The H-factor as a novel quality metric for homology modeling

Abstract

BackgroundDrug discovery typically starts with the identification of a potential target that is then tested and validated either through high-throughput screening against a library of drug compounds or by rational drug design. When the putative target is a protein, the latter approach requires the knowledge of its structure. Finding the structure of a protein is however a difficult task. Significant progress has come from high-resolution techniques such as X-ray crystallography and NMR; there are many proteins however whose structure have not yet been solved. Computational techniques for structure prediction are viable alternatives to experimental techniques for these cases. However, the proper validation of the structural models they generate remains an issue.FindingsIn this report, we focus on homology modeling techniques and introduce the H-factor, a new indicator for assessing the quality of protein structure models generated with these techniques. The H-factor is meant to mimic the R-factor used in X-ray crystallography. The method for computing the H-factor is fully described with a demonstration of its effectiveness on a test set of target proteins.ConclusionsWe have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor.

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“…A general rule of thumb is selecting the protein template with the highest sequence identity with the receptor of interest, particularly on the target pocket, and sequence identities lower than 30% will produce significantly less reliable models (Fiser, 2010). The quality of homology models must be assessed before using them in a receptor-based VS, and while there are many ways of evaluating the quality of a homology model (Bhattacharya et al ., 2008; DasGupta et al ., 2015; di Luccio and Koehl, 2012; Eramian et al ., 2006; Shen and Sali, 2006), and most of the modelling tools include scores for quality assessment, it is important to know their capabilities and limits. Many assessment tools are biased towards the more known structures and may fail with proteins less represented in the databases, as is the case for membrane proteins (Benkert et al ., 2011; di Luccio and Koehl, 2012).…”