Combining Charge Density Analysis with Machine Learning Tools To Investigate the Cruzain Inhibition Mechanism

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Molecular targets for Chagas disease: validation, challenges and lead compounds for widely exploited targets

INTRODUCTION

Chagas disease (CD) imposes social and economic burdens, yet the available treatments have limited efficacy in the disease's chronic phase and cause serious adverse effects. To address this challenge, target-based approaches are a possible strategy to develop new, safe, and active treatments for both phases of the disease.

AREAS COVERED

This review delves into target-based approaches applied to CD drug discovery, emphasizing the studies from the last five years. We highlight the proteins cruzain (CZ), trypanothione reductase (TR), sterol 14 α-demethylase (CPY51), iron superoxide dismutase (Fe-SOD), proteasome, cytochrome b (Cytb), and cleavage and polyadenylation specificity factor 3 (CPSF3), chosen based on their biological and chemical validation as drug targets. For each, we discuss its biological relevance and validation as a target, currently related challenges, and the status of the most promising inhibitors.

EXPERT OPINION

Target-based approaches toward developing potential CD therapeutics have yielded promising leads in recent years. We expect a significant advance in this field in the next decade, fueled by the new options for Trypanosoma cruzi genetic manipulation that arose in the past decade, combined with recent advances in computational chemistry and chemical biology.

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Impact of the Recognition Part of Dipeptidyl Nitroalkene Compounds on the Inhibition Mechanism of Cysteine Proteases Cruzain and Cathepsin L

Cysteine proteases (CPs) are an important class of enzymes, many of which are responsible for several human diseases. For instance, cruzain of protozoan parasite Trypanosoma cruzi is responsible for the Chagas disease, while the role of human cathepsin L is associated with some cancers or is a potential target for the treatment of COVID-19. However, despite paramount work carried out during the past years, the compounds that have been proposed so far show limited inhibitory action against these enzymes. We present a study of proposed covalent inhibitors of these two CPs, cruzain and cathepsin L, based on the design, synthesis, kinetic measurements, and QM/MM computational simulations on dipeptidyl nitroalkene compounds. The experimentally determined inhibition data, together with the analysis and the predicted inhibition constants derived from the free energy landscape of the full inhibition process, allowed describing the impact of the recognition part of these compounds and, in particular, the modifications on the P2 site. The designed compounds and, in particular, the one with a bulky group (Trp) at the P2 site show promising in vitro inhibition activities against cruzain and cathepsin L for use as a starting lead compound in the development of drugs with medical applications for the treatment of human diseases and future designs.

Experimental and Computational Study of Aryl-thiosemicarbazones Inhibiting Cruzain Reveals Reversible Inhibition and a Stepwise Mechanism

Trypanosoma cruzi is a parasite that infects about 6-7 million people worldwide, mostly in Latin America, causing Chagas disease. Cruzain, the main cysteine protease of T. cruzi, is a validated target for developing drug candidates for Chagas disease. Thiosemicarbazones are one of the most relevant warheads used in covalent inhibitors targeting cruzain. Despite its relevance, the mechanism of inhibition of cruzain by thiosemicarbazones is unknown. Here, we combined experiments and simulations to unveil the covalent inhibition mechanism of cruzain by a thiosemicarbazone-based inhibitor (compound 1). Additionally, we studied a semicarbazone (compound 2), which is structurally similar to compound 1 but does not inhibit cruzain. Assays confirmed the reversibility of inhibition by compound 1 and suggested a two-step mechanism of inhibition. The K_i was estimated to be 36.3 μM and K_i* to be 11.5 μM, suggesting the pre-covalent complex to be relevant for inhibition. Molecular dynamics simulations of compounds 1 and 2 with cruzain were used to propose putative binding modes for the ligands. One-dimensional (1D) quantum mechanics/molecular mechanics (QM/MM) potential of mean force (PMF) and gas-phase energies showed that the attack of Cys25-S^- on the C═S or C═O bond yields a more stable intermediate than the attack on the C═N bond of the thiosemicarbazone/semicarbazone. Two-dimensional (2D) QM/MM PMF revealed a putative reaction mechanism for compound 1, involving the proton transfer to the ligand, followed by the Cys25-S^- attack at C═S. The ΔG and energy barrier were estimated to be -1.4 and 11.7 kcal/mol, respectively. Overall, our results shed light on the inhibition mechanism of cruzain by thiosemicarbazones.

Bond Bundle Analysis of Ketosteroid Isomerase

Bond bundle analysis is used to investigate enzymatic catalysis in the ketosteroid isomerase (KSI) active site. We identify the unique bonding regions in five KSI systems, including those exposed to applied oriented electric fields and those with amino acid mutations, and calculate the precise redistribution of electron density and other regional properties that accompanies either enhancement or inhibition of KSI catalytic activity. We find that catalytic enhancement results from promoting both inter- and intra-molecular electron density redistribution, between bond bundles and bond wedges within the KSI-docked substrate molecule, in the forward direction of the catalyzed reaction. Though the redistribution applies to both types of perturbed systems and is thus suggestive of a general catalytic role, we observe that bond properties (e.g., volume vs energy vs electron count) can respond independently and disproportionately depending on the type of perturbation. We conclude that the resulting catalytic enhancement/inhibition proceeds via different mechanisms, where some bond properties are utilized more by one type of perturbation than the other. Additionally, we find that the correlations between bond wedge properties and catalyzed reaction barrier energies are additive to predict those of bond bundles and atomic basins, providing a rigorous grounding for connecting changes in local charge density to resulting shifts in reaction barrier energy.

Computational approaches towards the discovery and optimisation of cruzain inhibitors

The need to develop safer and more efficacious drugs to treat Chagas disease has motivated the search for cruzain inhibitors. Cruzain is the recombinant, truncated version of cruzipain, a cysteine protease from Trypanosoma cruzi with important roles during the parasite life cycle. Several computational techniques have been applied to discover and optimise cruzain inhibitors, providing a molecular basis to guide this process. Here, we review some of the most recent computational studies that provided important information for the design of cruzain inhibitors. Moreover, we highlight the diversity of applications of in silico techniques and their impact.

The gene repertoire of the main cysteine protease of Trypanosoma cruzi, cruzipain, reveals four sub-types with distinct active sites

Cruzipains are the main papain-like cysteine proteases of Trypanosoma cruzi, the protozoan parasite that causes Chagas disease. Encoded by a multigenic family, previous studies have estimated the presence of dozens of copies spread over multiple chromosomes in different parasite strains. Here, we describe the complete gene repertoire of cruzipain in three parasite strains, their genomic organization, and expression pattern throughout the parasite life cycle. Furthermore, we have analyzed primary sequence variations among distinct family members as well as structural differences between the main groups of cruzipains. Based on phylogenetic inferences and residue positions crucial for enzyme function and specificity, we propose the classification of cruzipains into two families (I and II), whose genes are distributed in two or three separate clusters in the parasite genome, according with the strain. Family I comprises nearly identical copies to the previously characterized cruzipain 1/cruzain, whereas Family II encompasses three structurally distinct sub-types, named cruzipain 2, cruzipain 3, and cruzipain 4. RNA-seq data derived from the CL Brener strain indicates that Family I genes are mainly expressed by epimastigotes, whereas trypomastigotes mainly express Family II genes. Significant differences in the active sites among the enzyme sub-types were also identified, which may play a role in their substrate selectivity and impact their inhibition by small molecules.

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.