Using bioinformatics tools to screen for trypanosomal cathepsin B cysteine protease inhibitors from the SANCDB as a novel therapeutic modality against Human African Trypanosomiasis (HAT)

Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a fatal chronic disease that is caused by flagellated protozoans, Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense. HAT is spread by a bite from an infected tsetse fly of the Glosina genus. Up to 60 million people in 36 countries in sub-Saharan Africa are at a risk of infection from HAT with up to 30 000 deaths reported every year. Current chemotherapy for HAT is insufficient since the available drugs exhibit unacceptable side effects (toxicity) and parasite resistance. Novel treatments and approaches for development of specific and more potent drugs for HAT are therefore required. One approach is to target vital proteins that are essential to the life cycle of the parasite. The main interest of this study is to explore Trypanosoma brucei cathepsin B-like protease (TbCatB) structural and functional properties with the primary goal of discovering non peptide small molecule inhibitors of TbCatB using bioinformatics approaches. TbCatB is a papain family C1 cysteine protease which belongs to clan CA group and it has emerged as a potential HAT drug target. Papain family cysteine proteases of Clan CA group of Trypanosoma brucei (rhodesain and TbCatB) have demonstrated potential as chemotherapeutic targets using synthetic protease inhibitors like Z-Phe-Ala-CHN2 to kill the parasite in vitro and in vivo. TbCatB has been identified as the essential cysteine protease of T. brucei since mRNA silencing of TbCatB killed the parasite and resulted in a cure in mice infected with T. brucei while mRNA silencing of rhodesain only extended mice life. TbCatB is therefore a promising drug target against HAT and the discovery and development of compounds that can selectively inhibit TbCatB without posing any danger to the human host represent a great therapeutic solution for treatment of HAT. To understand protein-inhibitor interactions, useful information can be obtained from high resolution protease-inhibitor crystal structure complexes. This study aims to use bioinformatics approaches to carry out comparative sequence, structural and functional analysis of TbCatB protease and its homologs from T. congolense, T, cruzi, T. vivax and H. sapien as well as to identify non-peptide small molecule inhibitors of TbCatB cysteine proteases from natural compounds of South African origin. Sequences of TbCatB (PDB ID: 3HHI) homologs were retrieved by a BLAST search. Human cathepsin B (PDB ID: 3CBJ) was selected from a list of templates for homology modelling found by HHpred. MODELLER version 9.10 program was used to generate a hundred models for T. congolense, T, cruzi and T. vivax cathepsin B like proteases using 3HHI and 3CBJ as templates. The best models were chosen based on their low DOPE Z scores before validation using MetaMQAPII, ANOLEA, PROCHECK and QMEAN6. The DOPE Z scores and the RMSD (RMS) values of the calculated models indicate that the models are of acceptable energy (stability) and fold (conformation). Results from the different MQAPs indicate the models are of acceptable quality and they can be used for docking studies. High throughput screening of SANCDB using AutoDock Vina revealed nine compounds, SANC00 478, 479, 480, 481, 482, 488, 489, 490 and 491, having a strong affinity for Trypanosoma spp. cathepsin B proteases than HsCatB. SANC00488 has the strongest binding to Trypanosoma spp. cathepsin B proteases and the weakest binding to HsCatB protease. Molecular dynamics (MD) simulations show that the complexes between SANC00488 and TbCatB, TcCatB, TcrCatB and TvCatB are stable and do not come apart during simulation. The complex between this compound and HsCatB however is unstable and comes apart during simulation. Residues that are important for the stability of SANC00488-TbCatB complex are Gly328 of the S2 subsite, Phe208, and Ala256. In conclusion SANC00488 is a good candidate for development of a drug against HAT

Optimization, random resampling, and modeling in bioinformatics

Quantitative phenotypes regulated by multiple genes are prevalent in nature and many diseases falls into this category. High-throughput sequencing and high-performance computing provides a basis to understand quantitative phenotypes. However, finding a statistical approach correctly model the phenotypes remain a challenging problem. In this work, I present a resampling-based approach to obtain biological functional categories from gene set and apply the approach to analyze lithium-sensitivity of neurological diseases and cancer. Then, the non-parametrical permutation-based approach is applied to evaluate the performance of a GWAS modeling procedure. While the procedure performs well in statistics, search space reduction is required to address the computation challenge