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

BACKGROUND: Drug 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. FINDINGS: In 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. CONCLUSIONS: We 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

The identification of natural inhibitory compounds against the plasmodium GTP Cyclohydrolase I (GCH1) enzyme

Malaria is a disease caused by protozoan parasites that invade red blood cells causing an infection. Malaria remains a global health problem. The genus Plasmodium infects about a quarter of a billion people annually, resulting in over a million death cases. This can severely affect the public health and socioeconomic development especially in countries with limited resources. Malaria is transmitted by the female Anopheles mosquito. Five species within the Plasmodium genus are known to cause infection in humans; namely Plasmodium falciparum, Plasmodium Ovale, Plasmodium knowlesi, Plasmodium vivax and Plasmodium malariae. The increased resistance by the parasite to the majority of available anti-malarial drugs has raised a great challenge in anti-malarial drug discovery. With the problem of drug resistance on the rise, the need to develop new anti-malarial treatment strategies and identification of alternative metabolic targets for the treatment of malaria is crucial. This study is focused on the Guanosine triphosphate CycloHydrolase I (GCH1) enzyme as a potential drug target. GCH1 is important for the survival of malaria parasites as shown by failed attempts to generate knockout lines in plasmodium falciparum. In this study, sequence and evolutionary analysis were carried out in both the human host and parasite GCH1 enzyme. Accurate 3D models of the parasite GCH1 were built and validated. The resulting models were used for high throughput screening against 623 compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/). The high throughput screening was done to identify possible binding sites as well as hit compounds with high selectivity and binding affinity towards the parasite enzyme, this is followed by molecular dynamics simulations to identify protein-ligand complexes and analyze their stability. In this study, a total of five SANCDB compounds were identified as potential inhibitors: SANC00317, SANC00335, SANC00368, SANC00106, SANC00103 and SANC00286. It was found that GCH1 protein can be a potential anti-malarial drug target as it showed selective binding with the inhibitor compounds. The identified inhibitors showed good selectivity and lower free energy of binding towards the parasite GCH1. Force field parameters of GCH1 active site metal were derived and validated. The development of these force field parameters was important for accurate MD simulations of the protein active site; which will allow for future investigation of interactions and stability of the GCH1 protein-ligand complexes