Identification of distant drug off-targets by direct superposition of binding pocket surfaces

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

PatchSearch: A Fast Computational Method for Off-Target Detection

Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .

Towards a critical evaluation of an empirical and volume-based solvation function for ligand docking

Molecular docking is an important tool for the discovery of new biologically active molecules given that the receptor structure is known. An excellent environment for the development of new methods and improvement of the current methods is being provided by the rapid growth in the number of proteins with known structure. The evaluation of the solvation energies outstands among the challenges for the modeling of the receptor-ligand interactions, especially in the context of molecular docking where a fast, though accurate, evaluation is ought to be achieved. Here we evaluated a variation of the desolvation energy model proposed by Stouten (Stouten P.F.W. et al, Molecular Simulation, 1993, 10: 97–120), or SV model. The SV model showed a linear correlation with experimentally determined solvation energies, as available in the database FreeSolv. However, when used in retrospective docking simulations using the benchmarks DUD, charged-matched DUD and DUD-Enhanced, the SV model resulted in poorer enrichments when compared to a pure force field model with no correction for solvation effects. The data provided here is consistent with other empirical solvation models employed in the context of molecular docking and indicates that a good model to account for solvent effects is still a goal to achieve. On the other hand, despite the inability to improve the enrichment of retrospective simulations, the SV solvation model showed an interesting ability to reduce the number of molecules with net charge -2 and -3 e among the top-scored molecules in a prospective test.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

A Novel Antiviral Target Structure Involved in the RNA Binding, Dimerization, and Nuclear Export Functions of the Influenza A Virus Nucleoprotein

Developing antiviral therapies for influenza A virus (IAV) infection is an ongoing process because of the rapid rate of antigenic mutation and the emergence of drug-resistant viruses. The ideal strategy is to develop drugs that target well-conserved, functionally restricted, and unique surface structures without affecting host cell function. We recently identified the antiviral compound, RK424, by screening a library of 50,000 compounds using cell-based infection assays. RK424 showed potent antiviral activity against many different subtypes of IAV in vitro and partially protected mice from a lethal dose of A/WSN/1933 (H1N1) virus in vivo. Here, we show that RK424 inhibits viral ribonucleoprotein complex (vRNP) activity, causing the viral nucleoprotein (NP) to accumulate in the cell nucleus. In silico docking analysis revealed that RK424 bound to a small pocket in the viral NP. This pocket was surrounded by three functionally important domains: the RNA binding groove, the NP dimer interface, and nuclear export signal (NES) 3, indicating that it may be involved in the RNA binding, oligomerization, and nuclear export functions of NP. The accuracy of this binding model was confirmed in a NP-RK424 binding assay incorporating photo-cross-linked RK424 affinity beads and in a plaque assay evaluating the structure-activity relationship of RK424. Surface plasmon resonance (SPR) and pull-down assays showed that RK424 inhibited both the NP-RNA and NP-NP interactions, whereas size exclusion chromatography showed that RK424 disrupted viral RNA-induced NP oligomerization. In addition, in vitro nuclear export assays confirmed that RK424 inhibited nuclear export of NP. The amino acid residues comprising the NP pocket play a crucial role in viral replication and are highly conserved in more than 7,000 NP sequences from avian, human, and swine influenza viruses. Furthermore, we found that the NP pocket has a surface structure different from that of the pocket in host molecules. Taken together, these results describe a promising new approach to developing influenza virus drugs that target a novel pocket structure within NP.

Influenza A virus nucleoprotein (NP) is a highly conserved multifunctional protein that plays an essential role in infection by all subtypes of influenza A virus, making it an attractive target for new antiviral drugs. NP regulates viral polymerase activity and transport of the viral genome into/from the host cell nucleus by forming the viral ribonucleoprotein complex (vRNP). Because NP regulates replication and transcription of the viral genome in addition to its role in nuclear export (all of which are essential for the production of viral progeny), it is a promising drug target. Here, we used the antiviral compound RK424 to identify a novel pocket structure within NP. This structure encompassed three different functional domains that are involved in the above-mentioned replication steps. RK424 inhibits viral genome replication/transcription and nuclear export of NP by destabilizing the NP oligomer and inhibiting the binding of chromosome region maintenance 1 (CRM1) to NP via nuclear export signal (NES) 3, which is located in close proximity to the NP pocket. Taken together, these findings suggest that this small NP pocket is a novel antiviral target.