Exploring the electronic structure of knotted proteins: the case of two ornithine transcarbamylase family.

Context: Geometrical knots are rare structural arrangements in proteins in which the polypeptide chain ties itself into a knot, which is very intriguing due to the uncertainty of their impact on the protein properties. Presently, classical molecular dynamics is the most employed technique in the few studies found on this topic, so any information on how the presence of knots affects the reactivity and electronic properties of proteins is even scarcer. Using the electronic structure methods and quantum chemical descriptors analysis, we found that the same amino-acid residues in the knot core have statistically larger values for the unknotted protein, for both hard-hard and soft-soft interaction descriptors.

Quantum chemical descriptors based on semiempirical methods for large biomolecules.

In this Review, we reviewed the efforts to expand the applications of conceptual density functional theory reactivity descriptors and hard and soft acid and base principles for macromolecules and other strategies that focused on low-level quantum chemistry methods. Currently, recent applications are taking advantage of modifications of these descriptors using semiempirical electronic structures to explain enzymatic catalysis reactions, protein-binding processes, and structural analysis in proteins. We have explored these new solutions along with their implementations in the software PRIMoRDiA, discussing their impact on the field and its perspectives.

The complex build algorithm to set up starting structures of lanthanoid complexes with stereochemical control for molecular modeling.

When handling metallic centers of higher coordination numbers, one is commonly deluded with the presumption that any assembled metal complex geometry (including a crystallographic one) is good enough as a starting structure for computational chemistry calculations; all oblivious to the fact that such a structure is nothing short of just one out of several, sometimes dozens, or even thousands of other stereoisomers. Moreover, coordination chirality, so frequently present in complexes of higher coordination numbers, is another often overlooked property, rarely recognized as such. The Complex Build algorithm advanced in this article has been designed with the purpose of generating starting structures for molecular modeling calculations with full stereochemical control, including stereoisomer complete identification and coordination chirality recognition.

PRIMoRDiA: A Software to Explore Reactivity and Electronic Structure in Large Biomolecules.

Plenty of enzymes with structural data do not have their mechanism of catalysis elucidated. Reactivity descriptors, theoretical quantities generated from resolved electronic structure, provide a way to predict and rationalize chemical processes of such systems. In this Application Note, we present PRIMoRDiA (MoRDiA acromolecular eactivity escriptors ccess), a software built to calculate the reactivity descriptors of large biosystems by employing an efficient and accurate treatment of the large output files produced by quantum chemistry packages.

Semiempirical methods do Fukui functions: Unlocking a modeling framework for biosystems.

Obtaining reactivity information from the molecular electronic structure of a chemical system is a computationally intensive process. As a way of probing reactivity information around that, there exist electron density response variables, such as the Fukui functions (FFs), which are well-established descriptors that summarize the local susceptibility to react. These properties only require few single-point quantum chemical calculations, but even then, the intrinsic high cost and unfavorable computational complexity with respect to the number of atoms in the system makes this approach available only to small fragments and systems.

Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors.

In general, computational simulations of enzymatic catalysis processes are thermodynamic and structural surveys to complement experimental studies, requiring high level computational methods to match accurate energy values. In the present work, we propose the usage of reactivity descriptors, theoretical quantities calculated from the electronic structure, to characterize enzymatic catalysis outlining its reaction profile using low-level computational methods, such as semiempirical Hamiltonians. We simulate three enzymatic reactions paths, one containing two reaction coordinates and without prior computational study performed, and calculate the reactivity descriptors for all obtained structures.

Efficient algorithm for expanding theoretical electron densities in canterakis-zernike functions.

An algorithm for the efficient computation of Canterakis-Zernike moments of theoretically computed molecular electron densities and rotationally invariant Fingerprint indices derived from them is reported. The algorithm is suitable for any density expressed in terms of Gaussian- or Slater-type functions within the Linear Combination of Atomic Orbitals framework at any level of computation. Electron density is expressed as a one-center expansion of real regular spherical harmonics times radial factors by means of translation techniques, which facilitates the efficient computation of the moments in terms of a single one-dimension numerical integration.

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.

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.