Computational anti-COVID-19 drug design: progress and challenges.

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Modeling the CRL4A ligase complex to predict target protein ubiquitination induced by cereblon-recruiting PROTACs.

PROteolysis TArgeting Chimeras (PROTACs) are hetero-bifunctional small molecules that can simultaneously recruit target proteins and E3 ligases to form a ternary complex, promoting target protein ubiquitination and degradation via the Ubiquitin-Proteasome System (UPS). PROTACs have gained increasing attention in recent years due to certain advantages over traditional therapeutic modalities and enabling targeting of previously "undruggable" proteins. To better understand the mechanism of PROTAC-induced Target Protein Degradation (TPD), several computational approaches have recently been developed to study and predict ternary complex formation. However, mounting evidence suggests that ubiquitination can also be a rate-limiting step in PROTAC-induced TPD. Here, we propose a structure-based computational approach to predict target protein ubiquitination induced by cereblon (CRBN)-based PROTACs by leveraging available structural information of the CRL4A ligase complex (CRBN/DDB1/CUL4A/Rbx1/NEDD8/E2/Ub). We generated ternary complex ensembles with Rosetta, modeled multiple CRL4A ligase complex conformations, and predicted ubiquitination efficiency by separating the ternary ensemble into productive and unproductive complexes based on the proximity of the ubiquitin to accessible lysines on the target protein. We validated our CRL4A ligase complex models with published ternary complex structures and additionally employed our modeling workflow to predict ubiquitination efficiencies and sites of a series of cyclin-dependent kinases (CDKs) after treatment with TL12-186, a pan-kinase PROTAC. Our predictions are consistent with CDK ubiquitination and site-directed mutagenesis of specific CDK lysine residues as measured using a NanoBRET ubiquitination assay in HEK293 cells. This work structurally links PROTAC-induced ternary formation and ubiquitination, representing an important step toward prediction of target "degradability."

Solvation free energy arithmetic for small organic molecules.

Solvent-mediated interactions contribute to ligand binding affinities in computational drug design and provide a challenge for theoretical predictions. In this study, we analyze the solvation free energy of benzene derivatives in water to guide the development of predictive models for solvation free energies and solvent-mediated interactions. We use a spatially resolved analysis of local solvation free energy contributions and define solvation free energy arithmetic, which enable us to construct additive models to describe the solvation of complex compounds. The substituents analyzed in this study are carboxyl and nitro-groups due to their similar sterical requirements but distinct interactions with water. We find that nonadditive solvation free energy contributions are primarily attributed to electrostatics, which are qualitatively reproduced with computationally efficient continuum models. This suggests a promising route for the development of efficient and accurate models for the solvation of complex molecules with varying substitution patterns using solvation arithmetic.

Computational Methods for Anticancer Drug Discovery; The MCT4 Paradigm.

Modern anticancer research has employed advanced computational techniques and artificial intelligence methods for drug discovery and development, along with the massive amount of generated clinical and in silico data over the last decades. Diverse computational techniques and state-of-the-art algorithms are being developed to enhance traditional Rational Drug Design pipelines and achieve cost-efficient and successful anticancer candidates to promote human health. Towards this direction, we have developed a pharmacophore- based drug design approach against MCT4, a member of the monocarboxylate transporter family (MCT), which is the main carrier of lactate across the membrane and highly involved in cancer cell metabolism. Specifically, MCT4 is a promising target for therapeutic strategies as it overexpresses in glycolytic tumors, and its inhibition has shown promising anticancer effects. Due to the lack of experimentally determined structure, we have elucidated the key features of the protein through an in silico drug design strategy, including for molecular modelling, molecular dynamics, and pharmacophore elucidation, towards the identification of specific inhibitors as a novel anti-cancer strategy.

An Optimized Cloud Computing Method for Extracting Molecular Descriptors.

Extracting molecular descriptors from chemical compounds is an essential preprocessing phase for developing accurate classification models. Supervised machine learning algorithms offer the capability to detect "hidden" patterns that may exist in a large dataset of compounds, which are represented by their molecular descriptors. Assuming that molecules with similar structure tend to share similar physicochemical properties, large chemical libraries can be screened by applying similarity sourcing techniques in order to detect potential bioactive compounds against a molecular target. However, the process of generating these compound features is time-consuming. Our proposed methodology not only employs cloud computing to accelerate the process of extracting molecular descriptors but also introduces an optimized approach to utilize the computational resources in the most efficient way.

Data-Driven Approaches Used for Compound Library Design for the Treatment of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015-2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.

1,2-Dibenzoylhydrazine as a Multi-Inhibitor Compound: A Morphological and Docking Study.

In the framework of the multitarget inhibitor study, we report an in silico analysis of 1,2-dibenzoylhydrazine (DBH) with respect to three essential receptors such as the ecdysone receptor (EcR), urease, and HIV-integrase. Starting from a crystallographic structural study of accidentally harvested crystals of this compound, we performed docking studies to evaluate the inhibitory capacity of DBH toward three selected targets. A crystal morphology prediction was then performed. The results of our molecular modeling calculations indicate that DBH is an excellent candidate as a ligand to inhibit the activity of EcR receptors and urease. Docking studies also revealed the activity of DBH on the HIV integrase receptor, providing an excellent starting point for developing novel inhibitors using this molecule as a starting lead compound.

Cyanide Bridged Platinum-Iron Complexes as Cisplatin Prodrug Systems: Design and Computational Study.

The potential role of cyanide-bridged platinum-iron complexes as an anti-cancer Pt(IV) prodrug is studied. We present design principles of a dual-function prodrug that can upon reduction dissociate and release concurrently six cisplatin units and a ferricyanide anion per prodrug unit. The prodrug molecule is a unique complex of hepta metal centers consisting of a ferricyanide core with six Pt(IV) centers each bonded to the Fe(III) core through a cyano ligand. The functionality of the prodrug is addressed through density functional theory (DFT) calculations.

SAMPL7: Host-guest binding prediction by molecular dynamics and quantum mechanics.

Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges provide routes to compare chemical quantities determined using computational chemistry approaches to experimental measurements that are shared after the competition. For this effort, several computational methods have been used to calculate the binding energies of Octa Acid (OA) and exo-Octa Acid (exoOA) host-guest systems for SAMPL7. The initial poses for molecular dynamics (MD) were generated by molecular docking. Binding free energy calculations were performed using molecular mechanics combined with Poisson-Boltzmann or generalized Born surface area solvation (MMPBSA/MMGBSA) approaches. The factors that affect the utility of the MMPBSA/MMGBSA approaches including solvation, partial charge, and solute entropy models were also analyzed. In addition to MD calculations, quantum mechanics (QM) calculations were performed using several different density functional theory (DFT) approaches. From SAMPL6 results, B3PW91-D3 was found to overestimate binding energies though it was effective for geometry optimizations, so it was considered for the DFT geometry optimizations in the current study, with single-point energy calculations carried out with B2PLYP-D3 with double-, triple-, and quadruple-ζ level basis sets. Accounting for dispersion effects, and solvation models was deemed essential for the predictions. MMGBSA and MMPBSA correlated better to experiment when used in conjunction with an empirical/linear correction.

Stacking Gaussian processes to improve [Formula: see text] predictions in the SAMPL7 challenge.

Accurate predictions of acid dissociation constants are essential to rational molecular design in the pharmaceutical industry and elsewhere. There has been much interest in developing new machine learning methods that can produce fast and accurate pKa predictions for arbitrary species, as well as estimates of prediction uncertainty. Previously, as part of the SAMPL6 community-wide blind challenge, Bannan et al. approached the problem of predicting [Formula: see text]s by using a Gaussian process regression to predict microscopic [Formula: see text]s, from which macroscopic [Formula: see text] values can be analytically computed (Bannan et al. in J Comput-Aided Mol Des 32:1165-1177). While this method can make reasonably quick and accurate predictions using a small training set, accuracy was limited by the lack of a sufficiently broad range of chemical space in the training set (e.g., the inclusion of polyprotic acids). Here, to address this issue, we construct a deep Gaussian Process (GP) model that can include more features without invoking the curse of dimensionality. We trained both a standard GP and a deep GP model using a database of approximately 3500 small molecules curated from public sources, filtered by similarity to targets. We tested the model on both the SAMPL6 and more recent SAMPL7 challenge, which introduced a similar lack of ionizable sites and/or environments found between the test set and the previous training set. The results show that while the deep GP model made only minor improvements over the standard GP model for SAMPL6 predictions, it made significant improvements over the standard GP model in SAMPL7 macroscopic predictions, achieving a MAE of 1.5 [Formula: see text].

Testing automatic methods to predict free binding energy of host-guest complexes in SAMPL7 challenge.

The design of new host-guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host-guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host-guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach.

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Molecular and cellular studies evaluating a potent 2-cyanoindolizine catechol diether NNRTI targeting wildtype and Y181C mutant HIV-1 reverse transcriptase.

The development of efficacious NNRTIs for HIV/AIDS therapy is commonly met with the emergence of drug resistant strains, including the Y181C variant. Using a computationally-guided approach, we synthesized the catechol diether series of NNRTIs, which display sub-nanomolar potency in cellular assays. Among the most potent were a series of 2-cyanoindolizine substituted catechol diethers, including Compound 1. We present here a thorough evaluation of this compound, including biochemical, cellular, and structural studies. The compound demonstrates low nanomolar potency against both WT and Y181C HIV-1 RT in in vitro and cellular assays. Our crystal structures of both the wildtype and mutant forms of RT in complex with Compound 1 allow the interrogation of this compound's features that allow it to maintain strong efficacy against the drug resistant mutant. Among these are compensatory shifts in the NNRTI binding pocket, persistence of multiple hydrogen bonds, and van der Waals contacts throughout the binding site. Further, the fluorine at the C6 position of the indolizine moiety makes multiple favorable interactions with both RT forms. The present study highlights the indolizine-substituted catechol diether class of NNRTIs as promising therapeutic candidates possessing optimal pharmacological properties and significant potency against multiple RT variants.

Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4.

For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from three-dimensional structures, there are few detailed reports on how best to construct and use datasets. Here, we examined how different datasets affected the prediction of the binding mode of CYP3A4 by a three-dimensional neural network when the number of crystal structures for the target protein was limited. We used four different training datasets: one large, general dataset containing various protein complexes and three smaller, more specific datasets containing complexes with CYP3A4-like pockets, complexes with CYP3A4-binding ligands, and complexes with CYP protein family members. We then trained models with different combinations of datasets with or without subsequent fine-tuning and evaluated the binding mode prediction performance of each model. The best receiver operating characteristic (ROC) area under the curve (AUC) model with respect to area under the receiver operating characteristic curve was obtained by training with a combination of the general protein and CYP family datasets. However, the ROC AUC-recall balanced model was obtained by training with this combination of datasets followed by fine-tuning with the CYP3A4-binding ligands dataset. Our results suggest that datasets that balance protein functionality and data size are important for optimizing binding mode prediction performance. In addition, datasets with large median binding pocket sizes may be important for the binding mode prediction specifically of CYP3A4.

Considerations on the Rational Design of Covalently Conjugated Cell-Penetrating Peptides (CPPs) for Intracellular Delivery of Proteins: A Guide to CPP Selection Using Glucarpidase as the Model Cargo Molecule.

Access of proteins to their intracellular targets is limited by a hydrophobic barrier called the cellular membrane. Conjugation with cell-penetrating peptides (CPPs) has been shown to improve protein transduction into the cells. This conjugation can be either covalent or non-covalent, each with its unique pros and cons. The CPP-protein covalent conjugation may result in undesirable structural and functional alterations in the target protein. Therefore, we propose a systematic approach to evaluate different CPPs for covalent conjugations. This guide is presented using the carboxypeptidase G2 (CPG2) enzyme as the target protein. Seventy CPPs -out of 1155- with the highest probability of uptake efficiency were selected. These peptides were then conjugated to the N- or C-terminus of CPG2. Translational efficacy of the conjugates, robustness and thermodynamic properties of the chimera, aggregation possibility, folding rate, backbone flexibility, and aspects of in vivo administration such as protease susceptibility were predicted. The effect of the position of conjugation was evaluated using unpaired t-test (p < 0.05). It was concluded that N-terminal conjugation resulted in higher quality constructs. Seventeen CPP-CPG2/CPG2-CPP constructs were identified as the most promising. Based on this study, the bioinformatics workflow that is presented may be universally applied to any CPP-protein conjugate design.

Rational design of small molecules that modulate the transcriptional function of the response regulator PhoP.

The response regulator PhoP, which is part of the PhoP/PhoQ two-component system, regulates the expression of multiple genes involved in controlling virulence in Salmonella enterica serovar Typhimurium and other species of Gram-negative bacteria. Modulating the phosphorylation-mediated dimerization in the receiver domain may interfere with the transcriptional function of PhoP. In this study, we analyzed the therapeutic potential of the PhoP receiver domain by exploring it as a potential target for drug design. The structural information was then applied to identify the first hit compounds from commercial chemical libraries by combining pharmacophore modelling and docking methods with a GFP (Green Fluorescent Protein)-based promoter-fusion bioassay. In total, one hundred and forty compounds were selected, purchased, and tested for biological activity. Several novel scaffolds showed acceptable potency to modulate the transcriptional function of PhoP, either by enhancing or inhibiting the expression of PhoP-dependent genes. These compounds may be used as the starting point for developing modulators that target the protein-protein interface of the PhoP protein as an alternative strategy against antibiotic resistance.

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.

An integrated approach with new strategies for QSAR models and lead optimization.

BACKGROUND: Computational drug design approaches are important for shortening the time and reducing the cost for drug discovery and development. Among these methods, molecular docking and quantitative structure activity relationship (QSAR) play key roles for lead discovery and optimization. Here, we propose an integrated approach with core strategies to identify the protein-ligand hot spots for QSAR models and lead optimization. These core strategies are: 1) to generate both residue-based and atom-based interactions as the features; 2) to identify compound common and specific skeletons; and 3) to infer consensus features for QSAR models. RESULTS: We evaluated our methods and new strategies on building QSAR models of human acetylcholinesterase (huAChE). The leave-one-out cross validation values q 2 and r 2 of our huAChE QSAR model are 0.82 and 0.78, respectively. The experimental results show that the selected features (resides/atoms) are important for enzymatic functions and stabling the protein structure by forming key interactions (e.g., stack forces and hydrogen bonds) between huAChE and its inhibitors. Finally, we applied our methods to arthrobacter globiformis histamine oxidase (AGHO) which is correlated to heart failure and diabetic. CONCLUSIONS: Based on our AGHO QSAR model, we identified a new substrate verified by bioassay experiments for AGHO. These results show that our methods and new strategies can yield stable and high accuracy QSAR models. We believe that our methods and strategies are useful for discovering new leads and guiding lead optimization in drug discovery.

Review structure- and dynamics-based computational design of anticancer drugs.

Cancer is a class of highly complex diseases involving multiple genes and multiple cross-talks between signaling networks. Cancer cells may be developed from inherited defects or acquired damages of DNA. However, many cancers are resistant to treatment, and metastasis of cancers makes the disease even more intractable. Secondary malignancies are frequently observed after cancer chemotherapy. The call for more effective cancer therapy is obligatory. Using drug-cocktails that combine multiple anti-cancer agents working in different mechanisms has been a standard treatment of cancers to overcome the drug resistance problem. More recently, design of multiple ligands (may be more easily understood as "multiple target ligands"), i.e., single agents that target multiple biomolecules in a rational manner, receives increasing attention. For those who work on computational drug design, such tasks serve as new opportunities for achieving drugs with more effective pharmacological actions, in addition to designing compounds with better binding affinity, better selectivity, or to discovering compounds that can exert their actions allosterically. Some recent methodological developments on computational drug design are reviewed, and a few recent drug design efforts on a selected set of targets (topoisomerases, Ras proteins, protein kinases, and histone deacetylases) toward cancer treatment and cancer prevention are summarized.

Identification of novel EZH2 inhibitors through pharmacophore-based virtual screening and biological assays.

Polycomb repressive complex 2 (PRC2) acts as a primary writer for di- and tri-methylation of histone H3 at lysine 27. This protein plays an essential role in silencing gene expression. Enhancer of zeste 2 (EZH2), the catalytic subunit of PRC2, is considered as a promising therapeutic target for cancer. GSK126, a specific inhibitor of EZH2, is undergoing phase I trials for hypermethylation-related cancers. In addition, many derivatives of GSK126 are also commonly used in laboratory investigations. However, studies on the mechanism and drug development of EZH2 are limited by the absence of structural diversity of these inhibitors because they share similar SAM-like scaffolds. In this study, we generated a pharmacophore model based on reported EZH2 inhibitors and performed in silico screenings. Experimental validations led to the identification of two novel EZH2 inhibitors, DCE_42 and DCE_254, with IC50 values of 23 and 11µM, respectively. They also displayed significant anti-proliferation activity against lymphoma cell lines. Thus, we discovered potent EZH2 inhibitors with novel scaffold using combined in silico screening and experimental study. Results from this study can also guide further development of novel specific EZH2 inhibitors.