Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro

Are protein-ligand docking programs good enough to predict experimental poses of noncovalent ligands bound to the SARS-CoV-2 main protease?

Hundreds of virtual screening (VS) studies have targeted the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease (M-pro) to identify small molecules that inhibit its proteolytic action. Most studies use AutoDock Vina or Glide methodologies [high-throughput VS (HTVS), standard precision (SP), or extra precision (XP)], independently or in a VS workflow. Moreover, the Protein Data Bank (PDB) includes multiple complexes between M-pro and various noncovalent ligands, providing an excellent benchmark for assessing the predictive capabilities of docking programs. Here, we analyze the ability of the three Glide methodologies and AutoDock Vina by using various target structures/preparations to predict the experimental poses of these complexes. Our aims are to optimize target setup and docking methodologies, minimize false positives, and maximize the identification of various chemotypes in a SARS-CoV-2 M-pro noncovalent inhibitor VS campaign.

An in-silico investigation based on molecular simulations of novel and potential brain-penetrant GluN2B NMDA receptor antagonists as anti-stroke therapeutic agents

GluN2B-induced activation of NMDA receptors plays a key function in central nervous system (CNS) disorders, including Parkinson, Alzheimer, and stroke, as it is strongly involved in excitotoxicity, which makes selective NMDA receptor antagonists one of the potential therapeutic agents for the treatment of neurodegenerative diseases, especially stroke. The present study aims to examine a structural family of thirty brain-penetrating GluN2B N-methyl-D-aspartate (NMDA) receptor antagonists, using virtual computer-assisted drug design (CADD) to discover highly candidate drugs for ischemic strokes. Initially, the physicochemical and ADMET pharmacokinetic properties confirmed that C13 and C22 compounds were predicted as non-toxic inhibitors of CYP2D6 and CYP3A4 cytochromes, with human intestinal absorption (HIA) exceeding 90%, and designed to be as efficient central nervous system (CNS) agents due to the highest probability to cross the blood-brain barrier (BBB). Compared to ifenprodil, a co-crystallized ligand complexed with the transport protein encoded as 3QEL.pdb, we have noticed that C13 and C22 chemical compounds were defined by good ADME-Toxicity profiles, meeting Lipinski, Veber, Egan, Ghose, and Muegge rules. The molecular docking results indicated that C22 and C13 ligands react specifically with the amino acid residues of the NMDA receptor subunit GluN1 and GluN2B. These intermolecular interactions produced between the candidate drugs and the targeted protein in the B chain remain stable over 200 nanoseconds of molecular dynamics simulation time. In conclusion, C22 and C13 ligands are highly recommended as anti-stroke therapeutic drugs due to their safety and molecular stability towards NMDA receptors.Communicated by Ramaswamy H. Sarma.

New N-(1,3,4-thiadiazole-2-yl)acetamide derivatives as human carbonic anhydrase I and II and acetylcholinesterase inhibitors

Various carbonic anhydrase (CA) enzyme isoforms are known today. In addition to the use of CA inhibitors as diuretics, antiepileptics and antiglaucoma agents, the inhibition of other specific isoforms of CA was reported to have clinical benefits in cancers. In this study, two groups of 1,3,4-thiadiazole derivatives were designed and synthesized to act as human CA I and II (hCA I and hCA II) inhibitors. The activities of these compounds were tested in vitro and evaluated in silico studies. The activity of the synthesized compounds was also tested against acetylcholinesterase (AChE) to evaluate the relation of the newly designed structures to the activity against AChE. The synthesized compounds were analyzed by 1H NMR,13C NMR and high-resolution mass spectroscopy (HRMS). The results displayed a better activity of all the synthesized compounds against hCA I than that of the commonly used standard drug, Acetazolamide (AAZ). The compounds also showed better activity against hCA II, except for compounds 5b and 6b. Only compounds 6a and 6c showed superior activity against AChE compared to the standard agent, tacrine (THA). In silico studies, including absorption, distribution, metabolism and excretion (ADME) and drug-likeness evaluation, molecular docking, molecular dynamic simulations (MDSs) and density functional theory (DFT) calculations, were compatible with the in vitro results and presented details regarding the structure-activity relationship.Communicated by Ramaswamy H. Sarma.

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

QSAR modelling, molecular docking, molecular dynamic and ADMET prediction of pyrrolopyrimidine derivatives as novel Bruton's tyrosine kinase (BTK) inhibitors

In recent years, there has been a focus on developing and discovering novel Bruton's tyrosine kinase (BTK) inhibitors, as they offer an effective treatment strategy for B-cell malignancies. BTK plays a crucial role in B cell receptor (BCR)-mediated activation and proliferation by regulating downstream factors such as the NF-κB and MAP kinase pathways. To address this challenge and propose potential therapeutic options for B-cell lymphomas, researchers conducted 2D-QSAR and ADMET studies on pyrrolopyrimidine derivatives that act as inhibitors of the BCR site in cytochrome b. These studies aim to improve and identify new compounds that could serve as more potent potential BTK inhibitors, which would lead to the identification of new drug candidates in this field. In our study, we used 2D-QSAR (multiple linear regression, multiple nonlinear regression, and artificial neural networks), molecular docking, molecular dynamics, and ADMET properties to investigate the potential of 35 pyrrolopyrimidine derivatives as BTK inhibitors. A molecular docking study and molecular dynamics simulations of molecule 13 over 10 ns revealed that it establishes multiple hydrogen bonds with several residues and exhibits frequent stability throughout the simulation period. Based on the results obtained by molecular modeling, we proposed six new compounds (Pred1, Pred2, Pred3, Pred4, Pred5, and Pred6) with highly significant predicted activity by MLR models. A study based on the in silico evaluation of the predicted ADMET properties of the new candidate molecules is strongly recommended to classify these molecules as promising candidates for new anticancer agents specifically designed to target Bruton's tyrosine kinase (BTK) inhibition.

Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon

In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.

Molecular Modeling Unveils the Effective Interaction of B-RAF Inhibitors with Rare B-RAF Insertion Variants

The Food and Drug Administration (FDA) has approved MAPK inhibitors as a treatment for melanoma patients carrying a mutation in codon V600 of the BRAF gene exclusively. However, BRAF mutations outside the V600 codon may occur in a small percentage of melanomas. Although these rare variants may cause B-RAF activation, their predictive response to B-RAF inhibitor treatments is still poorly understood. We exploited an integrated approach for mutation detection, tumor evolution tracking, and assessment of response to treatment in a metastatic melanoma patient carrying the rare p.T599dup B-RAF mutation. He was addressed to Dabrafenib/Trametinib targeted therapy, showing an initial dramatic response. In parallel, in-silico ligand-based homology modeling was set up and performed on this and an additional B-RAF rare variant (p.A598_T599insV) to unveil and justify the success of the B-RAF inhibitory activity of Dabrafenib, showing that it could adeptly bind both these variants in a similar manner to how it binds and inhibits the V600E mutant. These findings open up the possibility of broadening the spectrum of BRAF inhibitor-sensitive mutations beyond mutations at codon V600, suggesting that B-RAF V600 WT melanomas should undergo more specific investigations before ruling out the possibility of targeted therapy.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Structural Manipulations of Marine Natural Products Inspire a New Library of 3-Amino-1,2,4-Triazine PDK Inhibitors Endowed with Antitumor Activity in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the main aggressive types of cancer, characterized by late prognosis and drug resistance. Among the main factors sustaining PDAC progression, the alteration of cell metabolism has emerged to have a key role in PDAC cell proliferation, invasion, and resistance to standard chemotherapeutic agents. Taking into account all these factors and the urgency in evaluating novel options to treat PDAC, in the present work we reported the synthesis of a new series of indolyl-7-azaindolyl triazine compounds inspired by marine bis-indolyl alkaloids. We first assessed the ability of the new triazine compounds to inhibit the enzymatic activity of pyruvate dehydrogenase kinases (PDKs). The results showed that most of derivatives totally inhibit PDK1 and PDK4. Molecular docking analysis was executed to predict the possible binding mode of these derivatives using ligand-based homology modeling technique. Evaluation of the capability of new triazines to inhibit the cell growth in 2D and 3D KRAS-wild-type (BxPC-3) and KRAS-mutant (PSN-1) PDAC cell line, was carried out. The results showed the capacity of the new derivatives to reduce cell growth with a major selectivity against KRAS-mutant PDAC PSN-1 on both cell models. These data demonstrated that the new triazine derivatives target PDK1 enzymatic activity and exhibit cytotoxic effects on 2D and 3D PDAC cell models, thus encouraging further structure manipulation for analogs development against PDAC.

1,2,4-Amino-triazine derivatives as pyruvate dehydrogenase kinase inhibitors: Synthesis and pharmacological evaluation

Among the different hallmarks of cancer, deregulation of cellular metabolism turned out to be an essential mechanism in promoting cancer resistance and progression. The pyruvate dehydrogenase kinases (PDKs) are well known as key regulators in cells metabolic process and their activity was found to be overexpressed in different metabolic alerted types of cancer, including the high aggressive pancreatic ductal adenocarcinoma (PDAC). To date few PDK inhibitors have been reported, and the different molecules developed are characterized by structural chemical diversity. In an attempt to find novel classes of potential PDK inhibitors, the molecular hybridization approach, which combine two or more active scaffolds in a single structure, was employed. Herein we report the synthesis and the pharmacological evaluation of the novel hybrid molecules, characterized by the fusion of three different pharmacophoric sub-units such as 1,2,4-amino triazines, 7-azaindoles and indoles, in a single structure. The synthesized derivatives demonstrated a promising ability in hampering the enzymatic activity of PDK1 and 4, further confirmed by docking studies. Interestingly, these derivatives retained a strong antiproliferative activity against pancreatic cancer cells either in 2D and 3D models. Mechanistic studies in highly aggressive PDAC cells confirmed their ability to hamper PDK axis and to induce cancer cell death by apoptosis. Moreover, in vivo translational studies in a murine syngeneic solid tumor model confirmed the ability of the most representative compounds to target the PDK system and highlight the ability to reduce the tumor growth without inducing substantial body weight changes in the treated mice.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Discovery of the 3-Amino-1,2,4-triazine-Based Library as Selective PDK1 Inhibitors with Therapeutic Potential in Highly Aggressive Pancreatic Ductal Adenocarcinoma

Pyruvate dehydrogenase kinases (PDKs) are serine/threonine kinases, that are directly involved in altered cancer cell metabolism, resulting in cancer aggressiveness and resistance. Dichloroacetic acid (DCA) is the first PDK inhibitor that has entered phase II clinical; however, several side effects associated with weak anticancer activity and excessive drug dose (100 mg/kg) have led to its limitation in clinical application. Building upon a molecular hybridization approach, a small library of 3-amino-1,2,4-triazine derivatives has been designed, synthesized, and characterized for their PDK inhibitory activity using in silico, in vitro, and in vivo assays. Biochemical screenings showed that all synthesized compounds are potent and subtype-selective inhibitors of PDK. Accordingly, molecular modeling studies revealed that a lot of ligands can be properly placed inside the ATP-binding site of PDK1. Interestingly, 2D and 3D cell studies revealed their ability to induce cancer cell death at low micromolar doses, being extremely effective against human pancreatic KRAS mutated cancer cells. Cellular mechanistic studies confirm their ability to hamper the PDK/PDH axis, thus leading to metabolic/redox cellular impairment, and to ultimately trigger apoptotic cancer cell death. Remarkably, preliminary in vivo studies performed on a highly aggressive and metastatic Kras-mutant solid tumor model confirm the ability of the most representative compound 5i to target the PDH/PDK axis in vivo and highlighted its equal efficacy and better tolerability profile with respect to those elicited by the reference FDA approved drugs, cisplatin and gemcitabine. Collectively, the data highlights the promising anticancer potential of these novel PDK-targeting derivatives toward obtaining clinical candidates for combatting highly aggressive KRAS-mutant pancreatic ductal adenocarcinomas.

QSAR, ADME-Tox, molecular docking and molecular dynamics simulations of novel selective glycine transporter type 1 inhibitors with memory enhancing properties

A structural class of forty glycine transporter type 1 (GlyT1) inhibitors, was examined using molecular modeling techniques. The quantitative structure-activity relationships (QSAR) technology confirmed that human GlyT1 activity is strongly and significantly affected by constitutional, geometrical, physicochemical and topological descriptors. ADME-Tox in-silico pharmacokinetics revealed that L28 and L30 ligands were predicted as non-toxic inhibitors with a good ADME profile and the highest probability to penetrate the central nervous system (CNS). Molecular docking results indicated that the predicted inhibitors block GlyT1, reacting specifically with Phe319, Phe325, Tyr123, Tyr 124, Arg52, Asp475, Ala117, Ala479, Ile116 and Ile483 amino acids of the dopamine transporter (DAT) membrane protein. These results were qualified and strengthened using molecular dynamics (MD) study, which affirmed that the established intermolecular interactions for (L28, L30-DAT protein) complexes remain perfectly stable along 50 ns of MD simulation time. Therefore, they could be strongly recommended as therapeutics in medicine to improve memory performance.

Novel chiral Schiff base Palladium(II), Nickel(II), Copper(II) and Iron(II) complexes: Synthesis, characterization, anticancer activity and molecular docking studies

In this study, two chiral Schiff base ligands (L1 and L2) were synthesized from the condensation reaction of (S)-2-amino-3-phenyl-1-propanol with 2-hydroxybenzaldehyde and 2-hydroxy-1-naphthaldehyde as metal precursors for the preparation of transition metal complexes with Pd(II), Fe(II), Ni(II) and Cu(II). The compounds were characterized by using X-ray (for L1-Pd(II)), NMR, FT-IR, UV-Vis, magnetic susceptibility, molar conductivity, and elemental analysis. The in vitro cytotoxic effects of ligands (L1 and L2) and their metal complexes on colon cancer cells (DLD-1), breast cancer cells (MDA-MB-231) and healthy lung human cell lines were investigated by using the 3-(4,5-dimethylthiazol-2-yl)-2,5‑diphenyl tetrazolium bromide (MTT) assay. Among the synthesized compounds, L1-Pd(II) was particularly found to be the most potent anticancer drug candidate in this series with IC50 values of 4.07, and 9.97 µM in DLD-1 and MDA-MB-231 cell lines, respectively. In addition, molecular docking results indicate that Glu122, Asn103, Ala104, Lys126, Phe114, Leu123, and Lys126 amino acids are the binding site of the colon cancer antigen protein, in which the most active complex, L1-Pd(II) can inhibit the current target.

Bat coronaviruses related to SARS-CoV-2: what about their 3CL proteases (MPro)?

Despite a huge effort by the scientific community to determine the animal reservoir of SARS-CoV-2, which led to the identification of several SARS-CoV-2-related viruses both in bats and in pangolins, the origin of SARS-CoV-2 is still not clear. Recently, Temmam et al. reported the discovery of bat coronaviruses with a high degree of genome similarity with SARS-CoV-2, especially concerning the RBDs of the S protein, which mediates the capability of such viruses to enter and therefore infect human cells through a hACE2-dependent pathway. These viruses, especially the one named BANAL-236, showed a higher affinity for the hACE2 compared to the original strain of SARS-CoV-2. In the present work, we analyse the similarities and differences between the 3CL protease (main protease, Mpro) of these newly reported viruses and SARS-CoV-2, discussing their relevance relative to the efficacy of existing therapeutic approaches against COVID-19, particularly concerning the recently approved orally available Paxlovid, and the development of future ones.

From the Wuhan-Hu-1 strain to the XD and XE variants: is targeting the SARS-CoV-2 spike protein still a pharmaceutically relevant option against COVID-19?

Since the outbreak of the COVID-19 pandemic in December 2019, the SARS-CoV-2 genome has undergone several mutations. The emergence of such variants has resulted in multiple pandemic waves, contributing to sustaining to date the number of infections, hospitalisations, and deaths despite the swift development of vaccines, since most of these mutations are concentrated on the Spike protein, a viral surface glycoprotein that is the main target for most vaccines. A milestone in the fight against the COVID-19 pandemic has been represented by the development of Paxlovid, the first orally available drug against COVID-19, which acts on the Main Protease (Mpro). In this article, we analyse the structural features of both the Spike protein and the Mpro of the recently reported SARS-CoV-2 variant XE, as well the closely related XD and XF ones, discussing their impact on the efficacy of existing treatments against COVID-19 and on the development of future ones.

QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia

Forty-four bicyclo ((aryl) methyl) benzamides, acting as glycine transporter type 1 (GlyT1) inhibitors, are developed using molecular modeling techniques. QSAR models generated by multiple linear and non-linear regressions affirm that the biological inhibitory activity against the schizophrenia disease is strongly and significantly correlated with physicochemical, geometrical and topological descriptors, in particular: Hydrogen bond donor, polarizability, surface tension, stretch and torsion energies and topological diameter. According to in silico ADMET properties, the most active ligands (L6, L9, L30, L31 and L37) are the molecules having the highest probability of penetrating the central nervous system (CNS), but the molecule 32 has the highest probability of being absorbed by the gastrointestinal tract. Molecular docking results indicate that Tyr124, Phe43, Phe325, Asp46, Phe319 and Val120 amino acids are the active sites of the dopamine transporter (DAT) membrane protein, in which the most active ligands can inhibit the glycine transporter type 1 (GlyT1). The results of molecular dynamics (MD) simulation revealed that all five inhibitors remained stable in the active sites of the DAT protein during 100 ns, demonstrating their promising role as candidate drugs for the treatment of schizophrenia.