Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations

Searching potential GSK-3β inhibitors from marine sources using atomistic simulations

Glycogen synthase kinase-3 beta, GSK-3β, is one of the most common targets for cancer treatment. Inhibiting the biological activity of the enzyme can lead to the prevention of cancer development. Especially, estimating a new inhibitor for preventing GSK-3β by using natural compounds is of great interest. In this context, the marine compounds were investigated for their ligand-binding affinity to GSK-3β via atomistic simulations. The compounds, including xanalteric acid I, chaunolidone A, macrolactin V, and aspergiolide A, were suggested that can inhibit GSK-3β via molecular docking and steered-MD simulations. Moreover, the potency of these compounds was also confirmed via the perturbation simulations. Furthermore, the toxicity prediction also indicates that these compounds would adopt less toxicity. Therefore, it may be argued that four compounds can play as potential inhibitors preventing GSK-3β. In addition, the residues including Ile62, Val135, Pro136, Arg141, Lys183, Gln185, Asn186, and Asp200 play a crucial role in the GSK-3β binding process.

Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds?

Covalent-allosteric inhibitors (CAIs) may achieve the best of both worlds: increased potency, long-lasting effects, and reduced drug resistance typical of covalent ligands, along with enhanced specificity and decreased toxicity inherent in allosteric modulators. Therefore, CAIs can be an effective strategy to transform many undruggable targets into druggable ones. However, CAIs are challenging to design. In this perspective, we analyze the discovery of known CAIs targeting three protein families: protein phosphatases, protein kinases, and GTPases. We also discuss how computational methods and tools can play a role in addressing the practical challenges of rational CAI design.

Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations

Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE.

Covalent small-molecule inhibitors of SARS-CoV-2 Mpro: Insights into their design, classification, biological activity, and binding interactions

Since 2020, many compounds have been investigated for their potential use in the treatment of SARS-CoV-2 infection. Among these agents, a huge number of natural products and FDA-approved drugs have been evaluated as potential therapeutics for SARS-CoV-2 using virtual screening and docking studies. However, the identification of the molecular targets involved in viral replication led to the development of rationally designed anti-SARS-CoV-2 agents. Among these targets, the main protease (Mpro) is one of the key enzymes needed in the replication of the virus. The data gleaned from the crystal structures of SARS-CoV-2 Mpro complexes with small-molecule covalent inhibitors has been used in the design and discovery of many highly potent and broad-spectrum Mpro inhibitors. The current review focuses mainly on the covalent type of SARS-CoV-2 Mpro inhibitors. The design, chemistry, and classification of these inhibitors were also in focus. The biological activity of these inhibitors, including their inhibitory activities against Mpro, their antiviral activities, and the SAR studies, were discussed. The review also describes the potential mechanism of the interaction between these inhibitors and the catalytic Cys145 residue in Mpro. Moreover, the binding modes and key binding interactions of these covalent inhibitors were also illustrated. The covalent inhibitors discussed in this review were of diverse chemical nature and origin. Their antiviral activity was mediated mainly by the inhibition of SARS-CoV-2 Mpro, with IC50 values in the micromolar to the nanomolar range. Many of these inhibitors exhibited broad-spectrum inhibitory activity against the Mpro enzymes of other coronaviruses (SARS-CoV-1 and MERS-CoV). The dual inhibition of the Mpro and PLpro enzymes of SARS-CoV-2 could also provide higher therapeutic benefits than Mpro inhibition. Despite the approval of nirmatrelvir by the FDA, many mutations in the Mpro enzyme of SARS-CoV-2 have been reported. Although some of these mutations did not affect the potency of nirmatrelvir, there is an urgent need to develop a second generation of Mpro inhibitors. We hope that the data summarized in this review could help researchers in the design of a new potent generation of SARS-CoV-2 Mpro inhibitors.

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.

Peptide Drug Design Using Alchemical Free Energy Calculation: An Application and Validation on Agonists of Ghrelin Receptor

With recent large-scale applications and validations, the relative binding free energy (RBFE) calculated using alchemical free energy methods has been proven to be an accurate measure to probe the binding of small-molecule drug candidates. On the other hand, given the flexibility of peptides, it is of great interest to find out whether sufficient sampling could be achieved within the typical time scale of such calculation, and a similar level of accuracy could be reached for peptide drugs. However, the systematic evaluation of such calculations on protein-peptide systems has been less reported. Most reported studies of peptides were restricted to a limited number of data points or lacking experimental support. To demonstrate the applicability of the alchemical free energy method for protein-peptide systems in a typical real-world drug discovery project, we report an application of the thermodynamic integration (TI) method to the RBFE calculation of ghrelin receptor and its peptide agonists. Along with the calculation, the synthesis and in vitro EC50 activity of relamorelin and 17 new peptide derivatives were also reported. A cost-effective criterion to determine the data collection time was proposed for peptides in the TI simulation. The average of three TI repeats yielded a mean absolute error of 0.98 kcal/mol and Pearson's correlation coefficient (R) of 0.77 against the experimental free energy derived from the in vitro EC50 activity, showing good repeatability of the proposed method and a slightly better agreement than the results obtained from the arbitrary time frames up to 20 ns. Although it is limited by having one target and a deduced binding pose, we hope that this study can add some insights into alchemical free energy calculation of protein-peptide systems, providing theoretical assistance to the development of peptide drugs.

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

A Tight Contact: The Expanding Application of Salicylaldehydes in Lysine-Targeting Covalent Drugs

The installation of aldehydes into synthetic protein ligands is an efficient strategy to engage protein lysine residues in remarkably stable imine bonds and augment the compound affinity and selectivity for their biological targets. The high frequency of lysine residues in proteins and the reversibility of the covalent ligand-protein bond support the application of aldehyde-bearing ligands, holding promises for their future use as drugs. This review highlights the increasing exploitation of salicylaldehyde modules in various classes of protein binders, aimed at the reversible-covalent engagement of lysine residues.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

Nitriles: an attractive approach to the development of covalent inhibitors

Nitriles have broad applications in medicinal chemistry, with more than 60 small molecule drugs on the market containing the cyano functional group. In addition to the well-known noncovalent interactions that nitriles can perform with macromolecular targets, they are also known to improve drug candidates' pharmacokinetic profiles. Moreover, the cyano group can be used as an electrophilic warhead to covalently bind an inhibitor to a target of interest, forming a covalent adduct, a strategy that can present benefits over noncovalent inhibitors. This approach has gained much notoriety in recent years, mainly with diabetes and COVID-19-approved drugs. Nevertheless, the application of nitriles in covalent ligands is not restricted to it being the reactive center, as it can also be employed to convert irreversible inhibitors into reversible ones, a promising strategy for kinase inhibition and protein degradation. In this review, we introduce and discuss the roles of the cyano group in covalent inhibitors, how to tune its reactivity and the possibility of achieving selectivity only by replacing the warhead. Finally, we provide an overview of nitrile-based covalent compounds in approved drugs and inhibitors recently described in the literature.

Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces

We have used molecular dynamics (MD) simulations with hybrid quantum mechanics/molecular mechanics (QM/MM) potentials to investigate the reaction mechanism for covalent inhibition of cathepsin K and assess the reversibility of inhibition. The computed free energy profiles suggest that a nucleophilic attack by the catalytic cysteine on the inhibitor warhead and proton transfer from the catalytic histidine occur in a concerted manner. The results indicate that the reaction is more strongly exergonic for the alkyne-based inhibitors, which bind irreversibly to cathepsin K, than for the nitrile-based inhibitor odanacatib, which binds reversibly. Gas-phase energies were also calculated for the addition of methanethiol to structural prototypes for a number of warheads of interest in cysteine protease inhibitor design in order to assess electrophilicity. The approaches presented in this study are particularly applicable to assessment of novel warheads, and computed transition state geometries can be incorporated into molecular models for covalent docking.

Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations

The spread of severe acute respiratory syndrome coronavirus 2 novel coronavirus (SARS-CoV-2) worldwide has caused the coronavirus disease 2019 (COVID-19) pandemic. A hundred million people were infected, resulting in several millions of death worldwide. In order to prevent viral replication, scientists have been aiming to prevent the biological activity of the SARS-CoV-2 main protease (3CL pro or Mpro). In this work, we demonstrate that using a reasonable combination of deep-learning calculations and atomistic simulations could lead to a new approach for developing SARS-CoV-2 main protease (Mpro) inhibitors. Initially, the binding affinities of the natural compounds to SARS-CoV-2 Mpro were estimated via atomistic simulations. The compound tomatine, thevetine, and tribuloside could bind to SARS-CoV-2 Mpro with nanomolar/high-nanomolar affinities. Secondly, the deep-learning (DL) calculations were performed to chemically alter the top-lead natural compounds to improve ligand-binding affinity. The obtained results were then validated by free energy calculations using atomistic simulations. The outcome of the research will probably boost COVID-19 therapy.

Mechanism-Based and Computational-Driven Covalent Drug Design

Covalent drugs offer higher efficacy and longer duration of action than their noncovalent counterparts. Significant advances in computational methods for modeling covalent drugs are poised to shift the paradigm of small molecule therapeutics within the next decade. This viewpoint discusses the advantages of a two-state model for ranking reversible and irreversible covalent ligands and of more complex models for dissecting reaction mechanisms. The relation between these models highlights the complexity and diversity of covalent drug binding and provides opportunities for mechanism-based rational design.