Biopolymer stochastic moments. I. Modeling human rhinovirus cellular recognition with protein surface electrostatic moments.
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.
González-Díaz
Humberto
H
Department of Organic Chemistry, University of Santiago de Compostela, 15782, Spain. humbertogd@vodafone.es
Uriarte
Eugenio
E
D001704:Biopolymers / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D011991:Receptors, Virus / Q000737:chemistry* / Q000378:metabolism*; D012229:Rhinovirus / Q000502:physiology*; D055672:Static Electricity; D013269:Stochastic Processes; D013499:Surface Properties; D013816:Thermodynamics; D014764:Viral Proteins / Q000737:chemistry* / Q000378:metabolism*
D001704:Biopolymers; D011991:Receptors, Virus; D014764:Viral Proteins