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Alibakhshi A, Schäfer LV. Electron iso-density surfaces provide a thermodynamically consistent representation of atomic and molecular surfaces. Nat Commun 2024; 15:6086. [PMID: 39030194 PMCID: PMC11271626 DOI: 10.1038/s41467-024-50408-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/02/2024] [Indexed: 07/21/2024] Open
Abstract
The surface area of atoms and molecules plays a crucial role in shaping many physiochemical properties of materials. Despite its fundamental importance, precisely defining atomic and molecular surfaces has long been a puzzle. Among the available definitions, a straightforward and elegant approach by Bader describes a molecular surface as an iso-density surface beyond which the electron density drops below a certain cut-off. However, so far neither this theory nor a decisive value for the density cut-off have been amenable to experimental verification due to the limitations of conventional experimental methods. In the present study, we employ a state-of-the-art experimental method based on the recently developed concept of thermodynamically effective (TE) surfaces to tackle this longstanding problem. By studying a set of 104 molecules, a close to perfect agreement between quantum chemical evaluations of iso-density surfaces contoured at a cut-off density of 0.0016 a.u. and experimental results obtained via thermodynamic phase change data is demonstrated, with a mean unsigned percentage deviation of 1.6% and a correlation coefficient of 0.995. Accordingly, we suggest the iso-density surface contoured at an electron density value of 0.0016 a.u. as a representation of the surface of atoms and molecules.
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Affiliation(s)
- Amin Alibakhshi
- Center for Theoretical Chemistry, Ruhr University Bochum, 44780, Bochum, Germany.
- Lehrstuhl für Theoretische Chemie II, Ruhr University Bochum, 44780, Bochum, Germany.
- Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780, Bochum, Germany.
- Research Center Trustworthy Data Science and Security, Technical University Dortmund, 44227, Dortmund, Germany.
| | - Lars V Schäfer
- Center for Theoretical Chemistry, Ruhr University Bochum, 44780, Bochum, Germany.
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2
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Hu X, Amin KS, Schneider M, Lim C, Salahub D, Baldauf C. System-Specific Parameter Optimization for Nonpolarizable and Polarizable Force Fields. J Chem Theory Comput 2024; 20:1448-1464. [PMID: 38279917 PMCID: PMC10867808 DOI: 10.1021/acs.jctc.3c01141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/29/2024]
Abstract
The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer (CT) and polarization (POL) effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding CT and POL. Furthermore, we present an open-source parametrization tool, called FFAFFURR, that enables the (system-specific) parametrization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a zinc-finger protein.
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Affiliation(s)
- Xiaojuan Hu
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Kazi S. Amin
- Centre
for Molecular Simulation and Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Markus Schneider
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
| | - Carmay Lim
- Institute
of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- Department
of Chemistry, National Tsing Hua University, Hsinchu 300, Taiwan
| | - Dennis Salahub
- Centre
for Molecular Simulation and Department of Chemistry, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Carsten Baldauf
- Fritz-Haber-Institut
der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany
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3
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Zhang P, Yang W. Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein. J Chem Phys 2023; 159:024118. [PMID: 37431910 PMCID: PMC10481389 DOI: 10.1063/5.0142280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/22/2023] [Indexed: 07/12/2023] Open
Abstract
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.
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Affiliation(s)
- Pan Zhang
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Weitao Yang
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
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Charvati E, Sun H. Potential Energy Surfaces Sampled in Cremer-Pople Coordinates and Represented by Common Force Field Functionals for Small Cyclic Molecules. J Phys Chem A 2023; 127:2646-2663. [PMID: 36893434 DOI: 10.1021/acs.jpca.3c00095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
The complex conformations of the cyclic moieties impact the physical and chemical properties of molecules. In this work, we chose 22 molecules of four-, five-, and six-membered rings and performed a thorough conformational sampling using Cremer-Pople coordinates. With consideration of symmetries, we obtained a total of 1504 conformational structures for four-membered, 5576 for five-membered, and 13509 for six-membered rings. All well-known and many less well-known conformers for each molecule were identified. We represented the potential energy surfaces (PESs) by fitting the data to common analytical force field (FF) functional forms. We found that the general features of PESs can be described by the essential FF functional forms; however, the accuracy of representation can be improved remarkably by including the torsion-bond and torsion-angle coupling terms. The best fit yields R-squared (R2) values close to 1.0 and mean absolute errors in energy less than 0.3 kcal/mol.
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Affiliation(s)
- Evangelia Charvati
- School of Chemistry and Chemical Engineering, Materials Genome Initiative Center, and Key Laboratory of Scientific and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huai Sun
- School of Chemistry and Chemical Engineering, Materials Genome Initiative Center, and Key Laboratory of Scientific and Engineering Computing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
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5
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Dommer A, Casalino L, Kearns F, Rosenfeld M, Wauer N, Ahn SH, Russo J, Oliveira S, Morris C, Bogetti A, Trifan A, Brace A, Sztain T, Clyde A, Ma H, Chennubhotla C, Lee H, Turilli M, Khalid S, Tamayo-Mendoza T, Welborn M, Christensen A, Smith DG, Qiao Z, Sirumalla SK, O'Connor M, Manby F, Anandkumar A, Hardy D, Phillips J, Stern A, Romero J, Clark D, Dorrell M, Maiden T, Huang L, McCalpin J, Woods C, Gray A, Williams M, Barker B, Rajapaksha H, Pitts R, Gibbs T, Stone J, Zuckerman DM, Mulholland AJ, Miller T, Jha S, Ramanathan A, Chong L, Amaro RE. #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2023; 37:28-44. [PMID: 36647365 PMCID: PMC9527558 DOI: 10.1177/10943420221128233] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.
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Affiliation(s)
| | | | | | | | | | | | - John Russo
- Oregon Health & Science University, Portland, OR, USA
| | | | | | | | - Anda Trifan
- Argonne National Laboratory, Lemont, IL, USA
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alexander Brace
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Terra Sztain
- UC San Diego, La Jolla, CA, USA
- Freie Universitat Berlin
| | - Austin Clyde
- Argonne National Laboratory, Lemont, IL, USA
- University of Chicago, Chicago, IL, USA
| | - Heng Ma
- Argonne National Laboratory, Lemont, IL, USA
| | | | - Hyungro Lee
- Brookhaven National Lab and Rutgers University
| | | | | | | | | | | | | | - Zhuoran Qiao
- California Institute of Technology, Pasadena, CA, USA
| | | | | | | | - Anima Anandkumar
- California Institute of Technology, Pasadena, CA, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | - David Hardy
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - James Phillips
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | | | | | | | - Tom Maiden
- Pittsburgh Supercomputing Center, Pittsburgh, PA, USA
| | - Lei Huang
- Texas Advanced Computing Center, Austin, TX, USA
| | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign, Urbana, IL, USA
- NVIDIA Corp, Santa Clara, CA, USA
| | | | | | - Thomas Miller
- Entos, Inc., San Diego, CA, USA
- California Institute of Technology, Pasadena, CA, USA
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6
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Dommer A, Casalino L, Kearns F, Rosenfeld M, Wauer N, Ahn SH, Russo J, Oliveira S, Morris C, Bogetti A, Trifan A, Brace A, Sztain T, Clyde A, Ma H, Chennubhotla C, Lee H, Turilli M, Khalid S, Tamayo-Mendoza T, Welborn M, Christensen A, Smith DGA, Qiao Z, Sirumalla SK, O'Connor M, Manby F, Anandkumar A, Hardy D, Phillips J, Stern A, Romero J, Clark D, Dorrell M, Maiden T, Huang L, McCalpin J, Woods C, Gray A, Williams M, Barker B, Rajapaksha H, Pitts R, Gibbs T, Stone J, Zuckerman D, Mulholland A, Miller T, Jha S, Ramanathan A, Chong L, Amaro R. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.11.12.468428. [PMID: 34816263 PMCID: PMC8609898 DOI: 10.1101/2021.11.12.468428] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. ACM REFERENCE FORMAT Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Anda Trifan
- Argonne National Laboratory
- University of Illinois at Urbana-Champaign
| | | | | | - Austin Clyde
- Argonne National Laboratory
- University of Chicago
| | | | | | - Hyungro Lee
- Brookhaven National Lab & Rutgers University
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Stone
- University of Illinois at Urbana-Champaign
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