Wheeler Annual Reports in Computational Chemistry

1. Auflage 2014
ISBN: 978-0-444-63397-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 10, 236 Seiten

Reihe: Annual Reports in Computational Chemistry

Annual Reports in Computational Chemistry provides timely and critical reviews of important topics in computational chemistry as applied to all chemical disciplines. Topics covered include quantum chemistry, molecular mechanics, force fields, chemical education, and applications in academic and industrial settings. Focusing on the most recent literature and advances in the field, each article covers a specific topic of importance to computational chemists. - Quantum chemistry - Molecular mechanics - Force fields - Chemical education and applications in academic and industrial settings

Autoren/Hrsg.

Wheeler, Ralph A.

Inhaltsverzeichnis

1;Front Cover;1
2;Annual Reports in Computational Chemistry;4
3;Copyright;5
4;Contents;6
5;Contributors;10
6;Preface;12
7;Section A: Quantum Chemistry;14
7.1;Chapter One: Quantum Chemistry Methods with Multiwavelet Bases on Massive Parallel Computers;16
7.1.1;1. Introduction;17
7.1.2;2. Concepts;20
7.1.2.1;2.1. Numerical Basis;20
7.1.2.2;2.2. Operators;22
7.1.2.3;2.3. High-Level Composition;23
7.1.3;3. Quantum Chemistry;25
7.1.4;4. Current Development;31
7.1.4.1;4.1. Boosting Performance for Distributed Computing;31
7.1.4.2;4.2. Norm-Conserving Pseudopotentials and Linear-Scaling Approaches;32
7.1.4.3;4.3. Perturbed Wavefunctions, Linear Response, and Time-Dependent Excitons;33
7.1.5;5. Summary;34
7.1.6;Acknowledgments;34
7.1.7;References;34
7.2;Chapter Two: On the Transferability of Three Water Models Developed by Adaptive Force Matching;38
7.2.1;1. Introduction;39
7.2.2;2. The Adaptive Force-Fitting Procedure;41
7.2.3;3. Three Water Models Developed by AFM;43
7.2.4;4. Computational Details;45
7.2.5;5. Results and Discussion;48
7.2.6;6. Conclusion;52
7.2.7;Acknowledgments;53
7.2.8;References;53
8;Section B: Biological Applications;58
8.1;Chapter Three: Seeing the Forest in Lieu of the Trees: Continuum Simulations of Cell Membranes at Large Length Scales;60
8.1.1;1. Introduction;61
8.1.2;2. General Formulation of the Model;62
8.1.2.1;2.1. Derivation of the Model in Real Space;62
8.1.2.2;2.2. Real-Space Implementation;63
8.1.2.3;2.3. Formulation in Fourier Space;65
8.1.2.4;2.4. Fourier Space Implementation;66
8.1.3;3. Application: Composition Fluctuations;71
8.1.3.1;3.1. Deriving the General Model;72
8.1.3.2;3.2. Results;74
8.1.3.3;3.3. Phase Diagram;74
8.1.3.3.1;3.3.1. Numeric Calculation;76
8.1.4;4. Application: Hybrid Models;79
8.1.4.1;4.1. General Elastic Model;79
8.1.4.2;4.2. Hybrid Membrane-Particle Model;81
8.1.5;5. Discussion and Outlook;85
8.1.6;Acknowledgments;86
8.1.7;References;86
8.2;Chapter Four: A Microscopic View of the Mechanisms of Active Transport Across the Cellular Membrane;90
8.2.1;1. Membrane Transport: A Universal Process in All Living Cells;91
8.2.2;2. Overview of Methodology;93
8.2.2.1;2.1. Molecular Dynamics;93
8.2.2.2;2.2. System Preparation for Membrane Protein Simulation;95
8.2.2.3;2.3. Nonequilibrium Simulations;96
8.2.3;3. Dynamical Description of Substrate-Binding Mechanism and Pathway;97
8.2.3.1;3.1. Spontaneous Substrate Binding in an Antiporter;98
8.2.3.2;3.2. Substrate Unbinding Pathway in a Na+/Sugar Symporter;102
8.2.3.3;3.3. MD-Based Homology Modeling of a Ligand-Bound Active Site;104
8.2.4;4. Local Conformational Changes in Alternating-Access Mechanism;107
8.2.4.1;4.1. Gating Mechanism Controlling Substrate Accessibility in a Neurotransmitter Transporter;108
8.2.4.2;4.2. Substrate-Induced Structural Changes in an Antiporter;110
8.2.5;5. Global Structural Transitions of Membrane Transporters;112
8.2.5.1;5.1. Structural Flexibility of ABC Exporters;113
8.2.5.2;5.2. Outward- to Inward-Facing State Transition in a Bacterial ABC Transporter;115
8.2.6;6. Formation of Transient Water-Conducting States in Membrane Transporters;119
8.2.7;7. Mechanistic Role for Water-Mediated H+ Pathways;123
8.2.8;8. Concluding Remarks;126
8.2.9;Acknowledgments;127
8.2.10;References;127
8.3;Chapter Five: Monte Carlo Simulation of Electrolyte Solutions in Biology: In and Out of Equilibrium;140
8.3.1;1. Introduction;141
8.3.2;2. Statistical Mechanical Background;142
8.3.3;3. Molecular Models;144
8.3.4;4. Simulation Methods;146
8.3.4.1;4.1. Molecular Dynamics;146
8.3.4.2;4.2. Langevin Dynamics;147
8.3.4.3;4.3. Metropolis Monte Carlo;147
8.3.4.4;4.4. Dynamic Monte Carlo;150
8.3.4.5;4.5. Grand Canonical Monte Carlo;150
8.3.4.6;4.6. Local Equilibrium Monte Carlo;153
8.3.5;5. Results;156
8.3.5.1;5.1. Bulk Electrolytes;156
8.3.5.2;5.2. Reduced Models of Calcium Channels;160
8.3.5.3;5.3. Selective Binding in the L-Type Ca Channel;162
8.3.5.4;5.4. Selective Transport in the RyR Ca Channel;165
8.3.6;6. Conclusions;167
8.3.7;Acknowledgments;168
8.3.8;References;168
9;SectionC: Chemical Education;178
9.1;Chapter Six: The Development and Implementation of a Biomolecular Docking Exercise for the General Chemistry Laboratory;180
9.1.1;1. Introduction;181
9.1.2;2. Methods;183
9.1.2.1;2.1. Components of the Exercise;183
9.1.2.2;2.2. List of Preloaded Ligands and Proteins;188
9.1.2.3;2.3. Assessment Tools;191
9.1.3;3. Results and Discussion;191
9.1.3.1;3.1. Pre- and Postexercise Knowledge Assessment;191
9.1.3.2;3.2. Student Assessment of the Exercise;195
9.1.4;4. Conclusions;197
9.1.5;Acknowledgments;199
9.1.6;References;199
10;Subject Index;202
11;Cumulative Index;208

Leseproben

Chapter Two On the Transferability of Three Water Models Developed by Adaptive Force Matching
Hongyi Hu; Zhonghua Ma; Feng Wang1    Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, Arkansas, USA
1 Corresponding author: email address: [e-mail] Abstract
Water is perhaps the most simulated liquid. Recently, three water models have been developed following the adaptive force matching (AFM) method that provides excellent predictions of water properties with only electronic structure information as a reference. Compared to many other electronic structure-based force fields that rely on fairly sophisticated energy expressions, the AFM water models use point-charge-based energy expressions that are supported by most popular molecular dynamics packages. An outstanding question regarding simple force fields is whether such force fields provide reasonable transferability outside of their conditions of parameterization. A survey of three AFM water models, B3LYPD-4F, BLYPSP-4F, and WAIL, are provided for simulations under conditions ranging from the melting point up to the critical point. By including ice-Ih configurations in the training set, the WAIL potential predicts the melting temperate, TM, of ice-Ih correctly. Without training for ice, BLYPSP-4F underestimates TM by about 15 K. Interestingly, the B3LYPD-4F model gives a TM 14 K too high. The overestimation of TM by B3LYPD-4F mostly likely reflects a deficiency of the B3LYP reference. The BLYPSP-4F model gives the best estimate of the boiling temperature TB and is arguably the best potential for simulating water in the temperature range from TM to TB. None of the three AFM potentials provides a good description of the critical point. Although the B3LYPD-4F model gives the correct critical temperature TC and critical density ?C, there are good reasons to believe that the agreement is reached fortuitously. Links to Gromacs input files for the three water models are provided at the end of the chapter. Keywords Force matching Critical point Surface tension Liquid–vapor Ab initio Force field Melting 1 Introduction
Water is ubiquitous and generally considered to be one of the most versatile liquids. It is not surprising that a significant amount of simulation has been done to investigate various properties of water. There are probably more potentials developed for water than any other liquids (1). Early models that gained significant popularity include SPC (2), SPC/E (3), TIP3P (4), and TIP4P (5). All of these models were developed by fitting to experimental properties. Of these, arguably, SPC/E and TIP4P are considered to be the most successful. In recent years, new members in the TIP4P family, such as TIP4P-Ew (6) and TIP4P-2005 (7), have been created and are generally believed to be more accurate than the earlier ones. Although maybe not as popular as experiment-based potentials, quite a few water models were developed by fitting to electronic structure calculations. Early electronic structure-based models, such as the Matsuoka, Clementi, Yoshimine (MCY) model (8), fail to predict several key properties, such as the density of water. Consequently, these models are not as widely used as experiment-based potentials. In recent years, electronic structure-based potentials, such as the Thole-Type Model (TTM) family (9,10), Distributed Point Polarizable (DPP) family (11,12), Huang, Braams, Bowman (HBB) family (13,14), and many others (15,16), are more sophisticated and accurate. However, these potentials are rather expensive to evaluate and have only limited support in public domain molecular dynamics (MD) packages. Although experiment-based potentials satisfactorily reproduce the most important properties, it is very hard to judge if a property is reproduced for the correct reason. Also, it is hard to determine if such potentials can reliably predict properties not being fit. In this sense, a potential fit only to electronic structure information is more robust in that if such a potential does reproduce an experimental property, it is more likely that such an agreement is obtained by correctly capturing the underlying physics. Recently, several water potentials were developed based on the adaptive force matching (AFM) approach (17–21). These water models were created by only fitting to electronic structure calculations. With AFM, the fit was done iteratively in the condensed phase. Obtaining reference forces in the condensed phase allows fitting of relative simple energy expressions that implicitly capture many-body effects. Only energy expressions that are supported by popular MD packages, such as Gromacs, were used in typical force fields developed by AFM (20–23). With simple point-charge-based energy expressions, the three water models investigated in this work require computational resources comparable to that of TIP4P for each force evaluation. It is worth mentioning that these models are generally a factor of two slower than TIP4P due to the requirement for smaller time steps. However, the use of deuterium to replace hydrogen in simulations alleviates this disadvantage. Although simple energy expressions lead to efficient force fields, they may limit the transferability of the potential. The philosophy of AFM is to fit a force field for a specific condition. This is achieved by including, in the training set, only reference configurations representative of the condition of interest. This is actually not very different from the development of some, if not most, experimental-based force fields, where only experimental properties under limited conditions were fit. For example, the TIP4P potential was fit only to properties at 1 atm and 25 °C (5). Nonetheless, these water models are frequently used under thermodynamic conditions not tested during parameterization (24–27). Several water models have been created based on AFM. Some of the models (20,21) were designed to be used with ab initio free energy perturbation theory (28,29). Three recent water models, B3LYPD-3F, BLYPSP-4F, and WAIL, offer similar performance and are capable of simulating the liquid states (17–19). The objective of this chapter is to investigate the performance of these three models outside of the thermodynamic conditions of parameterization. Under the conditions of parameterization, the AFM models have been found to be highly competitive with experiment-based potentials. For example, the WAIL potential designed for the modeling of ice and water gives a very good description of the melting temperature (TM) of ice and temperature of maximum density (TMD) of water. It is interesting to check if these models are better or worse than experimental-based potentials outside their “comfort zone.” These results should establish the applicability of these models as general purpose potentials for water. In order to accomplish this purpose, we investigate TM, diffusion constant (D), viscosity (?), surface tension (?), static dielectric constant (?s), TMD, boiling temperature (TB), critical temperature (TC), critical density (?C), and critical pressure (PC). In this chapter, we will provide a brief review of the AFM procedure in Section 2, and a brief summary of the three water models in Section 3. Computational details are reported in Section 4. Results and summary are presented in Section 5. Conclusion is given in Section 6. 2 The Adaptive Force-Fitting Procedure
AFM was designed to fit a force field to best reproduce electronic structure forces obtained under a particular thermodynamic condition or a set of thermodynamic conditions of interest. AFM requires an initial guess to the force field. From such a force field, a typical realization of AFM contains three steps as illustrated in Figure 2.1. Figure 2.1 Schematic diagram illustrating the steps in adaptive force fitting. The first step in AFM is the sampling step. In this step, the phase space associated with thermodynamic conditions of interest is traversed with a sampling algorithm, such as MD or Monte Carlo (MC). The guess force field will be used to integrate MD or MC trajectories. Configurations are randomly selected from the trajectories to form the training set. Standard sampling algorithms traverse the phase space according to the Boltzmann weight of each microstate. More important regions of the phase space are thus better represented in the...