predicts the intrinsic charge hydration asymmetry (CHA) of water - a characteristic dependence of hydration free energy on the sign of the solute charge - in very close agreement with experiment. Two other recent 3-point rigid water models, TIP3PFB and H2ODC, each developed by its own, completely different optimization method, approach the global accuracy optimum represented by OPC3 in both the parameter space and accuracy of bulk properties. Thus, we argue that an accuracy limit of practical 3-point rigid non-polarizable models has effectively been reached; remaining accuracy issues are discussed.
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Affiliation(s)
- Saeed Izadi
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia 24060, USA
| | - Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA
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26
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Phipps MJS, Fox T, Tautermann CS, Skylaris CK. Energy Decomposition Analysis Based on Absolutely Localized Molecular Orbitals for Large-Scale Density Functional Theory Calculations in Drug Design. J Chem Theory Comput 2016; 12:3135-48. [PMID: 27248370 DOI: 10.1021/acs.jctc.6b00272] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We report the development and implementation of an energy decomposition analysis (EDA) scheme in the ONETEP linear-scaling electronic structure package. Our approach is hybrid as it combines the localized molecular orbital EDA (Su, P.; Li, H. J. Chem. Phys., 2009, 131, 014102) and the absolutely localized molecular orbital EDA (Khaliullin, R. Z.; et al. J. Phys. Chem. A, 2007, 111, 8753-8765) to partition the intermolecular interaction energy into chemically distinct components (electrostatic, exchange, correlation, Pauli repulsion, polarization, and charge transfer). Limitations shared in EDA approaches such as the issue of basis set dependence in polarization and charge transfer are discussed, and a remedy to this problem is proposed that exploits the strictly localized property of the ONETEP orbitals. Our method is validated on a range of complexes with interactions relevant to drug design. We demonstrate the capabilities for large-scale calculations with our approach on complexes of thrombin with an inhibitor comprised of up to 4975 atoms. Given the capability of ONETEP for large-scale calculations, such as on entire proteins, we expect that our EDA scheme can be applied in a large range of biomolecular problems, especially in the context of drug design.
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Affiliation(s)
- M J S Phipps
- School of Chemistry, University of Southampton , Highfield, Southampton SO17 1BJ, United Kingdom
| | - T Fox
- Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , 88397 Biberach, Germany
| | - C S Tautermann
- Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG , 88397 Biberach, Germany
| | - C-K Skylaris
- School of Chemistry, University of Southampton , Highfield, Southampton SO17 1BJ, United Kingdom
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