In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions

Protein surface analysis by dimension reduction with applications in functional annotation and drug target prediction

The protein structure initiatives have increased the number of experimentally determined protein tertiary structures, providing tremendous opportunities for detailed comparative analysis of proteins. Although protein structures provide the most exquisite type of molecular information that can yield mechanistic insights into how proteins function, there are still many protein structures with undetermined or poorly defined functions. Functional annotation from protein 3-D structures has attracted many researchers, with most approaches relying on structural superposition against well-characterized proteins. 3-D structure superposition is a complex and computationally demanding problem; forcing most available approaches to only consider backbone atoms for simplicity and efficiency. In this study, we propose protein surface as a more powerful representation of proteins than the traditional backbone or atomic representations. In order to efficiently analyze protein surfaces, we introduce a novel approach to reduce the 3-D surface to a 2-D image map and utilize image registration algorithms to compare these feature-rich images. Whereas the dimension reduction inherently captures the 3-D geometry of the surface patches, we enrich the image map with additional features known to be important for defining molecular activity of the proteins, such as curvature, electrostatic potential, hydrophobicity, and residue conservation. Comparison of these enriched surface maps using image registration methods allows us to find similar surface patches shared between proteins. While the computational challenges remain to scale our approach to study comparisons in the entire set of available protein structures, our novel approach provides unique advantages compared to other structure comparison methods. We show that our method is able to detect local similarities even when proteins lack a global structure similarity. We also demonstrate the utility of the image maps and their comparisons in functional annotation, and drug target prediction tasks.Ph.D., Biomedical Engineering -- Drexel University, 201

The design, synthesis and evaluation of Nrf2-Keap1 PPI inhibitors: a modular, virtual screening-led approach

Nrf2 (nuclear factor erythroid 2-related factor 2) is a cap’n’collar bZIP transcription factor and is the main activator of the transcription of over 100 genes that play roles in responses to oxidative stress and detoxifying xenobiotics. The main control of Nrf2 levels is exercised by Keap1 (Kelchlike ECH-associated protein 1) which facilitates the ubiquitination of Nrf2 and therefore its degradation. Keap1 is oxidation-sensitive and upon exposure to oxidants, it changes its conformation and binds Nrf2 tightly. Consequently, de novo-synthesised Nrf2 can accumulate. Following its discovery, Nrf2 received most attention in relation to cancer. Over the time, however, its implication in other pathologies have been more and more acknowledged, namely in inflammation and most importantly in neurodegenerative diseases. Especially Parkinson’s disease (PD), which is the second most common neurodegenerative disease, caused by the progressive loss of dopaminergic neurons in the substantia nigra, has been linked to oxidative stress. The role Nrf2 plays has been demonstrated in animal models of α-synuclein aggregation or chemically induced parkinsonism, where an increase in Nrf2 expression provided neuroprotection and a slowing of disease progression. Therefore, the inhibition of Keap1- mediated Nrf2 degradation presents a promising strategy for the mechanistic study and the therapy of PD. Several structures showing high potency towards Keap1 inhibition have been described, with activities in the nanomolar range. However, these compounds are large, or hydrophilic and charged. In order to develop new scaffolds, extensive virtual screening assays have been conducted which resulted in hits with promising molecular scaffolds. At the same time, chemical modifications on a known triazole structure have been performed in order to elucidate structure-activity relationships. In this thesis, the molecular modelling lead, as well the synthetic approach to both project components are described. Finally, the results of a competitive fluorescence polarisation (FP) assay for the second set of compounds are presented