ACS Publications. Most Trusted. Most Cited. Most Read
Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Theory Comput. 2015, 11, 8, 3714-3728
    Article

    Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins
    Click to copy article linkArticle link copied!

    View Author Information
    † ‡ †Department of Chemistry, ‡Laufer Center for Physical and Quantitative Biology, and §Department of Biochemistry, Stony Brook University, Stony Brook, New York 11794, USA
    *(C.S.) E-mail: [email protected]
    Other Access OptionsSupporting Information (1)

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2015, 11, 8, 3714–3728
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.5b00271
    Published July 7, 2015
    Copyright © 2015 American Chemical Society
    Request reuse permissions

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    The Generalized Born (GB) implicit solvent model has undergone significant improvements in accuracy for modeling of proteins and small molecules. However, GB still remains a less widely explored option for nucleic acid simulations, in part because fast GB models are often unable to maintain stable nucleic acid structures or they introduce structural bias in proteins, leading to difficulty in application of GB models in simulations of protein–nucleic acid complexes. Recently, GB-neck2 was developed to improve the behavior of protein simulations. In an effort to create a more accurate model for nucleic acids, a similar procedure to the development of GB-neck2 is described here for nucleic acids. The resulting parameter set significantly reduces absolute and relative energy error relative to Poisson–Boltzmann for both nucleic acids and nucleic acid–protein complexes, when compared to its predecessor GB-neck model. This improvement in solvation energy calculation translates to increased structural stability for simulations of DNA and RNA duplexes, quadruplexes, and protein–nucleic acid complexes. The GB-neck2 model also enables successful folding of small DNA and RNA hairpins to near native structures as determined from comparison with experiment. The functional form and all required parameters are provided here and also implemented in the AMBER software.

    Copyright © 2015 American Chemical Society

    Subjects

    what are subjects

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    Tables of training results, summaries of DNA, RNA, and protein/DNA complex systems used in this study, table of large errors for effective radii for original GB-neck model, and tables of major and minor groove width for various DNA and RNA systems. Figures of comparison between GB and PB calculation (perfect radii, energies) for various systems, figures of backbone RMSD vs time for various systems. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jctc.5b00271.

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    Click to copy section linkSection link copied!

    This article is cited by 53 publications.

    1. Craig A. Peeples, Ruibin Liu, Jana Shen. Force Field Limitations of All-Atom Continuous Constant pH Molecular Dynamics. The Journal of Physical Chemistry B 2024, 128 (47) , 11616-11624. https://doi.org/10.1021/acs.jpcb.4c05971
    2. Jason T. Linzer, Ethan Aminov, Aalim S. Abdullah, Colleen E. Kirkup, Rebeca I. Diaz Ventura, Vinay R. Bijoor, Jiyun Jung, Sophie Huang, Chi Gee Tse, Emily Álvarez Toucet, Hugo P. Onghai, Arghya P. Ghosh, Alex C. Grodzki, Emilee R. Haines, Aditya S. Iyer, Mark K. Khalil, Alexander P. Leong, Michael A. Neuhaus, Joseph Park, Asir Shahid, Matthew Xie, Jan M. Ziembicki, Carlos Simmerling, Maria C. Nagan. Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment. Journal of Chemical Information and Modeling 2024, 64 (15) , 6092-6104. https://doi.org/10.1021/acs.jcim.4c00756
    3. Xin Cao, Michelle H. Hummel, Yuzhang Wang, Carlos Simmerling, Evangelos A. Coutsias. Exact Analytical Algorithm for the Solvent-Accessible Surface Area and Derivatives in Implicit Solvent Molecular Simulations on GPUs. Journal of Chemical Theory and Computation 2024, 20 (11) , 4456-4468. https://doi.org/10.1021/acs.jctc.3c01366
    4. Yuanfeng Liu, Qixiang Li, Chongyu Shao, Yong She, Huifen Zhou, Yan Guo, Huiyan An, Ting Wang, Jiehong Yang, Haitong Wan. Exploring the Potential Mechanisms of Guanxinshutong Capsules in Treating Pathological Cardiac Hypertrophy based on Network Pharmacology, Computer-Aided Drug Design, and Animal Experiments. ACS Omega 2024, 9 (16) , 18083-18098. https://doi.org/10.1021/acsomega.3c10009
    5. Amaury Coste, Ema Slejko, Julija Zavadlav, Matej Praprotnik. Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media. Journal of Chemical Theory and Computation 2024, 20 (1) , 411-420. https://doi.org/10.1021/acs.jctc.3c00984
    6. Riccardo Rozza, Andrea Saltalamacchia, Clarissa Orrico, Pavel Janoš, Alessandra Magistrato. All-Atom Simulations Elucidate the Impact of U2AF2 Cancer-Associated Mutations on Pre-mRNA Recognition. Journal of Chemical Information and Modeling 2022, 62 (24) , 6691-6703. https://doi.org/10.1021/acs.jcim.2c00511
    7. Kye Won Wang, Jumin Lee, Han Zhang, Donghyuk Suh, Wonpil Im. CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs. The Journal of Physical Chemistry B 2022, 126 (38) , 7354-7364. https://doi.org/10.1021/acs.jpcb.2c05294
    8. Kenneth Lam, Koushik Kasavajhala, Sarah Gunasekera, Carlos Simmerling. Accelerating the Ensemble Convergence of RNA Hairpin Simulations with a Replica Exchange Structure Reservoir. Journal of Chemical Theory and Computation 2022, 18 (6) , 3930-3947. https://doi.org/10.1021/acs.jctc.2c00065
    9. Thomas J. Oweida, Ho Shin Kim, Johnny M. Donald, Abhishek Singh, Yaroslava G. Yingling. Assessment of AMBER Force Fields for Simulations of ssDNA. Journal of Chemical Theory and Computation 2021, 17 (2) , 1208-1217. https://doi.org/10.1021/acs.jctc.0c00931
    10. Upendra Adhikari, Barmak Mostofian, Jeremy Copperman, Sundar Raman Subramanian, Andrew A. Petersen, Daniel M. Zuckerman. Computational Estimation of Microsecond to Second Atomistic Folding Times. Journal of the American Chemical Society 2019, 141 (16) , 6519-6526. https://doi.org/10.1021/jacs.8b10735
    11. Sameer Sajja, Morgan Chandler, Dmitry Fedorov, Wojciech K. Kasprzak, Alexander Lushnikov, Mathias Viard, Ankit Shah, Dylan Dang, Jared Dahl, Beamlak Worku, Marina A. Dobrovolskaia, Alexey Krasnoslobodtsev, Bruce A. Shapiro, Kirill A. Afonin. Dynamic Behavior of RNA Nanoparticles Analyzed by AFM on a Mica/Air Interface. Langmuir 2018, 34 (49) , 15099-15108. https://doi.org/10.1021/acs.langmuir.8b00105
    12. Elijah L. Taylor, Kevin J. Metcalf, Benedetta Carlotti, Cheng-Tsung Lai, Justin A. Modica, George C. Schatz, Milan Mrksich, Theodore Goodson, III. Long-Range Energy Transfer in Protein Megamolecules. Journal of the American Chemical Society 2018, 140 (46) , 15731-15743. https://doi.org/10.1021/jacs.8b08208
    13. Saeed Izadi, Robert C. Harris, Marcia O. Fenley, Alexey V. Onufriev. Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies. Journal of Chemical Theory and Computation 2018, 14 (3) , 1656-1670. https://doi.org/10.1021/acs.jctc.7b00886
    14. Negin Forouzesh, Saeed Izadi, and Alexey V. Onufriev . Grid-Based Surface Generalized Born Model for Calculation of Electrostatic Binding Free Energies. Journal of Chemical Information and Modeling 2017, 57 (10) , 2505-2513. https://doi.org/10.1021/acs.jcim.7b00192
    15. Andrew V. Yeager, Jason M. Swails, and Bill R. Miller, III . Improved Accuracy for Constant pH-REMD Simulations through Modification of Carboxylate Effective Radii. Journal of Chemical Theory and Computation 2017, 13 (10) , 4624-4635. https://doi.org/10.1021/acs.jctc.7b00638
    16. Irene Maffucci and Alessandro Contini . Improved Computation of Protein–Protein Relative Binding Energies with the Nwat-MMGBSA Method. Journal of Chemical Information and Modeling 2016, 56 (9) , 1692-1704. https://doi.org/10.1021/acs.jcim.6b00196
    17. Fan Bu, Yagoub Adam, Ryszard W. Adamiak, Maciej Antczak, Belisa Rebeca H. de Aquino, Nagendar Goud Badepally, Robert T. Batey, Eugene F. Baulin, Pawel Boinski, Michal J. Boniecki, Janusz M. Bujnicki, Kristy A. Carpenter, Jose Chacon, Shi-Jie Chen, Wah Chiu, Pablo Cordero, Naba Krishna Das, Rhiju Das, Wayne K. Dawson, Frank DiMaio, Feng Ding, Anne-Catherine Dock-Bregeon, Nikolay V. Dokholyan, Ron O. Dror, Stanisław Dunin-Horkawicz, Stephan Eismann, Eric Ennifar, Reza Esmaeeli, Masoud Amiri Farsani, Adrian R. Ferré-D’Amaré, Caleb Geniesse, George E. Ghanim, Horacio V. Guzman, Iris V. Hood, Lin Huang, Dharm Skandh Jain, Farhang Jaryani, Lei Jin, Astha Joshi, Masha Karelina, Jeffrey S. Kieft, Wipapat Kladwang, Sebastian Kmiecik, Deepak Koirala, Markus Kollmann, Rachael C. Kretsch, Mateusz Kurciński, Jun Li, Shuang Li, Marcin Magnus, BenoÎt Masquida, S. Naeim Moafinejad, Arup Mondal, Sunandan Mukherjee, Thi Hoang Duong Nguyen, Grigory Nikolaev, Chandran Nithin, Grace Nye, Iswarya P. N. Pandaranadar Jeyeram, Alberto Perez, Phillip Pham, Joseph A. Piccirilli, Smita Priyadarshini Pilla, Radosław Pluta, Simón Poblete, Almudena Ponce-Salvatierra, Mariusz Popenda, Lukasz Popenda, Fabrizio Pucci, Ramya Rangan, Angana Ray, Aiming Ren, Joanna Sarzynska, Congzhou Mike Sha, Filip Stefaniak, Zhaoming Su, Krishna C. Suddala, Marta Szachniuk, Raphael Townshend, Robert J. Trachman, Jian Wang, Wenkai Wang, Andrew Watkins, Tomasz K. Wirecki, Yi Xiao, Peng Xiong, Yiduo Xiong, Jianyi Yang, Joseph David Yesselman, Jinwei Zhang, Yi Zhang, Zhenzhen Zhang, Yuanzhe Zhou, Tomasz Zok, Dong Zhang, Sicheng Zhang, Adriana Żyła, Eric Westhof, Zhichao Miao. RNA-Puzzles Round V: blind predictions of 23 RNA structures. Nature Methods 2024, 18 https://doi.org/10.1038/s41592-024-02543-9
    18. Azam Nesabi, Jas Kalayan, Sara Al-Rawashdeh, Mohammad A. Ghattas, Richard A. Bryce. Molecular dynamics simulations as a guide for modulating small molecule aggregation. Journal of Computer-Aided Molecular Design 2024, 38 (1) https://doi.org/10.1007/s10822-024-00557-1
    19. Pablo R Arantes, Xiaoyu Chen, Souvik Sinha, Aakash Saha, Amun C Patel, Matthew Sample, Łukasz Nierzwicki, Audrone Lapinaite, Giulia Palermo. Dimerization of the deaminase domain and locking interactions with Cas9 boost base editing efficiency in ABE8e. Nucleic Acids Research 2024, 379 https://doi.org/10.1093/nar/gkae1066
    20. Xiaohui Wang, Huaiyu Yang, Mao Wang, Zhe Huai, Zhaoxi Sun. Virtual screening of cucurbituril host-guest complexes: Large-scale benchmark of end-point protocols under MM and QM Hamiltonians. Journal of Molecular Liquids 2024, 407 , 125245. https://doi.org/10.1016/j.molliq.2024.125245
    21. Konstantin Röder, Samuela Pasquali. Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent. Biophysical Reviews 2024, 16 (3) , 285-295. https://doi.org/10.1007/s12551-024-01202-9
    22. Jiarui Li, Xincheng Kang, Irene Guidi, Lu Lu, Pablo Fernández-Millán, Guillem Prats-Ejarque, Ester Boix. Structural determinants for tRNA selective cleavage by RNase 2/EDN. Structure 2024, 32 (3) , 328-341.e4. https://doi.org/10.1016/j.str.2023.12.012
    23. Xiaohui Wang, Mao Wang, Zhaoxi Sun. Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-Guest Binding: IV. The QM Treatment, GB Models and the Multi-Trajectory Extension. Liquids 2023, 3 (4) , 426-439. https://doi.org/10.3390/liquids3040027
    24. Thomas J. Oweida, Yaroslava G. Yingling. Resolving Structure of ssDNA in Solution by Fusing Molecular Simulations and Scattering Experiments with Machine Learning. Advanced Theory and Simulations 2023, 6 (12) https://doi.org/10.1002/adts.202300411
    25. Rae A. Corrigan, Andrew C. Thiel, Jack R. Lynn, Thomas L. Casavant, Pengyu Ren, Jay W. Ponder, Michael J. Schnieders. A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model. The Journal of Chemical Physics 2023, 159 (5) https://doi.org/10.1063/5.0158914
    26. Reza Esmaeeli, Antonio Bauzá, Alberto Perez. Structural predictions of protein–DNA binding: MELD-DNA. Nucleic Acids Research 2023, 51 (4) , 1625-1636. https://doi.org/10.1093/nar/gkad013
    27. Jiebin Huang, Weiwei Lin, Yuxing Sun, Qian Wang, Shidian He, Zhihua Han, Lixing Lu, Xueran Kang, Yisheng Chen, Haoran Guo, Zhiyong Cui, Chenyu Sun, Ken Go, Junyi Wu, Mengxuan Yao, Mingfeng Cao, Yuzhen Xu. Quercetin targets VCAM1 to prevent diabetic cerebrovascular endothelial cell injury. Frontiers in Aging Neuroscience 2022, 14 https://doi.org/10.3389/fnagi.2022.944195
    28. Reza Esmaeeli, Benedict Andal, Alberto Perez. Searching for Low Probability Opening Events in a DNA Sliding Clamp. Life 2022, 12 (2) , 261. https://doi.org/10.3390/life12020261
    29. David van der Spoel, Jin Zhang, Haiyang Zhang. Quantitative predictions from molecular simulations using explicit or implicit interactions. WIREs Computational Molecular Science 2022, 12 (1) https://doi.org/10.1002/wcms.1560
    30. George Watson, Victor Velasco-Berrelleza, Agnes Noy. Atomistic Molecular Dynamics Simulations of DNA in Complex 3D Arrangements for Comparison with Lower Resolution Structural Experiments. 2022, 95-109. https://doi.org/10.1007/978-1-0716-2221-6_8
    31. George D. Watson, Elliot W. Chan, Mark C. Leake, Agnes Noy. Structural interplay between DNA-shape protein recognition and supercoiling: The case of IHF. Computational and Structural Biotechnology Journal 2022, 20 , 5264-5274. https://doi.org/10.1016/j.csbj.2022.09.020
    32. Samuel B Yoshua, George D Watson, Jamieson A L Howard, Victor Velasco-Berrelleza, Mark C Leake, Agnes Noy. Integration host factor bends and bridges DNA in a multiplicity of binding modes with varying specificity. Nucleic Acids Research 2021, 49 (15) , 8684-8698. https://doi.org/10.1093/nar/gkab641
    33. Mariya Edeleva, Paul H.M. Van Steenberge, Maarten K. Sabbe, Dagmar R. D’hooge. Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art. Polymers 2021, 13 (18) , 3027. https://doi.org/10.3390/polym13183027
    34. Yuichiro Yoshida, Satoru Iuchi, Hirofumi Sato. A quantum chemical model for a series of self-assembled nanocages: the origin of stability behind the coordination-driven formation of transition metal complexes up to [M 12 L 24 ] 24+. Physical Chemistry Chemical Physics 2021, 23 (2) , 866-877. https://doi.org/10.1039/D0CP04755D
    35. Hamid Hadi-Alijanvand. Soft regions of protein surface are potent for stable dimer formation. Journal of Biomolecular Structure and Dynamics 2020, 38 (12) , 3587-3598. https://doi.org/10.1080/07391102.2019.1662328
    36. Peng Tao, Yi Xiao. Using the generalized Born surface area model to fold proteins yields more effective sampling while qualitatively preserving the folding landscape. Physical Review E 2020, 101 (6) https://doi.org/10.1103/PhysRevE.101.062417
    37. Dustin C Woods, Jeff Wereszczynski. Elucidating the influence of linker histone variants on chromatosome dynamics and energetics. Nucleic Acids Research 2020, 48 (7) , 3591-3604. https://doi.org/10.1093/nar/gkaa121
    38. Jack W Shepherd, Robert J Greenall, Matt I J Probert, Agnes Noy, Mark C Leake. The emergence of sequence-dependent structural motifs in stretched, torsionally constrained DNA. Nucleic Acids Research 2020, 48 (4) , 1748-1763. https://doi.org/10.1093/nar/gkz1227
    39. Lorenzo Pallante, Antonio Rocca, Greta Klejborowska, Adam Huczynski, Gianvito Grasso, Jack A. Tuszynski, Marco A. Deriu. In silico Investigations of the Mode of Action of Novel Colchicine Derivatives Targeting β-Tubulin Isotypes: A Search for a Selective and Specific β-III Tubulin Ligand. Frontiers in Chemistry 2020, 8 https://doi.org/10.3389/fchem.2020.00108
    40. Paul Zakrevsky, Wojciech K. Kasprzak, William F. Heinz, Weimin Wu, Htet Khant, Eckart Bindewald, Nomongo Dorjsuren, Eric A. Fields, Natalia de Val, Luc Jaeger, Bruce A. Shapiro. Truncated tetrahedral RNA nanostructures exhibit enhanced features for delivery of RNAi substrates. Nanoscale 2020, 12 (4) , 2555-2568. https://doi.org/10.1039/C9NR08197F
    41. Christina Bergonzo, Alexander Grishaev. Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data. Journal of Structural Biology 2019, 207 (3) , 250-259. https://doi.org/10.1016/j.jsb.2019.07.001
    42. Chao Wu, Xiaolu Chen, Daoxing Chen, Qinqin Xia, Zhiguo Liu, Fuchuan Li, Yuxiang Yan, Yuepiao Cai. Insight into ponatinib resistance mechanisms in rhabdomyosarcoma caused by the mutations in FGFR4 tyrosine kinase using molecular modeling strategies. International Journal of Biological Macromolecules 2019, 135 , 294-302. https://doi.org/10.1016/j.ijbiomac.2019.05.138
    43. Alexey V. Onufriev, David A. Case. Generalized Born Implicit Solvent Models for Biomolecules. Annual Review of Biophysics 2019, 48 (1) , 275-296. https://doi.org/10.1146/annurev-biophys-052118-115325
    44. Ruth Nussinov, Chung-Jung Tsai, Amarda Shehu, Hyunbum Jang. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019, 24 (3) , 637. https://doi.org/10.3390/molecules24030637
    45. Xavier Bofill-De Ros, Wojciech K. Kasprzak, Yuba Bhandari, Lixin Fan, Quinn Cavanaugh, Minjie Jiang, Lisheng Dai, Acong Yang, Tie-Juan Shao, Bruce A. Shapiro, Yun-Xing Wang, Shuo Gu. Structural Differences between Pri-miRNA Paralogs Promote Alternative Drosha Cleavage and Expand Target Repertoires. Cell Reports 2019, 26 (2) , 447-459.e4. https://doi.org/10.1016/j.celrep.2018.12.054
    46. Matthias Schickinger, Martin Zacharias, Hendrik Dietz. Tethered multifluorophore motion reveals equilibrium transition kinetics of single DNA double helices. Proceedings of the National Academy of Sciences 2018, 115 (32) https://doi.org/10.1073/pnas.1800585115
    47. Chao Wu, Liguang Mao, Xie Huang, Weitao Fu, Xiong Cai, Yuepiao Cai, Liqun Shen, Xiaoxia Ye. Theoretical study of the intermolecular recognition mechanism between Survivin and substrate based on conserved binding mode analysis. Journal of Molecular Graphics and Modelling 2018, 83 , 53-63. https://doi.org/10.1016/j.jmgm.2018.05.001
    48. Igor S. Tolokh, Dennis G. Thomas, Alexey V. Onufriev. Explicit ions/implicit water generalized Born model for nucleic acids. The Journal of Chemical Physics 2018, 148 (19) https://doi.org/10.1063/1.5027260
    49. Alexey V. Onufriev, Saeed Izadi. Water models for biomolecular simulations. WIREs Computational Molecular Science 2018, 8 (2) https://doi.org/10.1002/wcms.1347
    50. Gregory A. Babbitt, Erin E. Coppola, Jamie S. Mortensen, Patrick X. Ekeren, Cosmo Viola, Dallan Goldblatt, André O. Hudson. Triplet-Based Codon Organization Optimizes the Impact of Synonymous Mutation on Nucleic Acid Molecular Dynamics. Journal of Molecular Evolution 2018, 86 (2) , 91-102. https://doi.org/10.1007/s00239-018-9828-x
    51. My-Tra Le, Wojciech K Kasprzak, Taejin Kim, Feng Gao, Megan YL Young, Xuefeng Yuan, Bruce A Shapiro, Joonil Seog, Anne E Simon. Folding behavior of a T-shaped, ribosome-binding translation enhancer implicated in a wide-spread conformational switch. eLife 2017, 6 https://doi.org/10.7554/eLife.22883
    52. Taejin Kim, Wojciech K. Kasprzak, Bruce A. Shapiro. Protocols for Molecular Dynamics Simulations of RNA Nanostructures. 2017, 33-64. https://doi.org/10.1007/978-1-4939-7138-1_3
    53. Dongsheng Lei, Matthew Rames, Xing Zhang, Lei Zhang, Shengli Zhang, Gang Ren. Insights into the Tunnel Mechanism of Cholesteryl Ester Transfer Protein through All-atom Molecular Dynamics Simulations. Journal of Biological Chemistry 2016, 291 (27) , 14034-14044. https://doi.org/10.1074/jbc.M116.715565
    Download PDF
    Go to Journal of Chemical Theory and Computation
    Get e-Alerts
    Get e-Alerts

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2015, 11, 8, 3714–3728
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.5b00271
    Published July 7, 2015
    Copyright © 2015 American Chemical Society
    Request reuse permissions

    Article Views

    1545

    Altmetric

    -

    Citations

    53
    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.