Abstract
Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
Highlights
Background to TvLDHA novel gene for lactate dehydrogenase (LDH) was identified from the genomic sequence of Trichomonas vaginalis (TvLDH)
Comparative modeling predicts the 3-D structure of a given protein sequence based primarily on its alignment to one or more proteins of known structure
When testing the procedure on a very difficult set of 19 modeling targets sharing only 4% to 27% sequence identity with their template structures, the average final alignment accuracy increased from 37% to 45% relative to the initial alignment
Summary
Comparative modeling consists of four main steps: fold assignment, targettemplate alignment, model building and model evaluation (Marti-Renom et al, 2000; Fig. 5.6.1). Comparative models can be helpful in designing mutants to test hypotheses about the protein's function (Wu et al, 1999; Vernal et al, 2002); in identifying active and binding sites (Sheng et al, 1996); in searching for, designing, and improving ligand binding strength for a given binding site (Ring et al, 1993; Li et al, 1996; Selzer et al, 1997; Enyedy et al, 2001; Que et al, 2002); modeling substrate specificity (Xu et al, 1996); in predicting antigenic epitopes (Sali and Blundell, 1993); in simulating protein-protein docking (Vakser, 1995); in inferring function from calculated electrostatic potential around the protein (Matsumoto et al, 1995); in facilitating molecular replacement in X-ray structure determination (Howell et al, 1992); in refining models based on NMR constraints (Modi et al, 1996); in testing and improving a sequence-structure alignment (Wolf et al, 1998); in annotating single nucleotide polymorphisms (Mirkovic et al, 2004; Karchin et al, 2005); in structural characterization of large complexes by docking to low-resolution cryo-electron density maps (Spahn et al, 2001; Gao et al, 2003); and in rationalizing known experimental observations. Models with such high accuracy have been shown to be useful even for refining crystallographic structures by the method of molecular replacement (Howell et al, 1992; Baker and Sali, 2001; Jones, 2001; Claude et al, 2004; Schwarzenbacher et al, 2004)
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