Topological Charge-Transfer Indices: From Small Molecules to Proteins

Abstract

Valence-topological charge-transfer indices are applied to the calculation of dipole moment-pH at the isoelectric point. Dipole moments calculated by algebraic-vector semisums of charge-transfer indices are defined. The ability of indices, for the description of molecular charge distribution, is established by comparing them with the dipole moment of the valence-isoelectronic series of cyclopentadiene-benzene-styrene. Both charge-transfer indices are proposed: vector semisums μ vec -μ vec V . The μ vec V is intermediate between μ vec and μ experiment . The steric effect is almost constant along series and the dominating effect is electronic. The indices are applied to the calculation of the dipole moments of the homologous series of percutaneous enhancers and the isoelectric point of 21 amino acids. In most fits no superimposition of the corresponding G k -J k /G k V -J k V pairs is observed, which diminishes the risk of collinearity. The inclusion of heteroatoms in the -electron systems is beneficial for the description of isoelectric point, because of either the role of additional p-orbitals provided by heteroatom or the role of steric factors in the -electron conjugation. The use of (valence) chargetransfer indices gives limited results for amino-acid isoelectric points. The inclusion of the number of acidic/basic groups improves the models, especially for amino acids with more than two functional groups. The fitting line for 21 amino acids is used to estimate the lysozyme isoelectric point by replacing (1+ n/n T ) with (M+ n)/n T . The lysozyme fragment results can estimate the isoelectric point of the whole protein within 1-13% error.

State of Art: Review of Theoretical Study of GSK-3β and a New Neural Networks QSAR Studies for the Design of New Inhibitors Using 2D-Descriptors

“…For instance, González-Díaz et al have discussed the use of these methods but only from the point of view of proteins [46]. Later, some groups have published different papers in one special issue on QSAR but they have been also restricted to the field of protein and proteomics [47][48][49][50][51][52][53]. In other recent issue, guestedited by González-Díaz [54] a series of papers have been published, devoted to QSAR/QSPR techniques for low-molecular-weight drugs [54][55][56][57][58][59][60][61][62][63].…”

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design

“…Sirois, Giguere, and Roy19 presented the first QSAR model for Galectin‐3 glycomimetic inhibitors based on docked structures to the carbohydrate recognition domain (CRD). We can use many physicochemical parameters to characterize proteins in these studies such as charges or hydrophilicity parameters20 or numerical parameters derived from a graph or network representation of the molecular systems (including but not limited to protein structure, as in this case) 21. However, there is no report of a general QSAR model for the prediction of Ls‐like proteins.…”

Molecular diversity classification via information theory: A review

“…Complexity, emergence and molecular diversity via information theory were analyzed. Valence topological charge-transfer indices for dipoles were obtained [5] and extended to homo/heterocycles and proteins [6]. Information-entropy molecular classification was applied to local anaesthetics [7,8] and inhibitors of human immunodeficiency virus type 1 (HIV-1) [9,10].…”