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Knowledge-based scoring functions are widely used for assessing putative complexes in protein-ligand and protein-protein docking and for structure prediction. Even with large training sets, knowledge-based scoring functions face the inevitable problem of sparse data. Here, we have developed a novel approach for handling the sparse data problem that is based on estimating the inaccuracies in knowledge-based scoring functions. This inaccuracy estimation is used to automatically weight the knowledge-based scoring function with an alternative, force-field-based potential (FFP) that does not rely on training data and can, therefore, provide an improved approximation of the interactions between rare chemical groups. The current version of STScore, a protein-ligand scoring function using our method, achieves a binding mode prediction success rate of 91% on the set of 100 complexes by Wang et al., and a binding affinity correlation of 0.514 with the experimentally determined affinities in PDBbind. The method presented here may be used with other FFPs and other knowledge-based scoring functions and can also be applied to protein-protein docking and protein structure prediction. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The small-amplitude conformational changes in macromolecules can be described by the changes in bond lengths and bond angles. The descriptors of large scale changes are torsions. We present a recursive algorithm, in which a bond vector is explicitly written in terms of these internal, or polyspherical coordinates, in a local frame defined by two other bond vectors and their cross product. Conformations of linear and branched molecules, as well as molecules containing rings can be described in this way. The orientation of the molecule is described by the orientation of a body frame. It is parametrized by the instantaneous rotation angle, and the two angles that parametrize the orientation of the instantaneous rotation axis. The reason not to use more conventional Euler angles is due to the fact that Euler angles are not well-defined in gimbal lock (i.e., when a body axis becomes aligned with its space fixed counter part). The position of the molecule is parametrized by its center of mass. Original and calculated positions are compared for several proteins, containing up to about 100,000 atoms. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

Tyrosine sulfation is a post-translational modification of many secreted and membrane-bound proteins. It governs protein-protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty-two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five-fold cross-validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in five-fold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

The three-body energy expansion in the fragment molecular orbital method (FMO) was applied to the 2nd order Møller–Plesset theory (MP2). The accuracy of both the two and three-body expansions was determined for water clusters, alanine n-mers (α-helices and β-strands) and one synthetic protein, using the 6-31G* and 6-311G* basis sets. At the best level of theory (three-body, two molecules/residues per fragment), the absolute errors in energy relative to ab initio MP2 were at most 1.2 and 5.0 mhartree, for the 6-31G* and 6-311G* basis sets, respectively. The relative accuracy was at worst 99.996% and 99.96%, for 6-31G* and 6-311G*, respectively. A three-body approximation was introduced and the optimum threshold value was determined. The protein calculation (6-31G*) at the production level (FMO2/2) took 3 h on 36 3.2-GHz Pentium 4 nodes and had the absolute error in the MP2 correlation energy of only 2 kcal/mol. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 [ABSTRACT FROM AUTHOR]

We developed a new parallel density-functional canonical molecular-orbital program for large molecules based on the resolution of the identity method. In this study, all huge matrices were decomposed and saved to the distributed local memory. The routines of the analytical molecular integrals and numerical integrals of the exchange-correlation terms were parallelized using the single program multiple data method. A conventional linear algebra matrix library, ScaLAPACK, was used for matrix operations, such as diagonalization, multiplication, and inversion. Anderson's mixing method was adopted to accelerate the self-consistent field (SCF) convergence. Using this program, we calculated the canonical wavefunctions of a 306-residue protein, insulin hexamer (26,790 orbitals), and a 133-residue protein, interleukin (11,909 orbitals) by the direct-SCF method. In regard to insulin hexamer, the total parallelization efficiency of the first SCF iteration was estimated to be 82% using 64 Itanium 2 processors connected at 3.2 GB/s (SGI Altix3700), and the calculation successfully converged at the 17-th SCF iteration. By adopting the update method, the computational time of the first and the final SCF loops was 229 min and 156 min, respectively. The whole computational time including the calculation before the SCF loop was 2 days and 17 h. This study put the calculations of the canonical wavefunction of 30,000 orbitals to practical use. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 [ABSTRACT FROM AUTHOR]

The energy decomposition analysis (EDA) by Kitaura and Morokuma was redeveloped in the framework of the fragment molecular orbital method (FMO). The proposed pair interaction energy decomposition analysis (PIEDA) can treat large molecular clusters and the systems in which fragments are connected by covalent bonds, such as proteins. The interaction energy in PIEDA is divided into the same contributions as in EDA: the electrostatic, exchange-repulsion, and charge transfer energies, to which the correlation (dispersion) term was added. The careful comparison to the ab initio EDA interaction energies for water clusters with 2–16 molecules revealed that PIEDA has the error of at most 1.2 kcal/mol (or about 1%). The analysis was applied to (H2O)1024, the α helix, β turn, and β strand of polyalanine (ALA)10, as well as to the synthetic protein (PDB code 1L2Y) with 20 residues. The comparative aspects of the polypeptide isomer stability are discussed in detail. © 2006 Wiley Periodicals, Inc.J Comput Chem 2007 [ABSTRACT FROM AUTHOR]

In terms of the classification of the protein secondary structures, we propose a 2D representation of protein secondary structure sequences. The representation are used to display, analyze, and compare the secondary structure sequences. Based on this representation, we assign the structural class to the protein, and verify the advantage or disadvantage of the methods of predicted protein second structure. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1119–1124, 2006 [ABSTRACT FROM AUTHOR]

The polarizable continuum model (PCM) for the description of solvent effects is combined with the fragment molecular orbital (FMO) method at several levels of theory, using a many-body expansion of the electron density and the corresponding electrostatic potential, thereby determining solute (FMO)–solvent (PCM) interactions. The resulting method, denoted FMO/PCM, is applied to a set of model systems, including α-helices and β-strands of alanine consisting of 10, 20, and 40 residues and their mutants to charged arginine and glutamate residues. The FMO/PCM error in reproducing the PCM solvation energy for a full system is found to be below 1 kcal/mol in all cases if a two-body expansion of the electron density is used in the PCM potential calculation and two residues are assigned to each fragment. The scaling of the FMO/PCM method is demonstrated to be nearly linear at all levels for polyalanine systems. A study of the relative stabilities of α-helices and β-strands is performed, and the magnitude of the contributing factors is determined. The method is applied to three proteins consisting of 20, 129, and 245 residues, and the solvation energy and computational efficiency are discussed. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 976–985, 2006 [ABSTRACT FROM AUTHOR]

An all-electron density functional (DF) calculation on insulin was performed by the Gaussian-based DF program, ProteinDF. Quasi-canonical localized orbitals (QCLOs) were used to improve the initial guess for the self-consistent field (SCF) calculation. All calculations were carried out by parallel computing on eight processors of an Itanium2 cluster (SGI Altix3700) with a theoretical peak performance of 41.6 GFlops. It took 35 h for the whole calculation. Insulin is a protein hormone consisting of two peptide chains linked by three disulfide bonds. The numbers of residues, atoms, electrons, orbitals, and auxiliary functions are 51, 790, 3078, 4439, and 8060, respectively. An all-electron DF calculation on insulin was successfully carried out, starting from connected QCLOs. Regardless of a large molecule with complicated topology, the differences in the total energy and the Mulliken atomic charge between initial and converged wavefunctions were very small. The calculation proceeded smoothly without any trial and error, suggesting that this is a promising method to obtain SCF convergence on large molecules such as proteins. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 987–993, 2005 [ABSTRACT FROM AUTHOR]