Quantification of the Influence of Single-Point Mutations on Haloalkane Dehalogenase Activity:  A Molecular Quantum Similarity Study

Abstract

Controlled modifications in certain protein amino acid residues can lead to changes in their function and stability. Amino acid structural features and their relation to these changes were examined by using quantum molecular similarity techniques. The effect of deliberate mutations in position 172 of the haloalkane dehalogenase enzyme, yielding to variations on the dehalogenation of 1,2-dibromoethane, was studied qualitatively and quantitatively using molecular quantum similarity techniques. A valuable classification of the residues according to their effect on activity was obtained by representing the optimal two-dimensional classical scaling solution. In addition, satisfactory quantitative relationships were found, comparable to those attained by previous studies on this same data set using other techniques. Molecular quantum similarity analysis provides a consistent, unbiased, and homogeneous set of molecular descriptors and is a feasible alternative to the use of physicochemical properties.

About the prediction of molecular properties using the fundamental Quantum QSPR (QQSPR) equation†

“…which is a variant of the form obtained in equation (23). The (n þ m) case can be easily handled as within previous discussion on the two described restrictions.…”

A new insight on the quantum quantitative structure‐properties relationships

“…In other words, the QSM column set can be used as a new n ‐dimensional vector tag set, attached to the molecular set M , in order to build up a new tagged set, namely a DQOS 17–21: …”

“…This is so because being the QSM: Z , by construction nonsingular (otherwise two density functions will be exactly the same), then there always can be computed a QSM inverse: Z −1 , obeying to the usual relationships: Z −1 Z = ZZ −1 = I , in such a way that the trivial result, defining the unknown coefficient vector: will be always obtained within a core set computational scheme. Furthermore, one can retrieve the exact value of the property for any molecule of the core set , just choosing the scalar products: The QSM for several core sets has been used in a quite large set of prediction studies 21–41, in every case employing up to date statistical tools, the usual procedures currently available in classical QSPR studies, see for example references 43–54. The use of the first order fundamental QQSPR equation to construct algorithms, which can be utilized as predictive tools independently of classical QSPR algorithms, has been previously attempted 55, but it has not been continued in practice until recently 42, 56, 57.…”

“…The optimal coefficient value 21 produces a minimum of the quadratic error, being the second order coefficient of the quadratic error polynomial 20, associated to the positive definite Euclidian norm of the U‐m discrete representation with respect the core set density tags, that is: 〈 z | z 〉>0. Such a quadratic error restriction is equivalent to construct a difference vector 19 with elements as small as possible.…”

Use of promolecular ASA density functions as a general algorithm to obtain starting MO in SCF calculations

“…The promolecular ASA (PASA) density function represents the atoms in a molecule as neutral entities of spherical shape, with a radial dependence equal to the isolated atoms. This approximate quantum mechanical development avoids costly molecular ab initio calculations and provides a sufficient precise three‐dimensional electron distribution for several purposes, among them, the molecular quantum similarity evaluation of QSAR models, involving large molecular systems and needing the optimization of the relative position of the corresponding molecular pairs 7, 11–18.…”