Biopolymer stochastic moments. I. Modeling human rhinovirus cellular recognition with protein surface electrostatic moments

Abstract

Stochastic moments may be applied as molecular descriptors in quantitative structure-activity relationship (QSAR) studies for small molecules (H. González-Dìaz et al., Journal of Molecular Modeling, 2002, Vol. 8, pp. 237-245; 2003, Vol. 9, pp. 395-407). However, applications in the field of biopolymers are less known. Recently, the MARCH-INSIDE approach has been generalized to encode structural features of proteins and other biopolymers (H. González-Dáaz et al., Bioinformatics, 2003, Vol. 19, pp. 2079-2087; Bioorganic & Medicinal Chemistry Letters, 2004, Vol. 14, pp. 4691-4695; Polymers, 2004, Vol. 45, pp. 3845-3853; Bioorganic & Medicinal Chemistry, 2005, Vol. 13, pp. 323-331). The present article attempts to extend this research by introducing for the first time stochastic moments for a surface road map of viral proteins. These moments are afterward used to seek a model that predicts the cellular receptor for human rhinoviruses. The model correctly classified 100% of 10 viruses binding to low-density lipoprotein receptor (LDLR) and 88.9% of 9 viruses binding to the intracellular adhesion molecule (ICAM) receptors in training. The same results have been obtained in four cross-validation experiments using a resubstitution technique. The present model favorably compares, in terms of complexity, with other previously reported based on entropy considerations, and offers a quantitative basis for the visual rule previously reported by Vlasak et al.

Medicinal Chemistry and Bioinformatics - Current Trends in Drugs Discovery with Networks Topological Indices

“…We used this procedure to calculate two kinds of TIs, entropies [92] and electrostatic moments [93], for the surface of viral capsid proteins of human Rhinoviruses (HRVs). These TIs were then used seek two models that discriminate data for HRVs according to host-cell receptor recognition.…”

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

“…In all these studies, we used different indices such as entropies [17], spectral moments [18,19], free energies [20,21], and electrostatic potentials [22,23]. The common feature of all these QSAR studies is the application of a Markov model (MM) to derive the molecular descriptors that encode the macromolecular structure [24].…”

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

“…In the work described here, we generalized our previous stochastic spectral moments 32,33,[58][59][60][61] to characterize protein 3D structure taking into consideration vdw interactions. The elements ( 1 p ij ) of the new matrix 1 P vdw are equal to the probability of direct vdw interaction between the amino acids a i and a j ( 1 p ij ) if a i and a j are placed at a distance shorter than a certain cut-off ( ij ¼ 1), which is otherwise equal to 0 ( ij ¼ 0).…”

“…26 In this sense, we used different mathematical formulations such as entropies, 27 absolute probabilities, 28 electrostatic potentials, 29 affinity constants, 30 and stochastic spectral moments. [31][32][33] However, a thorough inspection of this approach reveals that little attention is paid to other effects that are also of major importance for biological activity, such as van der Waals (vdw) interactions.…”