Accelerating drug discovery

Tryptase is a tetrameric serine protease and a key component of mast cell granules. Here, we explored an integrated approach to characterize tryptase ligands, combining novel experimental binding studies using Surface Plasmon Resonance, with in silico analysis through the Exscalate platform. For this, we focused on three inhibitors previously reported in the literature, including a bivalent inhibitor and its corresponding monovalent compound. All three ligands showed concentration-dependent binding to immobilized human tryptase with the bivalent inhibitor showing the highest affinity. Furthermore, Rmax values were similar, indicating that the compounds occupy all four binding pockets of the tryptase tetramer. This hypothesis was supported by in silico computational analysis that revealed the binding mode of the monovalent ligand, one in each monomer pocket, compared with crystal structure of the bivalent one, which simultaneously occupies two binding pockets. Additionally, we solved the 2.06 Å X-ray crystal structures of human Tryptase beta-2 (hTPSB2), in both its apo form and in complex with compound #1, experimentally confirming the binding mode and the key molecular interactions predicted by docking studies for this compound. This integrated approach offers a robust framework for elucidating both the strength and mode of interaction of potential tryptase inhibitors.

SARS-CoV-2 encodes a 3C-like protease (3CLpro) that is essential for viral replication. This cysteine protease cleaves viral polyproteins to release functional nonstructural proteins, making it a prime target for antiviral drug development. We investigated the inhibitory effects of halicin, a known c-Jun N-terminal kinase inhibitor, on 3CLpro. Mass spectrometry and crystallographic analysis revealed that halicin covalently binds to several cysteine residues in 3CLpro. As expected, Cys145, the catalytic residue, was found to be the most targeted residue by halicin. Secondly, Cys44 was found to be modified, suggesting a potential inhibitory role of this residue. A mutant protease (Cys44Ala) was generated to further understand the function of Cys44. In silico and enzymatic assays showed that the mutation significantly reduced the stability and activity of 3CLpro, indicating the importance of Cys44 in maintaining the active conformation of the protease. Differential scanning fluorimetry assays confirmed this evidence, showing a reduced thermal stability of the mutant compared to the wild-type protease. Our results highlight the potential of halicin as a multi-target inhibitor of 3CLpro and underline the importance of Cys44 in the function of the protease. These findings contribute to the development of effective antiviral therapies against COVID-19 by targeting critical residues in 3CLpro.

Successful virtual screening of therapeutically relevant proteins depends on selecting optimal protein structures and accurately identifying druggable binding sites. This study presents a novel methodology that combines pocket analysis, molecular docking, and molecular dynamics simulations to prioritize protein binding sites, using SARS-CoV-2 spike protein as a case study. The methodology begins by collecting a comprehensive dataset of resolved spike protein structures and known inhibitors. A pocket search identified potential binding sites across various protein conformations, followed by docking simulations to evaluate ligand-binding affinities. We introduce a novel algorithm COMPASS: COMputational Pocket Analysis and Scoring System includes the calculation of Pocket Frequency Score, which assesses pocket relevance based on the frequency of key residues, was introduced to refine pocket selection. This scoring system was combined with traditional pocket and docking scores to produce a Global Score, enabling the ranking of pockets. The top-ranked pockets underwent molecular dynamics simulations and free energy calculations to assess their stability and druggability. Six out of the ten best-ranked pockets demonstrated stable interactions with all tested inhibitors, highlighting their potential as drug targets. The study found that the selected pockets not only showed significant structural uniqueness but also correlated well with experimentally validated binding sites, confirming the method’s effectiveness. In conclusion, the proposed algorithm and the method enhance the accuracy of structure-based drug discovery by enabling the rational selection of protein-binding pockets. It shows potential for enhancing virtual screening efforts, especially for proteins with numerous available experimental structures, where selecting an optimal structure is critical.

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values. The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability. The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Drug discovery is a long and costly process. Recent studies demonstrated how the introduction of an in-silico stage, named virtual screening, that suggests which molecule to test in-vitro, increases the drug discovery success probability. In the context of urgent computing, where it is important to find a therapeutic solution in a short time frame, the number of candidates that we can virtual screen is limited only by the available computation power. In this paper, we focus on LiGen, the virtual screening application of the EXSCALATE platform. In particular, we address two challenges of performing an extreme-scale virtual screening on a modern HPC system. The first one is posed by hardware heterogeneity, where GPUs of different vendors account for a large fraction of their performance. The second challenge concerns the operational difficulties of running the campaign since it requires significant effort and technical skills that are not common among domain experts. We show how hinging on SYCL and the LEXIS Platform, is the solution that the EXSCALATE Platform uses to address these challenges.

[This corrects the article DOI: 10.1016/j.csbj.2024.05.024.].

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

The C-terminal-binding protein 1/brefeldin A ADP-ribosylation substrate (CtBP1/BARS) acts both as an oncogenic transcriptional co-repressor and as a fission inducing protein required for membrane trafficking and Golgi complex partitioning during mitosis, hence for mitotic entry. CtBP1/BARS overexpression, in multiple cancers, has pro-tumorigenic functions regulating gene networks associated with “cancer hallmarks” and malignant behavior including: increased cell survival, proliferation, migration/invasion, epithelial-mesenchymal transition (EMT). Structurally, CtBP1/BARS belongs to the hydroxyacid-dehydrogenase family and possesses a NAD(H)-binding Rossmann fold, which, depending on ligands bound, controls the oligomerization of CtBP1/BARS and, in turn, its cellular functions. Here, we proposed to target the CtBP1/BARS Rossmann fold with small molecules as selective inhibitors of mitotic entry and pro-tumoral transcriptional activities.

In recent years, there has been a growing interest in developing high-performance implementations of drug discovery processing software. To target modern GPU architectures, such applications are mostly written in proprietary languages such as CUDA or HIP. However, with the increasing heterogeneity of modern HPC systems and the availability of accelerators from multiple hardware vendors, it has become critical to be able to efficiently execute drug discovery pipelines on multiple large-scale computing systems, with the ultimate goal of working on urgent computing scenarios. This article presents the challenges of migrating LiGen, an industrial drug discovery software pipeline, from CUDA to the SYCL programming model, an industry standard based on C++ that enables heterogeneous computing. We perform a structured analysis of the performance portability of the SYCL LiGen platform, focusing on different aspects of the approach from different perspectives. First, we analyze the performance portability provided by the high-level semantics of SYCL, including the most recent group algorithms and subgroups of SYCL 2020. Second, we analyze how low-level aspects such as kernel occupancy and register pressure affect the performance portability of the overall application. The experimental evaluation is performed on two different versions of LiGen, implementing two different parallelization patterns, by comparing them with a manually optimized CUDA version, and by evaluating performance portability using both known and ad hoc metrics. The results show that, thanks to the combination of high-level SYCL semantics and some manual tuning, LiGen achieves native-comparable performance on NVIDIA, while also running on AMD GPUs.