Showing papers in "Journal of Computational Chemistry in 2009"
TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.
Abstract: AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
20,059 citations
TL;DR: AutoDock4 incorporates limited flexibility in the receptor and its utility in analysis of covalently bound ligands is reported, using both a grid‐based docking method and a modification of the flexible sidechain technique.
Abstract: We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.
15,616 citations
TL;DR: An overview of the CHARMM program as it exists today is provided with an emphasis on developments since the publication of the original CHARMM article in 1983.
Abstract: CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.
7,035 citations
TL;DR: This work has developed a code able to pack millions of atoms, grouped in arbitrarily complex molecules, inside a variety of three‐dimensional regions, which can be intersections of spheres, ellipses, cylinders, planes, or boxes.
Abstract: Adequate initial configurations for molecular dynamics simulations consist of arrangements of molecules distributed in space in such a way to approximately represent the system's overall structure. In order that the simulations are not disrupted by large van der Waals repulsive interactions, atoms from different molecules must keep safe pairwise distances. Obtaining such a molecular arrangement can be considered a packing problem: Each type molecule must satisfy spatial constraints related to the geometry of the system, and the distance between atoms of different molecules must be greater than some specified tolerance. We have developed a code able to pack millions of atoms, grouped in arbitrarily complex molecules, inside a variety of three-dimensional regions. The regions may be intersections of spheres, ellipses, cylinders, planes, or boxes. The user must provide only the structure of one molecule of each type and the geometrical constraints that each type of molecule must satisfy. Building complex mixtures, interfaces, solvating biomolecules in water, other solvents, or mixtures of solvents, is straightforward. In addition, different atoms belonging to the same molecule may also be restricted to different spatial regions, in such a way that more ordered molecular arrangements can be built, as micelles, lipid double-layers, etc. The packing time for state-of-the-art molecular dynamics systems varies from a few seconds to a few minutes in a personal computer. The input files are simple and currently compatible with PDB, Tinker, Molden, or Moldy coordinate files. The package is distributed as free software and can be downloaded from http://www.ime.unicamp.br/~martinez/packmol/.
5,322 citations
TL;DR: An extension of the CHARMM force field to drug‐like molecules is presented, making it possible to perform “all‐CHARMM” simulations on drug‐target interactions thereby extending the utility ofCHARMM force fields to medicinally relevant systems.
Abstract: The widely used CHARMM additive all-atom force field includes parameters for proteins, nucleic acids, lipids, and carbohydrates. In the present article, an extension of the CHARMM force field to drug-like molecules is presented. The resulting CHARMM General Force Field (CGenFF) covers a wide range of chemical groups present in biomolecules and drug-like molecules, including a large number of heterocyclic scaffolds. The parametrization philosophy behind the force field focuses on quality at the expense of transferability, with the implementation concentrating on an extensible force field. Statistics related to the quality of the parametrization with a focus on experimental validation are presented. Additionally, the parametrization procedure, described fully in the present article in the context of the model systems, pyrrolidine, and 3-phenoxymethylpyrrolidine will allow users to readily extend the force field to chemical groups that are not explicitly covered in the force field as well as add functional groups to and link together molecules already available in the force field. CGenFF thus makes it possible to perform "all-CHARMM" simulations on drug-target interactions thereby extending the utility of CHARMM force fields to medicinally relevant systems.
4,553 citations
TL;DR: By applying the DFT‐D/plane‐wave approach a substantial agreement with experiments is found for the structure and energetics of polyethylene and graphite, two typical solids that are badly described by standard local and semilocal density functionals.
Abstract: A semiempirical addition of dispersive forces to conventional density functionals (DFT-D) has been implemented into a pseudopotential plane-wave code. Test calculations on the benzene dimer reproduced the results obtained by using localized basis set, provided that the latter are corrected for the basis set superposition error. By applying the DFT-D/plane-wave approach a substantial agreement with experiments is found for the structure and energetics of polyethylene and graphite, two typical solids that are badly described by standard local and semilocal density functionals. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
660 citations
TL;DR: It is found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy.
Abstract: In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM-PBSA and MM-GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein-ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy-minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein-ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM-PBSA or MM-GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein-ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves.
576 citations
TL;DR: A complete implementation of all‐atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent is described.
Abstract: We describe a complete implementation of all-atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it can be more than 700 times faster than a conventional implementation running on a single CPU core.
547 citations
TL;DR: This overview of the available techniques and how they can be best used in a practical context is aimed at helping the reader choose the most appropriate combination of approaches for the biomolecular system, Hamiltonian and free energy difference of interest.
Abstract: Methods to compute free energy differences between different states of a molecular system are reviewed with the aim of identifying their basic ingredients and their utility when applied in practice to biomolecular systems. A free energy calculation is comprised of three basic components: (i) a suitable model or Hamiltonian, (ii) a sampling protocol with which one can generate a representative ensemble of molecular configurations, and (iii) an estimator of the free energy difference itself. Alternative sampling protocols can be distinguished according to whether one or more states are to be sampled. In cases where only a single state is considered, six alternative techniques could be distinguished: (i) changing the dynamics, (ii) deforming the energy surface, (iii) extending the dimensionality, (iv) perturbing the forces, (v) reducing the number of degrees of freedom, and (vi) multi-copy approaches. In cases where multiple states are to be sampled, the three primary techniques are staging, importance sampling, and adiabatic decoupling. Estimators of the free energy can be classified as global methods that either count the number of times a given state is sampled or use energy differences. Or, they can be classified as local methods that either make use of the force or are based on transition probabilities. Finally, this overview of the available techniques and how they can be best used in a practical context is aimed at helping the reader choose the most appropriate combination of approaches for the biomolecular system, Hamiltonian and free energy difference of interest. © 2009 Wiley Periodicals, Inc.
352 citations
TL;DR: The tests reveal that the computational expense for simulations with the ABSINTH implicit solvation model increase by a factor that is in the range of 2.5–5.0 with respect to gas‐phase calculations.
Abstract: A new implicit solvation model for use in Monte Carlo simulations of polypeptides is introduced. The model is termed ABSINTH for self-Assembly of Biomolecules Studied by an Implicit, Novel, and Tunable Hamiltonian. It is designed primarily for simulating conformational equilibria and oligomerization reactions of intrinsically disordered proteins in aqueous solutions. The paradigm for ABSINTH is conceptually similar to the EEF1 model of Lazaridis and Karplus (Proteins 1999, 35, 133). In ABSINTH, the transfer of a polypeptide solute from the gas phase into a continuum solvent is the sum of a direct mean field interaction (DMFI), and a term to model the screening of polar interactions. Polypeptide solutes are decomposed into a set of distinct solvation groups. The DMFI is a sum of contributions from each of the solvation groups, which are analogs of model compounds. Continuum-mediated screening of electrostatic interactions is achieved using a framework similar to the one used for the DMFI. Promising results are shown for a set of test cases. These include the calculation of NMR coupling constants for short peptides, the assessment of the thermal stability of two small proteins, reversible folding of both an alpha-helix and a beta-hairpin forming peptide, and the polymeric properties of intrinsically disordered polyglutamine peptides of varying lengths. The tests reveal that the computational expense for simulations with the ABSINTH implicit solvation model increase by a factor that is in the range of 2.5-5.0 with respect to gas-phase calculations.
321 citations
TL;DR: The efficiency of the reweighting procedure is shown by reconstructing the distribution of the four backbone dihedral angles of alanine dipeptide from two and even one dimensional metadynamics simulation.
Abstract: Metadynamics is a widely used and successful method for reconstructing the free-energy surface of complex systems as a function of a small number of suitably chosen collective variables. This is achieved by biasing the dynamics of the system. The bias acting on the collective variables distorts the probability distribution of the other variables. Here we present a simple reweighting algorithm for recovering the unbiased probability distribution of any variable from a well-tempered metadynamics simulation. We show the efficiency of the reweighting procedure by reconstructing the distribution of the four backbone dihedral angles of alanine dipeptide from two and even one dimensional metadynamics simulation. © 2009 Wiley Periodicals, Inc. J Comput Chem 2009
TL;DR: GridMAT‐MD is a new program developed to aid in the analysis of lipid bilayers from molecular dynamics simulations, which reads a GROMACS coordinate file and generates two types of data: a two‐dimensional contour plot depicting membrane thickness, and a polygon‐based tessellation of the individual lipid headgroups.
Abstract: GridMAT-MD is a new program developed to aid in the analysis of lipid bilayers from molecular dynamics simulations. It reads a GROMACS coordinate file and generates two types of data: a two-dimensional contour plot depicting membrane thickness, and a polygon-based tessellation of the individual lipid headgroups. GridMAT-MD can also account for proteins or small molecules within the headgroups of the lipids, closely approximating their occupied lateral area. The program requires no installation, is fast, and is freely available. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
TL;DR: Results of GolP on an independent test set of small molecules show the good performances of the force field, and gold polarization (image charge effects) is taken into account by a recently proposed procedure.
Abstract: A classical atomistic force field to describe the interaction of proteins with gold (111) surfaces in explicit water has been devised. The force field is specifically designed to be easily usable in most common bio-oriented molecular dynamics codes, such as GROMACS and NAMD. Its parametrization is based on quantum mechanical (density functional theory [DFT] and second order Moller-Plesset perturbation theory [MP2]) calculations and experimental data on the adsorption of small molecules on gold. In particular, a systematic DFT survey of the interaction between Au(111) and the natural amino acid side chains has been performed to single out chemisorption effects. Van der Waals parameters have been instead fitted to experimental desorption energy data of linear alkanes and were also studied via MP2 calculations. Finally, gold polarization (image charge effects) is taken into account by a recently proposed procedure (Iori, F.; Corni, S. J Comp Chem 2008, 29, 1656). Preliminary validation results of GolP on an independent test set of small molecules show the good performances of the force field.
TL;DR: Multiconformation continuum electrostatics explores different conformational degrees of freedom in Monte Carlo calculations of protein residue and ligand pKas to create a position dependent, heterogeneous dielectric response giving a more accurate picture of coupled ionization and position changes.
Abstract: Multiconformation continuum electrostatics (MCCE) explores different conformational degrees of freedom in Monte Carlo calculations of protein residue and ligand pK(a)s. Explicit changes in side chain conformations throughout a titration create a position dependent, heterogeneous dielectric response giving a more accurate picture of coupled ionization and position changes. The MCCE2 methods for choosing a group of input heavy atom and proton positions are described. The pK(a)s calculated with different isosteric conformers, heavy atom rotamers and proton positions, with different degrees of optimization are tested against a curated group of 305 experimental pK(a)s in 33 proteins. QUICK calculations, with rotation around Asn and Gln termini, sampling His tautomers and torsion minimum hydroxyls yield an RMSD of 1.34 with 84% of the errors being <1.5 pH units. FULL calculations adding heavy atom rotamers and side chain optimization yield an RMSD of 0.90 with 90% of the errors <1.5 pH unit. Good results are also found for pK(a)s in the membrane protein bacteriorhodopsin. The inclusion of extra side chain positions distorts the dielectric boundary and also biases the calculated pK(a)s by creating more neutral than ionized conformers. Methods for correcting these errors are introduced. Calculations are compared with multiple X-ray and NMR derived structures in 36 soluble proteins. Calculations with X-ray structures give significantly better pK(a)s. Results with the default protein dielectric constant of 4 are as good as those using a value of 8. The MCCE2 program can be downloaded from http://www.sci.ccny.cuny.edu/~mcce.
TL;DR: PEP‐FOLD simulations are fast—a few minutes only—opening the door to in silico large‐scale rational design of new bioactive peptides and miniproteins.
Abstract: Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 A Calpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 A Calpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins.
TL;DR: The binding of seven biotin analogues to avidin is studied, taking advantage of the fact that the protein is a tetramer with four independent binding sites, which should give the same estimated binding affinities.
Abstract: The molecular mechanics/generalized Born surface area (MM/GBSA) method has been investigated with the aim of achieving a statistical precision of 1 kJ/mol for the results. We studied the binding of seven biotin analogues to avidin, taking advantage of the fact that the protein is a tetramer with four independent binding sites, which should give the same estimated binding affinities. We show that it is not enough to use a single long simulation (10 ns), because the standard error of such a calculation underestimates the difference between the four binding sites. Instead, it is better to run several independent simulations and average the results. With such an approach, we obtain the same results for the four binding sites, and any desired precision can be obtained by running a proper number of simulations. We discuss how the simulations should be performed to optimize the use of computer time. The correlation time between the MM/GBSA energies is approximately 5 ps and an equilibration time of 100 ps is needed. For MM/GBSA, we recommend a sampling time of 20-200 ps for each separate simulation, depending on the protein. With 200 ps production time, 5-50 separate simulations are required to reach a statistical precision of 1 kJ/mol (800-8000 energy calculations or 1.5-15 ns total simulation time per ligand) for the seven avidin ligands. This is an order of magnitude more than what is normally used, but such a number of simulations is needed to obtain statistically valid results for the MM/GBSA method. (c) 2009 Wiley Periodicals, Inc. J Comput Chem 2009.
TL;DR: The COSMO‐RS method has been used for the prediction of pKa values in acetonitrile and the slope of the experimental pKa versus theoretical ΔGdiss was found to match the theoretical value 1/RT ln (10) very well, resulting in truly ab initio pKa prediction.
Abstract: The COSMO-RS method, a combination of the quantum chemical dielectric continuum solvation model COSMO with a statistical thermodynamics treatment for realistic solvation simulations, has been used for the prediction of pKa values in acetonitrile. For a variety of 93 organic acids, the directly calculated values of the free energies of dissociation in acetonitrile showed a very good correlation with the pKa values (r2 = 0.97) in acetonitrile, corresponding to a standard deviation of 1.38 pKa units. Thus, we have a prediction method for acetonitrile pKa with the intercept and the slope as the only adjusted parameters. Furthermore, the pKa values of CH acids yielding large anions with delocalized charge can be predicted with a rmse of 1.12 pKa units using the theoretical values of slope and intercept resulting in truly ab initio pKa prediction. In contrast to our previous findings on aqueous acidity predictions the slope of the experimental pKa versus theoretical ΔGdiss was found to match the theoretical value 1/RT ln (10) very well. The predictivity of the presented method is general and is not restricted to certain compound classes. However, a systematic correction of −7.5 kcal mol−1 is required for compounds that do not allow electron-delocalization in the dissociated anion. The prediction model was tested on a diverse test set of 129 complex multifunctional compounds from various sources, reaching a root mean square deviation of 2.10 pKa units. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
TL;DR: Here, a web‐server predictor called GPCR‐CA is presented, where “CA” stands for “Cellular Automaton”, meaning that the CA images have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences.
Abstract: Given an uncharacterized protein sequence, how can we identify whether it is a G-protein-coupled receptor (GPCR) or not? If it is, which functional family class does it belong to? It is important to address these questions because GPCRs are among the most frequent targets of therapeutic drugs and the information thus obtained is very useful for "comparative and evolutionary pharmacology," a technique often used for drug development. Here, we present a web-server predictor called "GPCR-CA," where "CA" stands for "Cellular Automaton" (Wolfram, S. Nature 1984, 311, 419), meaning that the CA images have been utilized to reveal the pattern features hidden in piles of long and complicated protein sequences. Meanwhile, the gray-level co-occurrence matrix factors extracted from the CA images are used to represent the samples of proteins through their pseudo amino acid composition (Chou, K.C. Proteins 2001, 43, 246). GPCR-CA is a two-layer predictor: the first layer prediction engine is for identifying a query protein as GPCR on non-GPCR; if it is a GPCR protein, the process will be automatically continued with the second-layer prediction engine to further identify its type among the following six functional classes: (a) rhodopsin-like, (b) secretin-like, (c) metabotrophic/glutamate/pheromone; (d) fungal pheromone, (e) cAMP receptor, and (f) frizzled/smoothened family. The overall success rates by the predictor for the first and second layers are over 91% and 83%, respectively, that were obtained through rigorous jackknife cross-validation tests on a new-constructed stringent benchmark dataset in which none of proteins has >or=40% pairwise sequence identity to any other in a same subset. GPCR-CA is freely accessible at http://218.65.61.89:8080/bioinfo/GPCR-CA, by which one can get the desired two-layer results for a query protein sequence within about 20 seconds.
TL;DR: How λ‐dynamics has been used to rapidly and reliably compute relative hydration free energies and binding affinities for series of ligands, to accurately identify crystallographically observed binding modes starting from incorrect orientations, and to model the effects of mutations upon protein stability is reviewed.
Abstract: Free energy calculations are fundamental to obtaining accurate theoretical estimates of many important biological phenomena including hydration energies, protein-ligand binding affinities and energetics of conformational changes. Unlike traditional free energy perturbation and thermodynamic integration methods, λ-dynamics treats the conventional "λ" as a dynamic variable in free energy simulations and simultaneously evaluates thermodynamic properties for multiple states in a single simulation. In the present paper, we provide an overview of the theory of λ-dynamics, including the use of biasing and restraining potentials to facilitate conformational sampling. We review how λ-dynamics has been used to rapidly and reliably compute relative hydration free energies and binding affinities for series of ligands, to accurately identify crystallographically observed binding modes starting from incorrect orientations, and to model the effects of mutations upon protein stability. Finally, we suggest how λ-dynamics may be extended to facilitate modeling efforts in structure-based drug design.
TL;DR: The calculated gap characteristics and the falling trend of gap width with the increasing X atomic number agree with the experimental results, despite the common DFT underestimation of gap values.
Abstract: The electronic structures of BiOX (X = F, Cl, Bi-, I) photocalalysts have been calculated with and without Bi 5d states using the experimental lattice parameters, via the plane-wave pseudopotential method based oil density functional theory (DFT). BiOF exhibits a direct band gap of 3.22 or 3.12 eV corresponding to the adoption of Bi 5d states or not. The indirect band gaps of BiOCl, BiOBr, and BiOI are 2.80, 2.36, and 1.75 eV, respectively, if calculated with Bi 5d states, whereas the absence of Bi 5d states reduces them to 2.59, 2.13, and 1.53 eV successively. The Calculated gap characteristics and the falling trend of gap width with the increasing X atomic number agree with the experimental results, despite the common DFT underestimation of gap values. The shapes of valence-band tops and conduction-band bottoms are almost independent of the involvement of Bi 5d states. The indirect characteristic becomes more remarkable, and the conduction-band bottom flattens in the sequence of BiOCl, BiOBr, and BiOI. Both O 2p and X np (n = 2, 3, 4, and 5 for X = F, Cl, Br, and I, respectively) states dominate the valence hands, whereas Bi 6p states contribute the most to the Conduction hands. With the growing X atomic number, the localized X np states shift closer toward the valence-band tops, and the valence and conduction bandwidths evolve in opposite trends. Atomic and bond populations have also been explored to elucidate the atomic interactions, along with the spatial distribution of orbital density. (C) 2008 Wiley Periodicals, Inc. J Comput Chem 30: 183-190, 2009
TL;DR: An algorithm for computing nonbonded interactions with cutoffs on a graphics processing unit is described and incorporated into OpenMM, a library for performing molecular simulations on high‐performance computer architectures.
Abstract: We describe an algorithm for computing nonbonded interactions with cutoffs on a graphics processing unit. We have incorporated it into OpenMM, a library for performing molecular simulations on high-performance computer architectures. We benchmark it on a variety of systems including boxes of water molecules, proteins in explicit solvent, a lipid bilayer, and proteins with implicit solvent. The results demonstrate that its performance scales linearly with the number of atoms over a wide range of system sizes, while being significantly faster than other published algorithms.
TL;DR: This review focuses on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem, and the use of a flexible description of the proteins under study and theUse of biological information as the localization of the hot spots, the important residues for protein–protein binding.
Abstract: Protein-protein binding is one of the critical events in biology, and knowledge of proteic complexes three-dimensional structures is of fundamental importance for the biochemical study of pharmacologic compounds. In the past two decades there was an emergence of a large variety of algorithms designed to predict the structures of protein-protein complexes--a procedure named docking. Computational methods, if accurate and reliable, could play an important role, both to infer functional properties and to guide new experiments. Despite the outstanding progress of the methodologies developed in this area, a few problems still prevent protein-protein docking to be a widespread practice in the structural study of proteins. In this review we focus our attention on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem. We also focus our attention on the use of a flexible description of the proteins under study and the use of biological information as the localization of the hot spots, the important residues for protein-protein binding. The most common docking softwares are described too.
TL;DR: The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross‐predictive models and with the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect.
Abstract: The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte-Carlo cross-validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross-predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods.
TL;DR: For reactions with more DOF and higher density of states at the TS, it is found that the rate constants from TST calculations are systematically higher than those obtained from global dynamics calculations, indicating large recrossing effect for these systems.
Abstract: In this review article, we present a systematic comparison of the theoretical rate constants for a range of bimolecular reactions that are calculated by using three different classes of theoretical methods: quantum dynamics (QD), quasi-classical trajectory (QCT), and transition state theory (TST) approaches. The study shows that the difference of rate constants between TST results and those of the global dynamics methods (QD and QCT) are seen to be related to a number of factors including the number of degrees-of-freedom (DOF), the density of states at transition state (TS), etc. For reactions with more DOF and higher density of states at the TS, it is found that the rate constants from TST calculations are systematically higher than those obtained from global dynamics calculations, indicating large recrossing effect for these systems. The physical insight of this phenomenon is elucidated in the present review. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
TL;DR: Based on the density functional theory (DFT), the lattice constants and atomic positions of BiOX (X = F, Cl, Br, I) species have been optimized, and the electronic and optical properties of the relaxedspecies have been calculated, with Bi 5d states considered or not.
Abstract: Based on the density functional theory (DFT), the lattice constants and atomic positions of BiOX (X = E Cl, Br, I) species have been optimized, and the electronic and optical properties of the relaxed species have been calculated, with Bi 5d states considered or not. Relaxation generally results in the shrinkage in a and the expansion of c. Relaxed BiOCl, BiOBr, and BiOI present indirect band gaps, whereas BiOF exhibits a direct or somewhat indirect band-gap feature corresponding to the relaxation and calculation with the Bi 5d states or not. The bottom of the conduction band is quite flat for relaxed BiOI, and apparently flat in BiOBr, and shows observable flatness in BiOCl as well when considering the Bi 5d states. The top of the valence band is rather even as well for some species. The obtained maximum gaps for relaxed BiOF, BiOCI, BiOBr, and BiOI are 3.34, 2.92, 2.65, and 1.75 eV, respectively. The density peak of X tip states in the valence band shifts toward the valence band maximum with the increasing X atomic number. The bandwidths, atomic charges, bond orders. and orbital density have also been investigated along with some optical properties. (C) 2008 Wiley Periodicals, Inc. J Comput Chem 30: 1882-1891, 2009
TL;DR: The counterpoise method was introduced into the FMO scheme to eliminate the basis set superposition error and the influence of geometrical fluctuation on the interaction energies were examined, thereby enabling rigorous analysis of the molecular interaction between PrP and GN8.
Abstract: We performed fragment molecular orbital (FMO) calculations to examine the molecular interactions between the prion protein (PrP) and GN8, which is a potential curative agent for prion diseases. This study has the following novel aspects: we introduced the counterpoise method into the FMO scheme to eliminate the basis set superposition error and examined the influence of geometrical fluctuation on the interaction energies, thereby enabling rigorous analysis of the molecular interaction between PrP and GN8. This analysis could provide information on key amino acid residues of PrP as well as key units of GN8 involved in the molecular interaction between the two molecules. The present FMO calculations were performed using an original program developed in our laboratory, called "Parallelized ab initio calculation system based on FMO (PAICS)".
TL;DR: T theoretical calculations are used to explore whether the carbon‐centered radicals R• which are created after breaking the bond can be stabilized enough so that they resist the addition of molecular oxygen, an important factor in designing carbon‐based antioxidants.
Abstract: In this paper we examine a series of hydrocarbons with structural features which cause a weakening of the CH bond. We use theoretical calculations to explore whether the carbon-centered radicals R• which are created after breaking the bond can be stabilized enough so that they resist the addition of molecular oxygen, i.e. where the reaction R• + O2 → ROO• becomes energetically unfavorable. Calculations using a B3LYP-based method provide accurate bond dissociation enthalpies (BDEs) for RH and ROO• bonds, as well as Gibbs free energy changes for the addition reaction. The data show strong correlations between ROO• and RH BDEs for a wide variety of structures. They also show an equally strong correlation between the ROO• BDE and the unpaired spin density at the site of addition. Using these data we examine the major functional group categories proposed in several experimental studies, and assess their relative importance. Finally, we combine effects to try to optimize resistance to the addition of molecular oxygen, an important factor in designing carbon-based antioxidants. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009
TL;DR: Re‐optimization of Lennard‐Jones parameters and symmetry‐adapted perturbation theory analysis for the benzene dimer suggests that better agreement cannot be expected unless more flexible functional forms are employed for the empirical force fields.
Abstract: Several popular force fields, namely, CHARMM, AMBER, OPLS-AA, and MM3, have been tested for their ability to reproduce highly accurate quantum mechanical potential energy curves for noncovalent interactions in the benzene dimer, the benzene-CH(4) complex, and the benzene-H(2)S complex. All of the force fields are semi-quantitatively correct, but none of them is consistently reliable quantitatively. Re-optimization of Lennard-Jones parameters and symmetry-adapted perturbation theory analysis for the benzene dimer suggests that better agreement cannot be expected unless more flexible functional forms (particularly for the electrostatic contributions) are employed for the empirical force fields.
TL;DR: The results suggest that the massive volume of results that were published in the older literature at the H‐F level is valid especially when used to study trends or in QSAR or QSPR studies, and, as long as the test set of molecules is representative, there is no pressing need to re‐evaluate them at other levels of theory except when inadequate basis sets were used by today's standards.
Abstract: This article compares molecular properties and atomic properties defined by the quantum theory of atoms in molecules (QTAIM) obtained from three underlying levels of theory: MP2(full), density functional theory (DFT) (B3LYP), and Hartree-Fock (H-F). The same basis set (6-311++G(d,p)) has been used throughout the study. The calculations and comparisons were applied to a set of 30 small molecules representing common fragments of biological molecules. The molecular properties investigated are the energies and the electrostatic moments (up to and including the quadrupoles), and the atomic properties include electron populations (and atomic charge), atomic dipolar and quadrupolar polarizations, atomic volumes, and corrected and raw atomic energies. The Cartesian distance between dipole vectors and the Frobenius distance between the quadrupole tensors calculated at the three levels of theory provide a measure of their correlation (or lack thereof). With the exception of energies (atomic and molecular), it is found that both DFT and H-F are in excellent agreement with MP2, especially with regards to the electrostatic mutipoles up to the quadrupoles, but DFT and MP2 agree better in almost all studied properties (with the exception of molecular geometries). QTAIM properties whether obtained from H-F, DFT(B3LYP), or MP2 calculations when used in the construction of empirical correlations with experiment such as quantitative structure-activity-(or property)-relationships (QSAR/QSPR) are equivalent (because the properties calculated at the three levels are very highly correlated among themselves with r(2) typically >0.95, and therefore preserving trends). These results suggest that the massive volume of results that were published in the older literature at the H-F level is valid especially when used to study trends or in QSAR or QSPR studies, and, as long as our test set of molecules is representative, there is no pressing need to re-evaluate them at other levels of theory except when inadequate basis sets were used by today's standards. Extensive tabulation of molecular and atomic properties at the three theoretical levels is available in the Supporting Information, including optimized geometries, molecular energies, virial ratios, molecular electrostatic moments up to and including hexadecapoles, atomic populations, atomic volumes, atomic electrostatic moments up to and including the quadrupoles, and atomic energies.
TL;DR: The results confirm the indication of Müller et al. (J Chem Phys 1999, 110, 7176) that the transition to the V state of ethene conforms to the Franck‐Condon principle and that it is not necessary to appeal to a nonvertical transition to interpret the experimental data.
Abstract: This article addresses an analysis of the physical effects required for the correct description of the ionic pi --> pi* excited states in the frame of ab initio quantum chemistry, using the ionic V state of the ethene molecule as an example. The importance of the dynamic sigma polarization (absent in methods where the sigma skeleton is treated at a mean-field level) has been recognized by many authors in the past. In this article a new physical effect is described, i.e. the spatial contraction of the pi and pi* molecular orbitals (or of the local p atomic orbitals) originated from the reduction of the ionicity due to the dynamic sigma polarization. Such an effect is a second-order effect (it appears only as a consequence of the dynamic sigma polarization) but it cannot be ignored. Many of the difficulties found in the past in the calculation of the vertical excitation energy of the ionic states are attributed to an incomplete description of this contraction, while the few successes have been obtained when it has been fortuitously introduced by ad hoc procedures or when it is described in a brute force approach. Various strategies are proposed to allow for the spatial contraction of the p atomic orbitals. If this effect is considered at the orbital optimization step, it is shown that for the V state of ethene no Rydberg/valence mixing occurs and a simple perturbation correction (to the second order in the energy) on the pi --> pi* singly excited configuration gives stable results with respect to the computational parameters and in good agreement with the experimental findings and with the best theoretical calculations. Moreover, our results confirm the indication of Muller et al. (J Chem Phys 1999, 110, 7176) that the transition to the V state of ethene conforms to the Franck-Condon principle and that it is not necessary to appeal to a nonvertical transition to interpret the experimental data. The strategy reported in this article for ethene can be in principle generalized to the pi --> pi* ionic excited states of other molecules.