A toxicity evaluation and predictive system based on neural networks and wavelets

A computational approach has been developed for performing efficient and reasonably accurate toxicity evaluation and prediction. The approach is based on computational neural networks linked to modern computational chemistry and wavelet methods. In this paper, we present details of this approach and results demonstrating its accuracy and flexibility for predicting diverse biological endpoints including metabolic processes, mode of action, and hepato- and neurotoxicity. The approach also can be used for automatic processing of microarray data to predict modes of action.     

Performance of density‐functional tight‐binding models in describing hydrogen‐bonded anionic‐water clusters

Density‐functional tight‐binding (DFTB) models are computationally efficient approximations to density‐functional theory that have been shown to predict reliable structural and energetic properties for various systems. In this work, the reliability and accuracy of the self‐consistent‐charge DFTB model and its recent extension(s) in predicting the structures, binding energies, charge distributions, and vibrational frequencies of small water clusters containing polyatomic anions of the Hofmeister series (carbonate, sulfate, hydrogen phosphate, acetate, nitrate, perchlorate, and thiocyanate) have been carefully and systematically evaluated on the basis of high‐level ab initio quantum‐chemistry [MP2/aug‐cc‐pVTZ and CCSD(T)/aug‐cc‐pVQZ] reference data. Comparison with available experimental data has also been made for further validation. The self‐consistent‐charge DFTB model, and even more so its recent extensions, are shown to properly account for the structural properties, energetics, intermolecular polarization, and spectral signature of hydrogen‐bonding in anionic water clusters at a fraction of the computational cost of ab initio quantum‐chemistry methods. This makes DFTB models candidates of choice for investigating much larger systems such as seeded water droplets, their structural properties, formation thermodynamics, and infrared spectra. © 2014 Wiley Periodicals, Inc.     

Formulation and implementation of direct algorithm for the symmetry-adapted cluster and symmetry-adapted cluster-configuration interaction method

We present a new computational algorithm, called direct algorithm, for the symmetry-adapted cluster (SAC) and SAC-configuration interaction (SAC-CI) methodology for the ground, excited, ionized, and electron-attached states. The perturbation-selection technique and the molecular orbital index based direct sigma-vector algorithm were combined efficiently with the use of the sparse nature of the matrices involved. The formal computational cost was reduced to O(N(2)xM) for a system with N-active orbitals and M-selected excitation operators. The new direct SAC-CI program has been applied to several small molecules and free-base porphin and has been shown to be more efficient than the conventional nondirect SAC-CI program for almost all cases. Particularly, the acceleration was significant for large dimensional computations. The direct SAC-CI algorithm has achieved an improvement in both accuracy and efficiency. It would open a new possibility in the SAC/SAC-CI methodology for studying various kinds of ground, excited, and ionized states of molecules.     

Water PMF for predicting the properties of water molecules in protein binding site

Water is an important component in living systems and deserves better understanding in chemistry and biology. However, due to the difficulty of investigating the water functions in protein structures, it is usually ignored in computational modeling, especially in the field of computer‐aided drug design. Here, using the potential of mean forces (PMFs) approach, we constructed a water PMF (wPMF) based on 3946 non‐redundant high resolution crystal structures. The extracted wPMF potential was first used to investigate the structure pattern of water and analyze the residue hydrophilicity. Then, the relationship between wPMF score and the B factor value of crystal waters was studied. It was found that wPMF agrees well with some previously reported experimental observations. In addition, the wPMF score was also tested in parallel with 3D‐RISM to measure the ability of retrieving experimentally observed waters, and showed comparable performance but with much less computational cost. In the end, we proposed a grid‐based clustering scheme together with a distance weighted wPMF score to further extend wPMF to predict the potential hydration sites of protein structure. From the test, this approach can predict the hydration site at the accuracy about 80% when the calculated score lower than ?4.0. It also allows the assessment of whether or not a given water molecule should be targeted for displacement in ligand design. Overall, the wPMF presented here provides an optional solution to many water related computational modeling problems, some of which can be highly valuable as part of a rational drug design strategy. © 2012 Wiley Periodicals, Inc.     

An efficient algorithm for multistate protein design based on FASTER

Most of the methods that have been developed for computational protein design involve the selection of side‐chain conformations in the context of a single, fixed main‐chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD‐FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD‐FASTER and multistate design Monte Carlo indicated that MSD‐FASTER discovers low‐energy sequences much more consistently. MSD‐FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD‐FASTER should allow protein designers to test experimentally better‐scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

A Low‐Computational‐Cost Strategy to Localize Points in the Slow Manifold Proximity for Isothermal Chemical Kinetics

Dimensionality reduction for the modeling of reacting chemical systems can represent a fundamental achievement both for a clear understanding of the complex mechanisms under study and also for the practical calculation of quantities of interest. To tackle the problem, different approaches have been proposed in the literature. Among them, particular attention has been devoted to the exploitation of the so‐called slow manifolds (SMs). These are lower dimensional hypersurfaces where the slow part of the evolution takes place. In this study, we present a low‐computational‐cost algorithm (based on a previously developed theoretical framework) for the localization of candidate points in the proximity of the SM. A parallel implementation (called DRIMAK) of such an approach has been developed, and the source code is made freely available. We tested the performance of the code on two model schemes for hydrogen combustion, being able to localize points that fall very close to the perceived SM with limited computational effort. The method can provide starting points for other more accurate but computationally demanding strategies; this can be a great help especially when no information about the SM is available a priori, and very many species are involved in the reaction mechanism.     

Improving the accuracy of predicting disulfide connectivity by feature selection

Disulfide bonds are primary covalent cross‐links formed between two cysteine residues in the same or different protein polypeptide chains, which play important roles in the folding and stability of proteins. However, computational prediction of disulfide connectivity directly from protein primary sequences is challenging due to the nonlocal nature of disulfide bonds in the context of sequences, and the number of possible disulfide patterns grows exponentially when the number of cysteine residues increases. In the previous studies, disulfide connectivity prediction was usually performed in high‐dimensional feature space, which can cause a variety of problems in statistical learning, such as the dimension disaster, overfitting, and feature redundancy. In this study, we propose an efficient feature selection technique for analyzing the importance of each feature component. On the basis of this approach, we selected the most important features for predicting the connectivity pattern of intra‐chain disulfide bonds. Our results have shown that the high‐dimensional features contain redundant information, and the prediction performance can be further improved when these high‐dimensional features are reduced to a lower but more compact dimensional space. Our results also indicate that the global protein features contribute little to the formation and prediction of disulfide bonds, while the local sequential and structural information play important roles. All these findings provide important insights for structural studies of disulfide‐rich proteins. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Prediction of Protein Structure Using Surface Accessibility Data

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach.     

GRID: A high‐resolution protein structure refinement algorithm

The energy‐based refinement of protein structures generated by fold prediction algorithms to atomic‐level accuracy remains a major challenge in structural biology. Energy‐based refinement is mainly dependent on two components: (1) sufficiently accurate force fields, and (2) efficient conformational space search algorithms. Focusing on the latter, we developed a high‐resolution refinement algorithm called GRID. It takes a three‐dimensional protein structure as input and, using an all‐atom force field, attempts to improve the energy of the structure by systematically perturbing backbone dihedrals and side‐chain rotamer conformations. We compare GRID to Backrub, a stochastic algorithm that has been shown to predict a significant fraction of the conformational changes that occur with point mutations. We applied GRID and Backrub to 10 high‐resolution (≤ 2.8 Å) crystal structures from the Protein Data Bank and measured the energy improvements obtained and the computation times required to achieve them. GRID resulted in energy improvements that were significantly better than those attained by Backrub while expending about the same amount of computational resources. GRID resulted in relaxed structures that had slightly higher backbone RMSDs compared to Backrub relative to the starting crystal structures. The average RMSD was 0.25 ± 0.02 Å for GRID versus 0.14 ± 0.04 Å for Backrub. These relatively minor deviations indicate that both algorithms generate structures that retain their original topologies, as expected given the nature of the algorithms. © 2012 Wiley Periodicals, Inc.     

A hierarchical algorithm for fast debye summation with applications to small angle scattering

Debye summation, which involves the summation of sinc functions of distances between all pair of atoms in three‐dimensional space, arises in computations performed in crystallography, small/wide angle X‐ray scattering (SAXS/WAXS), and small angle neutron scattering (SANS). Direct evaluation of Debye summation has quadratic complexity, which results in computational bottleneck when determining crystal properties, or running structure refinement protocols that involve SAXS or SANS, even for moderately sized molecules. We present a fast approximation algorithm that efficiently computes the summation to any prescribed accuracy ? in linear time. The algorithm is similar to the fast multipole method (FMM), and is based on a hierarchical spatial decomposition of the molecule coupled with local harmonic expansions and translation of these expansions. An even more efficient implementation is possible when the scattering profile is all that is required, as in small angle scattering reconstruction (SAS) of macromolecules. We examine the relationship of the proposed algorithm to existing approximate methods for profile computations, and show that these methods may result in inaccurate profile computations, unless an error‐bound derived in this article is used. Our theoretical and computational results show orders of magnitude improvement in computation complexity over existing methods, while maintaining prescribed accuracy. © 2012 Wiley Periodicals, Inc.     

Accurate and fast treatment of large molecular systems: Assessment of CEPA and pCCSD within the local pair natural orbital approximation

The local pair natural orbital approach, which has been combined with two post‐Hartree–Fock methods, CEPA‐1 and pCCSD‐1a, recently, is assessed for its applicability to large real‐world problems without abundant computing resources. Test cases are selected based on being representative for computational chemistry problems and availability of reliable reference data. Both methods show a good performance and can be applied easily to systems of up to 100 atoms when very accurate energies are sought after. A considerable demand for basis sets of good quality has been identified and practical guidelines to satisfy this are mapped out. © 2012 Wiley Periodicals, Inc.     

An improved pairwise decomposable finite-difference Poisson-Boltzmann method for computational protein design

Our goal is to develop accurate electrostatic models that can be implemented in current computational protein design protocols. To this end, we improve upon a previously reported pairwise decomposable, finite difference Poisson-Boltzmann (FDPB) model for protein design (Marshall et al., Protein Sci 2005, 14, 1293). The improvement involves placing generic sidechains at positions with unknown amino acid identity and explicitly capturing two-body perturbations to the dielectric environment. We compare the original and improved FDPB methods to standard FDPB calculations in which the dielectric environment is completely determined by protein atoms. The generic sidechain approach yields a two to threefold increase in accuracy per residue or residue pair over the original pairwise FDPB implementation, with no additional computational cost. Distance dependent dielectric and solvent-exclusion models were also compared with standard FDPB energies. The accuracy of the new pairwise FDPB method is shown to be superior to these models, even after reparameterization of the solvent-exclusion model.     

An evaluation of experimental design in QSAR modelling utilizing the k‐medoid clustering

A reliable selection of a representative subset of chemical compounds has been reported to be crucial for numerous tasks in computational chemistry and chemoinformatics. We investigated the usability of an approach on the basis of the k‐medoid algorithm for this task and in particular for experimental design and the split between training and validation set. We therefore compared the performance of models derived from such a selection to that of models derived using several other approaches, such as space‐filling design and D‐optimal design. We validated the performance on four datasets with different endpoints, representing toxicity, physicochemical properties and others. Compared with the models derived from the compounds selected by the other examined approaches, those derived with the k‐medoid selection show a high reliability for experimental design, as their performance was constantly among the best for all examined datasets. Of all the models derived with all examined approaches, those derived with the k‐medoid approach were the only ones that showed a significantly improved performance compared with a random selection, for all datasets, the whole examined range of selected compounds and for each dimensionality of the search space. Copyright © 2012 John Wiley & Sons, Ltd.     

An improved algorithm for analytical gradient evaluation in resolution-of-the-identity second-order Møller-Plesset perturbation theory: application to alanine tetrapeptide conformational analysis

We present a new algorithm for analytical gradient evaluation in resolution‐of‐the‐identity second‐order Møller‐Plesset perturbation theory (RI‐MP2) and thoroughly assess its computational performance and chemical accuracy. This algorithm addresses the potential I/O bottlenecks associated with disk‐based storage and access of the RI‐MP2 t‐amplitudes by utilizing a semi‐direct batching approach and yields computational speed‐ups of approximately 2–3 over the best conventional MP2 analytical gradient algorithms. In addition, we attempt to provide a straightforward guide to performing reliable and cost‐efficient geometry optimizations at the RI‐MP2 level of theory. By computing relative atomization energies for the G3/99 set and optimizing a test set of 136 equilibrium molecular structures, we demonstrate that satisfactory relative accuracy and significant computational savings can be obtained using Pople‐style atomic orbital basis sets with the existing auxiliary basis expansions for RI‐MP2 computations. We also show that RI‐MP2 geometry optimizations reproduce molecular equilibrium structures with no significant deviations (>0.1 pm) from the predictions of conventional MP2 theory. As a chemical application, we computed the extended‐globular conformational energy gap in alanine tetrapeptide at the extrapolated RI‐MP2/cc‐pV(TQ)Z level as 2.884, 4.414, and 4.994 kcal/mol for structures optimized using the HF, DFT (B3LYP), and RI‐MP2 methodologies and the cc‐pVTZ basis set, respectively. These marked energetic discrepancies originate from differential intramolecular hydrogen bonding present in the globular conformation optimized at these levels of theory and clearly demonstrate the importance of long‐range correlation effects in polypeptide conformational analysis. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007     

Efficient Implementation of Cluster Expansion Models in Surface Kinetic Monte Carlo Simulations with Lateral Interactions: Subtraction Schemes,Supersites, and the Supercluster Contraction

While lateral interaction models for reactions at surfaces have steadily gained popularity and grown in terms of complexity, their use in chemical kinetics has been impeded by the low performance of current kinetic Monte Carlo (KMC) algorithms. The origins of the additional computational cost in KMC simulations with lateral interactions are traced back to the more elaborate cluster expansion Hamiltonian, the more extensive rate updating, and to the impracticality of rate-catalog-based algorithms for interacting adsorbate systems. Favoring instead site-based algorithms, we propose three ways to reduce the cost of KMC simulations: (1) representing the lattice energy by a smaller Supercluster Hamiltonian without loss of accuracy, (2) employing the subtraction schemes for updating key quantities in the simulation that undergo only small, local changes during a reaction event, and (3) applying efficient search algorithms from a set of established methods (supersite approach). The cost of the resulting algorithm is fixed with respect to the number of lattice sites for practical lattice sizes and scales with the square of the range of lateral interactions. The overall added cost of including a complex lateral interaction model amounts to less than a factor 3. Practical issues in implementation due to finite numerical accuracy are discussed in detail, and further suggestions for treating long-range lateral interactions are made. We conclude that, while KMC simulations with complex lateral interaction models are challenging, these challenges can be overcome by modifying the established variable step-size method by employing the supercluster, subtraction, and supersite algorithms (SSS-VSSM). © 2019 Wiley Periodicals, Inc.     

Symmetrizer: Algorithmic determination of point groups in nearly symmetric molecules

Symmetry is an extremely useful and powerful tool in computational chemistry, both for predicting the properties of molecules and for simplifying calculations. Although methods for determining the point groups of perfectly symmetric molecules are well‐known, finding the closest point group for a “nearly” symmetric molecule is far less studied, although it presents many useful applications. For this reason, we introduce Symmetrizer, an algorithm designed to determine a molecule's symmetry elements and closest matching point groups based on a user‐adjustable tolerance, and then to symmetrize that molecule to a given point group geometry. In contrast to conventional methods, Symmetrizer takes a bottom‐up approach to symmetry detection by locating all possible symmetry elements and uses this set to deduce the most probable point groups. We explain this approach in detail, and assess the flexibility, robustness, and efficiency of the algorithm with respect to various input parameters on several test molecules. We also demonstrate an application of Symmetrizer by interfacing it with the WebMO web‐based interface to computational chemistry packages as a showcase of its ease of integration. © 2012 Wiley Periodicals, Inc.     

Block‐adaptive quantum mechanics: An adaptive divide‐and‐conquer approach to interactive quantum chemistry

We present a novel Block‐Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. Although quantum chemistry models are known to be computationally demanding, we achieve interactive rates by focusing computational resources on the most active parts of the system. BAQM is based on a divide‐and‐conquer technique and constrains some nucleus positions and some electronic degrees of freedom on the fly to simplify the simulation. As a result, each time step may be performed significantly faster, which in turn may accelerate attraction to the neighboring local minima. By applying our approach to the nonself‐consistent Atom Superposition and Electron Delocalization Molecular Orbital theory, we demonstrate interactive rates and efficient virtual prototyping for systems containing more than a thousand of atoms on a standard desktop computer. © 2012 Wiley Periodicals, Inc.     

A scheme of numerical solution for three‐dimensional isoelectronic series of hydrogen atom using one‐dimensional basis functions

The solution of three‐dimensional Schrödinger wave equations of the hydrogen atoms and their isoelectronic ions (Z = 1 − 4) are obtained from the linear combination of one‐dimensional hydrogen wave functions. The use of one‐dimensional basis functions facilitates easy numerical integrations. An iteration technique is used to obtain accurate wave functions and energy levels. The obtained ground state energy level for the hydrogen atom converges stably to −0.498 a.u. The result shows that the novel approach is efficient for the three‐dimensional solution of the wave equation, extendable to the numerical solution of general many‐body problems, as has been demonstrated in this work with hydrogen anion.     

Application of a genomic model for high‐dimensional chemometric analysis

The rapid development of new technologies for large‐scale analysis of genetic variation in the genomes of individuals and populations has presented statistical geneticists with a grand challenge to develop efficient methods for identifying the small proportion of all identified genetic polymorphisms that have effects on traits of interest. To address such a “large p small n” problem, we have developed a heteroscedastic effects model (HEM) that has been shown to be powerful in high‐throughput genetic analyses. Here, we describe how this whole‐genome model can also be utilized in chemometric analysis. As a proof of concept, we use HEM to predict analyte concentrations in silage using Fourier transform infrared spectroscopy signals. The results show that HEM often outperforms the classic methods and in addition to this presents a substantial computational advantage in the analyses of such high‐dimensional data. The results thus show the value of taking an interdisciplinary approach to chemometric analysis and indicate that large‐scale genomic models can be a promising new approach for chemometric analysis that deserve to be evaluated more by experts in the field. The software used for our analyses is freely available as an R package at http://cran.r‐project.org/web/packages/bigRR/ . Copyright © 2014 John Wiley & Sons, Ltd.     

Numerical interpretation of molecular surface field in dielectric modeling of solvation

Continuum solvent models, particularly those based on the Poisson‐Boltzmann equation (PBE), are widely used in the studies of biomolecular structures and functions. Existing PBE developments have been mainly focused on how to obtain more accurate and/or more efficient numerical potentials and energies. However to adopt the PBE models for molecular dynamics simulations, a difficulty is how to interpret dielectric boundary forces accurately and efficiently for robust dynamics simulations. This study documents the implementation and analysis of a range of standard fitting schemes, including both one‐sided and two‐sided methods with both first‐order and second‐order Taylor expansions, to calculate molecular surface electric fields to facilitate the numerical calculation of dielectric boundary forces. These efforts prompted us to develop an efficient approximated one‐dimensional method, which is to fit the surface field one dimension at a time, for biomolecular applications without much compromise in accuracy. We also developed a surface‐to‐atom force partition scheme given a level set representation of analytical molecular surfaces to facilitate their applications to molecular simulations. Testing of these fitting methods in the dielectric boundary force calculations shows that the second‐order methods, including the one‐dimensional method, consistently perform among the best in the molecular test cases. Finally, the timing analysis shows the approximated one‐dimensional method is far more efficient than standard second‐order methods in the PBE force calculations. © 2017 Wiley Periodicals, Inc.