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

Eric Di Luccio,Patrice Koehl

doi:10.1186/2043-9113-2-18

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.

Highlights

Drug discovery typically starts with the identification of a potential target that is tested and validated either through high-throughput screening against a library of drug compounds or by rational drug design
X-ray crystallography and NMR remain the experimental techniques of choice to acquire this knowledge; there are many proteins that are difficult to crystallize or to purify to the level requested by these techniques
We focus on homology modelling, following our previous study where we reviewed the common practices in homology modelling of proteins and provided a set of guidelines for building better models [1]

Summary

Background

Structure-based drug design relies on the concept of “druggability” which is used to describe proteins that possess structures that favour interactions with a drug-like chemical compound. The proper stereochemistry of models can be assessed by commonly used programs such as Procheck or WhatIf [16,17] Despite all these methods, the homology modelling community still lacks a simple and easy to use indicator which gives an unambiguous feedback on how the final model, or family of models, reflects the data that were used in the modelling process, similar to the couple R-factor/R-free for X-ray crystallography. Computational strategy The H-factor combines information of four scoring functions that evaluates (1) the template structure(s) (based on the corresponding PDB files); (2) the sequence alignment between the template(s) and the target sequences; (3) the structural heterogeneity of the models built; and (4) the structural neighborhood within protein families (Figure 1). Score (4): Assessment of the structural integrity of functional domains Score (4) is designed to evaluate the quality of all functional domains in the model with respect to available experimental structures deposited in the protein data bank (PDB). The H-factor computation is accessible online at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor

Results and discussion

Conclusion

10. Sippl MJ

19. Jones DT

22. Eddy SR