Assessment of semiempirical enthalpy of formation in solution as an effective energy function to discriminate native-like structures in protein decoy sets.

In this work, we tested the PM6, PM6-DH+, PM6-D3, and PM7 enthalpies of formation in aqueous solution as scoring functions across 33 decoy sets to discriminate native structures or good models in a decoy set. In each set these semiempirical quantum chemistry methods were compared according to enthalpic and geometric criteria. Enthalpically, we compared the methods according to how much lower was the enthalpy of each native, when compared with the mean enthalpy of its set. Geometrically, we compared the methods according to the fraction of native contacts (Q), which is a measure of geometric closeness between an arbitrary structure and the native. For each set and method, the Q of the best decoy was compared with the Q0 , which is the Q of the decoy closest to the native in the set. It was shown that the PM7 method is able to assign larger energy differences between the native structure and the decoys in a set, arguably because of a better description of dispersion interactions, however PM6-DH+ was slightly better than the rest at selecting geometrically good models in the absence of a native structure in the set. © 2016 Wiley Periodicals, Inc.

GPU Linear Algebra Libraries and GPGPU Programming for Accelerating MOPAC Semiempirical Quantum Chemistry Calculations.

In this study, we present some modifications in the semiempirical quantum chemistry MOPAC2009 code that accelerate single-point energy calculations (1SCF) of medium-size (up to 2500 atoms) molecular systems using GPU coprocessors and multithreaded shared-memory CPUs. Our modifications consisted of using a combination of highly optimized linear algebra libraries for both CPU (LAPACK and BLAS from Intel MKL) and GPU (MAGMA and CUBLAS) to hasten time-consuming parts of MOPAC such as the pseudodiagonalization, full diagonalization, and density matrix assembling. We have shown that it is possible to obtain large speedups just by using CPU serial linear algebra libraries in the MOPAC code. As a special case, we show a speedup of up to 14 times for a methanol simulation box containing 2400 atoms and 4800 basis functions, with even greater gains in performance when using multithreaded CPUs (2.1 times in relation to the single-threaded CPU code using linear algebra libraries) and GPUs (3.8 times). This degree of acceleration opens new perspectives for modeling larger structures which appear in inorganic chemistry (such as zeolites and MOFs), biochemistry (such as polysaccharides, small proteins, and DNA fragments), and materials science (such as nanotubes and fullerenes). In addition, we believe that this parallel (GPU-GPU) MOPAC code will make it feasible to use semiempirical methods in lengthy molecular simulations using both hybrid QM/MM and QM/QM potentials.