A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

The first laminin G-like domain of protein S is essential for binding and activation of Tyro3 receptor and intracellular signalling.

The homologous proteins Gas6 and protein S (ProS1) are both natural ligands for the TAM (Tyro3, Axl, MerTK) receptor tyrosine kinases. ProS1 selectively activates Tyro3; however, the precise molecular interface of the ProS1-Tyro3 contact has not been characterised. We used a set of chimeric proteins in which each of the C-terminal laminin G-like (LG) domains of ProS1 were swapped with those of Gas6, as well as a set of ProS1 mutants with novel added glycosylations within LG1. Alongside wildtype ProS1, only the chimera containing ProS1 LG1 domain stimulated Tyro3 and Erk phosphorylation in human cancer cells, as determined by Western blot. In contrast, Gas6 and chimeras containing minimally the Gas6 LG1 domain stimulated Axl and Akt phosphorylation. We performed in silico homology modelling and molecular docking analysis to construct and evaluate structural models of both ProS1-Tyro3 and Gas6-Axl ligand-receptor interactions. These analyses revealed a contact between the ProS1 LG1 domain and the first immunoglobulin domain of Tyro3, which was similar to the Gas6-Axl interaction, and involved long-range electrostatic interactions that were further stabilised by hydrophobic and polar contacts. The mutant ProS1 proteins, which had added glycosylations within LG1 but which were all outside of the modelled contact region, all activated Tyro3 in cells with no hindrance. In conclusion, we show that the LG1 domain of ProS1 is necessary for activation of the Tyro3 receptor, involving protein-protein interaction interfaces that are homologous to those of the Gas6-Axl interaction.

Kinase signaling as a drug target modality for regulation of vascular hyperpermeability: A case for ARDS therapy development.

The endothelial vascular permeability barrier has an important role throughout the body's extensive vasculature, and its disruption leads to vascular hyperpermeability (leakage), which is associated with numerous medical conditions. In the lung, vascular hyperpermeability can lead to pulmonary edema and acute respiratory distress syndrome (ARDS), the most severe and deadly complication of viral and bacterial infections, trauma and radiation exposure. There is currently no pharmacological treatment for ARDS with the only approved options being focused on supportive care. The development of effective treatments for ARDS has a potential to turn infectious diseases such as bacterial and viral pneumonia (including COVID-19) into manageable conditions, saving lives and providing a new tool to combat future epidemics. Strategies that aim to protect and augment the vascular endothelial barrier are important avenues to consider as potential treatments for ARDS and other conditions underlined by vascular hyperpermeability. We propose the activation of the MAPKAPK2 (MK2) kinase pathway as a new approach to augment the endothelial barrier and prevent or reverse ARDS and other conditions characterized by vascular barrier dysfunction.

A toolkit for covalent docking with GOLD: from automated ligand preparation with KNIME to bound protein-ligand complexes.

Motivation: Current covalent docking tools have limitations that make them difficult to use for performing large-scale structure-based covalent virtual screening (VS). They require time-consuming tasks for the preparation of proteins and compounds (standardization, filtering according to the type of warheads), as well as for setting up covalent reactions. We have developed a toolkit to help accelerate drug discovery projects in the phases of hit identification by VS of ultra-large covalent libraries and hit expansion by exploration of the binding of known covalent compounds. With this application note, we offer the community a toolkit for performing automated covalent docking in a fast and efficient way. Results: The toolkit comprises a KNIME workflow for ligand preparation and a Python program to perform the covalent docking of ligands with the GOLD docking engine running in a parallelized fashion. Availability and implementation: The KNIME workflow entitled 'Evotec_Covalent_Processing_forGOLD.knwf' for the preparation of the ligands is available in the KNIME Hub https://hub.knime.com/emilie_pihan/spaces. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises.

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Demystifying the Molecular Basis of Pyrazoloquinolinones Recognition at the Extracellular α1+/ß3- Interface of the GABAA Receptor by Molecular Modeling.

GABAA receptors are pentameric ligand-gated ion channels that serve as major inhibitory neurotransmitter receptors in the mammalian brain and the target of numerous clinically relevant drugs interacting with different ligand binding sites. Here, we report an in silico approach to investigate the binding of pyrazoloquinolinones (PQs) that mediate allosteric effects through the extracellular α+/ß- interface of GABAA receptors. First, we docked a potent prototype of PQs into the α1+/ß3- site of a homology model of the human α1ß3γ2 subtype of the GABAA receptor. Next, for each docking pose, we computationally derived protein-ligand complexes for 18 PQ analogs with known experimental potency. Subsequently, binding energy was calculated for all complexes using the molecular mechanics-generalized Born surface area method. Finally, docking poses were quantitatively assessed in the light of experimental data to derive a binding hypothesis. Collectively, the results indicate that PQs at the α1+/ß3- site likely exhibit a common binding mode that can be characterized by a hydrogen bond interaction with ß3Q64 and hydrophobic interactions involving residues α1F99, ß3Y62, ß3M115, α1Y159, and α1Y209. Importantly, our results are in good agreement with the recently resolved cryo-Electron Microscopy structures of the human α1ß3γ2 and α1ß2γ2 subtypes of GABAA receptors.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages.

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases.

SUMMARY: Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer. AVAILABILITY AND IMPLEMENTATION: FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools. CONTACT: bruno.villoutreix@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1).

L-type Amino acid Transporter 1 (LAT1) plays a significant role in the growth and propagation of cancer cells by facilitating the cross-membrane transport of essential nutrients, and is an attractive drug target. Several halogen-containing L-phenylalanine-based ligands display high affinity and high selectivity for LAT1; nonetheless, their molecular mechanism of binding remains unclear. In this study, a combined in silico strategy consisting of homology modeling, molecular docking, and Quantum Mechanics-Molecular Mechanics (QM-MM) simulation was applied to elucidate the molecular basis of ligand binding in LAT1. First, a homology model of LAT1 based on the atomic structure of a prokaryotic homolog was constructed. Docking studies using a set of halogenated ligands allowed for deriving a binding hypothesis. Selected docking poses were subjected to QM-MM calculations to investigate the halogen interactions. Collectively, the results highlight the dual nature of the ligand-protein binding mode characterized by backbone hydrogen bond interactions of the amino acid moiety of the ligands and residues I63, S66, G67, F252, G255, as well as hydrophobic interactions of the ligand's side chains with residues I139, I140, F252, G255, F402, W405. QM-MM optimizations indicated that the electrostatic interactions involving halogens contribute to the binding free energy. Importantly, our results are in good agreement with the recently unraveled cryo-Electron Microscopy structures of LAT1.

Discovery of Potent Inhibitors for the Large Neutral Amino Acid Transporter 1 (LAT1) by Structure-Based Methods.

The large neutral amino acid transporter 1 (LAT1) is a promising anticancer target that is required for the cellular uptake of essential amino acids that serve as building blocks for cancer growth and proliferation. Here, we report a structure-based approach to identify chemically diverse and potent inhibitors of LAT1. First, a homology model of LAT1 that is based on the atomic structures of the prokaryotic homologs was constructed. Molecular docking of nitrogen mustards (NMs) with a wide range of affinity allowed for deriving a common binding mode that could explain the structure-activity relationship pattern in NMs. Subsequently, validated binding hypotheses were subjected to molecular dynamics simulation, which allowed for extracting a set of dynamic pharmacophores. Finally, a library of ~1.1 million molecules was virtually screened against these pharmacophores, followed by docking. Biological testing of the 30 top-ranked hits revealed 13 actives, with the best compound showing an IC50 value in the sub-µM range.

Insights into the Structure, Function, and Ligand Discovery of the Large Neutral Amino Acid Transporter 1, LAT1.

The large neutral amino acid transporter 1 (LAT1, or SLC7A5) is a sodium- and pH-independent transporter, which supplies essential amino acids (e.g., leucine, phenylalanine) to cells. It plays an important role at the Blood⁻Brain Barrier (BBB) where it facilitates the transport of thyroid hormones, pharmaceuticals (e.g., l-DOPA, gabapentin), and metabolites into the brain. Moreover, its expression is highly upregulated in various types of human cancer that are characterized by an intense demand for amino acids for growth and proliferation. Therefore, LAT1 is believed to be an important drug target for cancer treatment. With the crystallization of the arginine/agmatine antiporter (AdiC) from Escherichia Coli, numerous homology models of LAT1 have been built to elucidate the substrate binding site, ligand⁻transporter interaction, and structure⁻function relationship. The use of these models in combination with molecular docking and experimental testing has identified novel chemotypes of ligands of LAT1. Here, we highlight the structure, function, transport mechanism, and homology modeling of LAT1. Additionally, results from structure⁻function studies performed on LAT1 are addressed, which have enhanced our knowledge of the mechanism of substrate binding and translocation. This is followed by a discussion on ligand- and structure-based approaches, with an emphasis on elucidating the molecular basis of LAT1 inhibition. Finally, we provide an exhaustive summary of different LAT1 inhibitors that have been identified so far, including the recently discovered irreversible covalent inhibitors.

Translation Termination Factor GSPT1 Is a Phenotypically Relevant Off-Target of Heterobifunctional Phthalimide Degraders.

Protein degradation is an emerging therapeutic strategy with a unique molecular pharmacology that enables the disruption of all functions associated with a target. This is particularly relevant for proteins depending on molecular scaffolding, such as transcription factors or receptor tyrosine kinases (RTKs). To address tractability of multiple RTKs for chemical degradation by the E3 ligase CUL4-RBX1-DDB1-CRBN (CRL4CRBN), we synthesized a series of phthalimide degraders based on the promiscuous kinase inhibitors sunitinib and PHA665752. While both series failed to induce degradation of their consensus targets, individual molecules displayed pronounced efficacy in leukemia cell lines. Orthogonal target identification supported by molecular docking led us to identify the translation termination factor G1 to S phase transition 1 (GSPT1) as a converging off-target, resulting from inadvertent E3 ligase modulation. This research highlights the importance of monitoring degradation events that are independent of the respective targeting ligand as a unique feature of small-molecule degraders.