title: Predicting stability of Arc repressor mutants with protein stochastic moments.
abstract: As more and more protein structures are determined and applied to drug manufacture, there is increasing interest in studying their stability. In this study, the stochastic moments ((SR)pi(k)) of 53 Arc repressor mutants were introduced as molecular descriptors modeling protein stability. The Linear Discriminant Analysis model developed correctly classified 43 out of 53, 81.13% of proteins according to their thermal stability. More specifically, the model classified 20/28 (71.4%) proteins with near wild-type stability and 23/25 (92%) proteins with reduced stability. Moreover, validation of the model was carried out by re-substitution procedures (81.0%). In addition, the stochastic moments based model compared favorably with respect to others based on physicochemical and geometric parameters such as D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, which presented less than 77% of accuracy. This result illustrates the possibilities of the stochastic moments' method for the study of bioorganic and medicinal chemistry relevant proteins.
authors.affiliation: Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela 15706, Spain. humbertogd@vodafone.es
authors.forename: Humberto
authors.initials: H
authors.lastname: González-Díaz
authors.forename: Eugenio
authors.initials: E
authors.lastname: Uriarte
authors.forename: Ronal
authors.initials: R
authors.lastname: Ramos de Armas
mesh_terms: D008390:Markov Chains; D015233:Models, Statistical; D009154:Mutation; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D012097:Repressor Proteins / Q000737:chemistry* / Q000235:genetics; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D012097:Repressor Proteins