Aleix Gimeno

The Protein Data Bank (PDB) contains more than 235,000 three‐dimensional biostructures and is growing at a rate of nearly 10% per year. The PDB is essential to gain knowledge on how proteins and ligands interact and how these interactions are correlated with the quantitative activity of each ligand/target pair. Unfortunately, the lack of a tool that can classify structures as apo or holo, that is by no means straightforward, and differentiate between covalent and non‐covalent ligand–protein complexes makes it difficult to obtain the structures that belong to each set. To address this issue, we present PDB‐CAT, a user‐friendly tool that facilitates the categorization and extraction of key information from PDBx/mmCIF files through an efficient parallelized implementation. PDB‐CAT uses a blacklist‐based approach to automatically identify the ligand in a complex. It then classifies the PDB files based on ligand presence: structures without a ligand are classified as apo, whereas those with a ligand are classified as covalently or non‐covalently bound, depending on the type of binding. As well as making this classification, the program can verify if there are any mutations in the protein sequence by comparing it to a reference sequence. An example is included to illustrate two different uses: the classification of SARS‐CoV‐2 Main Protease complexes depending on their variant, and the complete screening of the PDBbindv2020, achieved in <10 min. PDB‐CAT is now available on GitHub ( https://github.com/URV-cheminformatics/PDB-CAT ) and the corresponding tutorial on GitBook ( https://ariadnallopps-organization.gitbook.io/pdb-cat ).

The Protein Data Bank contains more than 223,000 three dimensional biostructures and is growing at a rate of nearly 10% per year. The lack of a tool that facilitates the classification between apo and holo structures and differentiates between covalent and non-covalent ligand protein complexes, makes it difficult to manage a large number of structures. To address this issue, we present PDBCAT, a user-friendly tool that facilitates the categorization and extraction of key information from PDBx/mmCIF files. PDB-CAT is a program that classifies a group of protein structures based on their ligands into three categories: apo, covalently, and non-covalently bonded. Besides this classification, the program can verify if there are any mutations in the protein sequence by comparing it to a reference sequence. PDB-CAT is designed to be user-friendly, with its output clearly defining every entity present in each entry to facilitate decision-making. PDB-CAT is now available on GitHub (https://github.com/URV-cheminformatics/PDB-CAT).

Matrix metalloproteinase 13 plays a central role in osteoarthritis (OA), as its overexpression induces an excessive breakdown of collagen that results in an imbalance between collagen synthesis and degradation in the joint, leading to progressive articular cartilage degradation. Therefore, MMP-13 has been proposed as a key therapeutic target for OA. Here we have developed a virtual screening workflow aimed at identifying selective non-zinc-binding MMP-13 inhibitors by targeting the deep S1′ pocket of MMP-13. Three ligands were found to inhibit MMP-13 in the µM range, and one of these showed selectivity over other MMPs. A structure-based analysis guided the chemical optimization of the hit compound, leading to the obtaining of a new N-acyl hydrazone-based derivative with improved inhibitory activity and selectivity for the target enzyme.

This review makes a critical evaluation of 61 peer‐reviewed manuscripts that use a docking step in a virtual screening (VS) protocol to predict SARS‐CoV‐2 M‐pro (M‐pro) inhibitors in approved or investigational drugs. Various manuscripts predict different compounds, even when they use a similar initial dataset and methodology, and most of them do not validate their methodology or results. In addition, a set of known 150 SARS‐CoV‐2 M‐pro inhibitors extracted from the literature and a second set of 81 M‐pro inhibitors and 113 inactive compounds obtained from the COVID Moonshot project were used to evaluate the reliability of using docking scores as feasible predictors of the potency of a SARS‐CoV‐2 M‐pro inhibitor. Using two SARS‐CoV‐2 M‐pro structures and five protein‐ligand docking programs, we proved that the correlation between the pIC₅₀ and docking scores is not good. Neither was any correlation found between the pIC₅₀ and the ∆G calculated with an MM‐GBSA method. When a group of experimentally known inactive compounds was added, neither the docking scores or the ∆G were able to distinguish between compounds with or without M‐pro experimental inhibitory activity. Performances improved when covalent and noncovalent inhibitors were treated separately, but were not good enough to fully support using a docking score as a cutoff value for selecting new putative M‐pro inhibitors or predicting the relative bioactivity of a compound by comparison with a reference compound. The two sets of known SARS‐CoV‐2 M‐pro inhibitors presented here could be used for validating future VS protocols which aim to predict M‐pro inhibitors.

Matrix metalloproteinases (MMPs) are the family of proteases that are mainly responsible for degrading extracellular matrix (ECM) components. In the skin, the overexpression of MMPs as a result of ultraviolet radiation triggers an imbalance in the ECM turnover in a process called photoaging, which ultimately results in skin wrinkling and premature skin ageing. Therefore, the inhibition of different enzymes of the MMP family at a topical level could have positive implications for photoaging. Considering that the MMP catalytic region is mostly conserved across different enzymes of the MMP family, in this study we aimed to design a virtual screening (VS) workflow to identify broad-spectrum MMP inhibitors that can be used to delay the development of photoaging. Our in silico approach was validated in vitro with 20 VS hits from the Specs library that were not only structurally different from one another but also from known MMP inhibitors. In this bioactivity assay, 18 of the 20 compounds inhibit at least one of the assayed MMPs at 100 μM (with 5 of them showing around 50% inhibition in all the tested MMPs at this concentration). Finally, this VS was used to identify natural products that have the potential to act as broad-spectrum MMP inhibitors and be used as a treatment for photoaging.

Since the outbreak of the COVID-19 pandemic in December 2019 and its rapid spread worldwide, the scientific community has been under pressure to react and make progress in the development of an effective treatment against the virus responsible for the disease. Here, we implement an original virtual screening (VS) protocol for repositioning approved drugs in order to predict which of them could inhibit the main protease of the virus (M-pro), a key target for antiviral drugs given its essential role in the virus’ replication. Two different libraries of approved drugs were docked against the structure of M-pro using Glide, FRED and AutoDock Vina, and only the equivalent high affinity binding modes predicted simultaneously by the three docking programs were considered to correspond to bioactive poses. In this way, we took advantage of the three sampling algorithms to generate hypothetic binding modes without relying on a single scoring function to rank the results. Seven possible SARS-CoV-2 M-pro inhibitors were predicted using this approach: Perampanel, Carprofen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin and ethyl biscoumacetate. Carprofen and Celecoxib have been selected by the COVID Moonshot initiative for in vitro testing; they show 3.97 and 11.90% M-pro inhibition at 50 µM, respectively.

Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.

Protein tyrosine phosphatase 1B (PTP1B) is a potential drug target for diabetes and obesity. However, the design of PTP1B inhibitors that combine potency and bioavailability is a great challenge, and new leads are needed to circumvent this problem. Virtual screening (VS) workflows can be used to find new PTP1B inhibitors with little chemical similarity to existing inhibitors. Unfortunately, previous VS workflows for the identification of PTP1B inhibitors have several limitations, such as a small number of experimentally tested compounds and the low bioactivity of those compounds. We developed a VS workflow capable of identifying 15 structurally diverse PTP1B inhibitors from 20 compounds, the bioactivity of which was tested in vitro. Moreover, we identified two PTP1B inhibitors with the highest bioactivity reported by any VS campaign (i.e., IC₅₀ values of 1.4 and 2.1 μm), which could be used as new lead compounds.