Constructing Potential Energy Surface with Correlated Theory for Dipeptides Using Molecular Tailoring Approach.
| Title: | Constructing Potential Energy Surface with Correlated Theory for Dipeptides Using Molecular Tailoring Approach. |
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| Authors: | Khire SS; RIKEN Center for Computational Science, Kobe, 650-0047, Japan.; Department of Scientific Computing Modelling and Simulation, Savitribai Phule Pune University, Pune, 411 007, India.; Gattadahalli N; Department of Scientific Computing Modelling and Simulation, Savitribai Phule Pune University, Pune, 411 007, India.; Gurav ND; Department of Scientific Computing Modelling and Simulation, Savitribai Phule Pune University, Pune, 411 007, India.; Organisch-Chemisches Institut and Center for Multiscale Theory and Computation (CMTC), Westfälische Wilhelms-Universität Münster, Corrensstrasse 36, 48149, Münster, Germany.; Kumar A; School of Pharmacy, University of Maryland, Baltimore, 20 Penn Street, HSFII, Baltimore, Maryland, 21201, U.S.A.; Gadre SR; Department of Scientific Computing Modelling and Simulation, Savitribai Phule Pune University, Pune, 411 007, India. |
| Source: | Chemphyschem : a European journal of chemical physics and physical chemistry [Chemphyschem] 2023 May 16; Vol. 24 (10), pp. e202200784. Date of Electronic Publication: 2023 Mar 09. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Wiley-VCH Verlag Country of Publication: Germany NLM ID: 100954211 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1439-7641 (Electronic) Linking ISSN: 14394235 NLM ISO Abbreviation: Chemphyschem Subsets: MEDLINE; PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Weinheim, Germany : Wiley-VCH Verlag, c2000- |
| Abstract: | We demonstrate a cost-effective alternative employing the fragment-based molecular tailoring approach (MTA) for building the potential energy surface (PES) for two dipeptides viz. alanine-alanine and alanine-proline employing correlated theory, with augmented Dunning basis sets. About 1369 geometries are generated for each test dipeptide by systematically varying the dihedral angles INLINEMATH and INLINEMATH . These conformational geometries are partially optimized by relaxing all the other Z-matrix parameters, fixing the values of INLINEMATH and INLINEMATH . The MP2 level PES is constructed from the MTA-energies of chemically intact geometries using minimal hardware. The fidelity of MP2/aug-cc-pVDZ level PES is brought out by comparing it with its full calculation counterpart. Further, we bring out the power of the method by reporting the MTA-based CCSD/aug-cc-pVDZ level PES for these two dipeptides containing 498 and 562 basis functions respectively.; (© 2023 Wiley-VCH GmbH.) |
| References: | J. Jungwirth, J. Šebestík, M. Šafařík, J. Kapitán, P. Bouř, J. Phys. Chem. B 2017, 121, 8956.; F. Zhang, N. Adnani, E. Vazquez-Rivera, D. R. Braun, M. Tonelli, D. R. Andes, T. S. Bugni, J. Org. Chem. 2015, 80, 8713.; A. N. Drozdov, A. Grossfield, R. V. Pappu, J. Am. Chem. Soc. 2004, 126, 2574.; G. Ramachandran, C. Ramakrishnan, V. Sasisekharan, J. Mol. Biol. 1963, 7, 95.; S. Ołdziej, U. Kozłowska, A. Liwo, H. A. Scheraga, J. Phys. Chem. A 2003, 107, 8035.; R. Vargas, J. Garza, B. P. Hay, D. A. Dixon, J. Phys. Chem. A 2002, 106, 3213.; M. Elstner, K. Jalkanen, M. Knapp-Mohammady, T. Frauenheim, S. Suhai, Chem. Phys. 2000, 256, 15.; A. Q. Zhou, C. S. O'Hern, L. Regan, Protein Sci. 2011, 20, 1166.; V. Parchaňský, J. Kapitán, J. Kaminský, J. Šebestík, P. Bouř, J. Phys. Chem. Lett. 2013, 4, 2763.; Z.-X. Wang, Y. Duan, J. Comput. Chem. 2004, 25, 1699.; S. Mondal, D. S. Chowdhuri, S. Ghosh, A. Misra, S. Dalai, J. Mol. Struct. 2007, 810, 81.; V. Mironov, Y. Alexeev, V. K. Mulligan, D. G. Fedorov, J. Comput. Chem. 2019, 40, 297.; Y. K. Kang, H. S. Park, Chem. Phys. Lett. 2014, 600, 112.; M. Ropo, M. Schneider, C. Baldauf, V. Blum, Sci. Data 2016, 3, 2052.; P. L. Freddolino, F. Liu, M. Gruebele, K. Schulten, Biophys. J. 2008, 94, L75.; J. Huang, S. Rauscher, G. Nawrocki, T. Ran, M. Feig, B. L. de Groot, H. Grubmüller, A. D. MacKerell, Nat. Methods 2017, 14, 71-73.; A. D. MacKerell, M. Feig, C. L. Brooks, J. Am. Chem. Soc. 2004, 126, 698.; P. E. M. Lopes, J. Huang, J. Shim, Y. Luo, H. Li, B. Roux, A. D. MacKerell, J. Chem. Theory Comput. 2013, 9, 5430.; A. Szabo, N. S. Ostlund, Morden Quantum Chemistry, McGraw-Hill, New York 1989.; K. Kitaura, E. Ikeo, T. Asada, T. Nakano, M. Uebayasi, Chem. Phys. Lett. 1999, 313, 701.; S. R. Gadre, R. N. Shirsat, A. C. Limaye, J. Phys. Chem. 1994, 98, 9165.; M. A. Collins, M. W. Cvitkovic, R. P. A. Bettens, Acc. Chem. Res. 2014, 47, 2776.; S. Li, W. Li, T. Fang, J. Am. Chem. Soc. 2005, 127, 7215.; M. S. Gordon, D. G. Fedorov, S. R. Pruitt, L. V. Slipchenko, Chem. Rev. 2012, 112, 632.; K. Raghavachari, A. Saha, Chem. Rev. 2015, 115, 5643.; M. A. Collins, R. P. A. Bettens, Chem. Rev. 2015, 115, 5607.; S. S. Khire, N. D. Gurav, A. Nandi, S. R. Gadre, J. Phys. Chem. A 2022, 126, 1458.; N. Sahu, S. R. Gadre, Acc. Chem. Res. 2014, 47, 2739.; K. Babu, S. R. Gadre, J. Comput. Chem. 2003, 24, 484.; S. R. Gadre, V. Ganesh, J. Theor. Comput. Chem. 2006, 05, 835.; A. P. Rahalkar, V. Ganesh, S. R. Gadre, J. Chem. Phys. 2008, 129, 234101.; V. Ganesh, R. K. Dongare, P. Balanarayan, S. R. Gadre, J. Chem. Phys. 2006, 125, 104109.; S. R. Gadre, S. D. Yeole, N. Sahu, Chem. Rev. 2014, 114, 12132.; N. Sahu, S. S. Khire, S. R. Gadre, Mol. Phys. 2015, 113, 2970.; S. S. Khire, N. Sahu, S. R. Gadre, Comput. Phys. Commun. 2022, 270, 108175.; M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. V. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. J. Bearpark, J. J. Heyd, E. N. Brothers, K. N. Kudin, V. N. Staroverov, T. A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. P. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, D. J. Fox, Gaussian∼16 Revision C.01 2016, gaussian Inc. Wallingford CT.; M. W. Schmidt, K. K. Baldridge, J. A. Boatz, S. T. Elbert, M. S. Gordon, J. H. Jensen, S. Koseki, N. Matsunaga, K. A. Nguyen, S. Su, T. L. Windus, M. Dupuis, J. A. Montgomery Jr, J. Comput. Chem. 1993, 14, 1347.; S. S. Khire, L. J. Bartolotti, S. R. Gadre, J. Chem. Phys. 2018, 149, 064112.; V. Ganesh, J. Comput. Chem. 2009, 30, 661.; P. Virtanen, R. Gommers, T. E. Oliphant et al., Nat. Methods 2020, 17, 261.; J. Hunter, Comput. Sci. Eng. 2007, 9, 90. |
| Contributed Indexing: | Keywords: Ramachandran plots; dipeptides; fragmentation method; molecular tailoring approach; potential energy surfaces |
| Entry Date(s): | Date Created: 20230203 Date Completed: 20230516 Latest Revision: 20230516 |
| Update Code: | 20260130 |
| DOI: | 10.1002/cphc.202200784 |
| PMID: | 36735449 |
| Database: | MEDLINE |
Journal Article