RCD+: Fast Loop Modeling Server

Modeling loops is a critical and challenging step in protein modeling and prediction. We have developed a quick online service (http://rcd.chaconlab.org) for ab initio loop modeling combining a coarse-grained conformational search with a full-atom refinement. Our original Random Coordinate Descent (RCD) loop closure algorithm has been greatly improved to enrich the sampling distribution towards near-native conformations. These improvements include a new workflow optimization, MPI-parallelization and fast backbone angle sampling based on neighbor-dependent Ramachandran probability distributions. The server starts by efficiently searching the vast conformational space from only the loop sequence information and the environment atomic coordinates. The generated closed loop models are subsequently ranked using a fast distance-orientation dependent energy filter. Top ranked loops are refined with the Rosetta energy function to obtain accurate all-atom predictions that can be interactively inspected in an user-friendly web interface. Using standard benchmarks, the average root mean squared deviation (RMSD) is 0.8 and 1.4 angstrom for 8 and 12 residues loops, respectively, in the challenging modeling scenario in where the side chains of the loop environment are fully remodeled. These results are not only very competitive compared to those obtained with public state of the art methods, but also they are obtained similar to 10-fold faster

SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

The in silico prediction of foot-and-mouth disease virus (FMDV) epitopes on the South African territories (SAT)1, SAT2 and SAT3 serotypes

Foot-and-mouth disease (FMD) is a highly contagious and economically important disease that affects even-toed hoofed mammals. The FMD virus (FMDV) is the causative agent of FMD, of which there are seven clinically indistinguishable serotypes. Three serotypes, namely, South African Territories (SAT)1, SAT2 and SAT3 are endemic to southern Africa and are the most antigenically diverse among the FMDV serotypes. A negative consequence of this antigenic variation is that infection or vaccination with one virus may not provide immune protection from other strains or it may only confer partial protection. The identification of B-cell epitopes is therefore key to rationally designing cross-reactive vaccines that recognize the immunologically distinct serotypes present within the population. Computational epitope prediction methods that exploit the inherent physicochemical properties of epitopes in their algorithms have been proposed as a cost and time-effective alternative to the classical experimental methods. The aim of this project is to employ in silico epitope prediction programmes to predict B-cell epitopes on the capsids of the SAT serotypes. Sequence data for 18 immunologically distinct SAT1, SAT2 and SAT3 strains from across southern Africa were collated. Since, only one SAT1 virus has had its structure elucidated by X-ray crystallography (PDB ID: 2WZR), homology models of the 18 virus capsids were built computationally using Modeller v9.12. They were then subjected to energy minimizations using the AMBER force field. The quality of the models was evaluated and validated stereochemically and energetically using the PROMOTIF and ANOLEA servers respectively. The homology models were subsequently used as input to two different epitope prediction servers, namely Discotope1.0 and Ellipro. Only those epitopes predicted by both programmes were defined as epitopes. Both previously characterised and novel epitopes were predicted on the SAT strains. Some of the novel epitopes are located on the same loops as experimentally derived epitopes, while others are located on a putative novel antigenic site, which is located close to the five-fold axis of symmetry. A consensus set of 11 epitopes that are common on at least 15 out of 18 SAT strains was collated. In future work, the epitopes predicted in this study will be experimentally validated using mutagenesis studies. Those found to be true epitopes may be used in the rational design of broadly reactive SAT vaccinesLife and Consumer SciencesM. Sc. (Life Sciences

De Novo Protein Structure Modeling from Cryoem Data Through a Dynamic Programming Algorithm in the Secondary Structure Topology Graph

Proteins are the molecules carry out the vital functions and make more than the half of dry weight in every cell. Protein in nature folds into a unique and energetically favorable 3-Dimensional (3-D) structure which is critical and unique to its biological function. In contrast to other methods for protein structure determination, Electron Cryorricroscopy (CryoEM) is able to produce volumetric maps of proteins that are poorly soluble, large and hard to crystallize. Furthermore, it studies the proteins in their native environment. Unfortunately, the volumetric maps generated by current advances in CryoEM technique produces protein maps at medium resolution about (~5 to 10Å) in which it is hard to determine the atomic-structure of the protein. However, the resolution of the volumetric maps is improving steadily, and recent works could obtain atomic models at higher resolutions (~3Å). De novo protein modeling is the process of building the structure of the protein using its CryoEM volumetric map. Thereupon, the volumetric maps at medium resolution generated by CryoEM technique proposed a new challenge. At the medium resolution, the location and orientation of secondary structure elements (SSE) can be visually and computationally identified. However, the order and direction (called protein topology) of the SSEs detected from the CryoEM volumetric map are not visible. In order to determine the protein structure, the topology of the SSEs has to be figured out and then the backbone can be built. Consequently, the topology problem has become a bottle neck for protein modeling using CryoEM In this dissertation, we focus to establish an effective computational framework to derive the atomic structure of a protein from the medium resolution CryoEM volumetric maps. This framework includes a topology graph component to rank effectively the topologies of the SSEs and a model building component. In order to generate the small subset of candidate topologies, the problem is translated into a layered graph representation. We developed a dynamic programming algorithm (TopoDP) for the new representation to overcome the problem of large search space. Our approach shows the improved accuracy, speed and memory use when compared with existing methods. However, the generating of such set was infeasible using a brute force method. Therefore, the topology graph component effectively reduces the topological space using the geometrical features of the secondary structures through a constrained K-shortest paths method in our layered graph. The model building component involves the bending of a helix and the loop construction using skeleton of the volumetric map. The forward-backward CCD is applied to bend the helices and model the loops

Therapeutic strategy to end Tuberculosis (TB) world: structural and functional characterization of potential weak hotspots of Mycobacterium tuberculosis molecular targets from combinatorial in silico perspective.

Doctoral Degree. University of KwaZulu-Natal, Durban.The world has witnessed several decades of Tuberculosis (TB) pandemic and numerous advanced scientific efforts to control the invasiveness of the newly evolving Mycobacterium tuberculosis strains (Mtb) resulting in drug resistance. TB disease has killed hundreds of millions of humans and left millions maimed that need to be rehabilitated; about 10.0 million infections and 1.5 million annually in the last decade. Drug-resistant TB has remained more challenging in the previous 20 years than drug-susceptible TB and is chromosomal mutations-associated in selected genes of the Mtb. Notable mutations identified by biomarkers are related to phenotypic drug resistance, and these include; an 81 bp region in rpoB gene with > 95 % mutations in rifampicin (RIF) clinical isolates and katG gene promoter of the mabA-inhA showed to be associated with INH-resistance. Different Strategies, including the recent WHO End TB approach, have been employed to alleviate or stop TB. The current identification of the critical roles of Mtb demethylmenaquinone methyltransferase (menG) target in the survival, pathogenesis, virulence, and drug resistance created an avenue for the development of efficacious therapeutics that can eradicate TB. MenG is a member of the methyltransferase superfamily. It catalyzes one of the last steps of the menaquinone biosynthesis pathway, requires for maintenance of the Mtb cell envelope. The other two studied targets investigated in this work are N-acetylglucosamine-6-phosphate deacetylase enzyme (NagA), which represents a critical enzymatic step in the production of essential amino sugar required by Mtb for the cell wall biosynthesis and the secreted antigen 85C enzyme (Ag85C) target. The latter target catalyzes the synthesis of trehalose derivatives and attachment of mycolic acids. These targets have gained considerable attention in drug discovery pipelines. However, there is little information about menG, as it lasks structural dynamics due to the lack of crystal structure, active site regions, and amino acids of it Mycobacteria homologs. Similarly, the dynamics of the NagA and Ag85C proteins structure are still unknown. Therefore, justifications led to the modelling of the 3D Structure of menG to understand the structural and functional features that could be investigated at the atomistic level. Homology models were also created for the five (5) mycobacterial homologs. Furthermore, the inevitable need for new drugs has led to the application of in silico techniques including molecular modelling and molecular dynamics simulations, which provide opportunities for the chemists to evaluate and assess numerous compounds that can lead to potential drugs against the mycobacterial disease. Furthermore, these computational techniques justify the present incorporation of several computational tools integrated into this study to provide insights into the conformational changes that illuminate potential inhibitory mechanism, identification of the binding site amino acids, and characterization. Here, we analyze the weak hotspots dynamics specific to each of the Mtb targets, most notably the loop and active residues around or within the ligand-binding sites to obtain useful findings for the design of higher efficacious potential antitubercular drugs. Molecular dynamics simulations were performed to gain molecular standpoints of the conformational binding of the experimental drugs, which were reported to be highly effective against each respective target. Structural dynamics and motions behaviour of menG upon the binding of inhibitor (DG70, biphenyl amide compound) were estimated. Additional in silico thermodynamic analyses were further employed to explore intuitions into the binding mode of each inhibitor mainly for the proposed binding site of menG to identify the residues for binding. Sequence analysis of the homologs of Mycobacterium tuberculosis NagA and Ag85C targets, including those of smegmatis, marinum, leprae, ulcerans, were performed to obtain unique sequence similarities and differences and the structural and functional characterization upon the binding of the ligand. An experimental protocol let to the discovery of a selective covalent inhibitor, β- isomer monocyclic enolphosphorus Cycliphostin, of Ag85C SER-124. Moreover, chapter 4 also unravels the impact of the function of the non-synonymous single nucleotide polymorphisms of NagA target. The desired expectation is that the implementation of the information extricated from this study would provide the structural silhouette for pharmaceutical scientists and molecular biologists to abet in the identification and design of novel antimycobacterial drugs most especially for TB

Structural bioinformatics studies and tool development related to drug discovery

This thesis is divided into two distinct sections which can be combined under the broad umbrella of structural bioinformatics studies related to drug discovery. The first section involves the establishment of an online South African natural products database. Natural products (NPs) are chemical entities synthesised in nature and are unrivalled in their structural complexity, chemical diversity, and biological specificity, which has long made them crucial to the drug discovery process. South Africa is rich in both plant and marine biodiversity and a great deal of research has gone into isolating compounds from organisms found in this country. However, there is no official database containing this information, making it difficult to access for research purposes. This information was extracted manually from literature to create a database of South African natural products. In order to make the information accessible to the general research community, a website, named “SANCDB”, was built to enable compounds to be quickly and easily searched for and downloaded in a number of different chemical formats. The content of the database was assessed and compared to other established natural product databases. Currently, SANCDB is the only database of natural products in Africa with an online interface. The second section of the thesis was aimed at performing structural characterisation of proteins with the potential to be targeted for antimalarial drug therapy. This looked specifically at 1) The interactions between an exported heat shock protein (Hsp) from Plasmodium falciparum (P. falciparum), PfHsp70-x and various host and exported parasite J proteins, as well as 2) The interface between PfHsp90 and the heat shock organising protein (PfHop). The PfHsp70-x:J protein study provided additional insight into how these two proteins potentially interact. Analysis of the PfHsp90:PfHop also provided a structural insight into the interaction interface between these two proteins and identified residues that could be targeted due to their contribution to the stability of the Hsp90:Hop binding complex and differences between parasite and human proteins. These studies inspired the development of a homology modelling tool, which can be used to assist researchers with homology modelling, while providing them with step-by-step control over the entire process. This thesis presents the establishment of a South African NP database and the development of a homology modelling tool, inspired by protein structural studies. When combined, these two applications have the potential to contribute greatly towards in silico drug discovery research

Modeling Structures and Motions of Loops in Protein Molecules

Unlike the secondary structure elements that connect in protein structures, loop fragments in protein chains are often highly mobile even in generally stable proteins. The structural variability of loops is often at the center of a protein’s stability, folding, and even biological function. Loops are found to mediate important biological processes, such as signaling, protein-ligand binding, and protein-protein interactions. Modeling conformations of a loop under physiological conditions remains an open problem in computational biology. This article reviews computational research in loop modeling, highlighting progress and challenges. Important insight is obtained on potential directions for future research