Systematic Studies on the Protocol and Criteria for Selecting a Covalent Docking Tool

Synthesis, In vitro and In silico Studies of Novel Bis-triazolopyridopyrimidines from Curcumin Analogues as Potential Aromatase Agents

Breast cancer remains a major global health issue, particularly affecting women and contributing significantly to mortality rates. Current treatments for estrogen receptor-positive breast cancers, such as aromatase inhibitors, are effective but often come with side effects and resistance issues. This study addresses these gaps by targeting aromatase, an enzyme crucial for estrogen synthesis, which plays a pivotal role in breast cancer progression. The innovative approach involves synthesizing novel bis-triazolopyridopyrimidines, designed to leverage the combined pharmacological benefits of pyridopyrimidine and 1,2,4-triazole structures, known for their potent aromatase inhibition and anti-cancer properties. These compounds were synthesized and characterized using 1H-NMR, 13C-NMR, and MS spectral analyses, and their anticancer efficacy was evaluated through MTT assays against MCF-7 breast cancer cell lines in vitro. Molecular docking analyses revealed strong binding energies with aromatase, particularly for compounds 5 b, 5 c, 10 a, and 10 b, indicating their potential as effective aromatase inhibitors. The study highlights these compounds as promising candidates for further development as therapeutic agents against breast cancer.

How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms

Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking the potential of more rigorous quantum mechanical/molecular mechanics (QM/MM) models combined with molecular dynamics-based free energy techniques could have a tremendous impact. Indeed, these two relatively old techniques are emerging as promising methods in the field. This has been favored by the exponential growth of computer power and the proliferation of powerful data-driven methods. Here, we briefly review recent advances and applications, and give our perspective on the impact that QM/MM and free-energy methods combined with machine learning-aided algorithms can have on drug design.

Two-Step Covalent Docking with Attracting Cavities

Due to their various advantages, interest in the development of covalent drugs has been renewed in the past few years. It is therefore important to accurately describe and predict their interactions with biological targets by computer-aided drug design tools such as docking algorithms. Here, we report a covalent docking procedure for our in-house docking code Attracting Cavities (AC), which mimics the two-step mechanism of covalent ligand binding. Ligand binding to the protein cavity is driven by nonbonded interactions, followed by the formation of a covalent bond between the ligand and the protein through a chemical reaction. To test the performance of this method, we developed a diverse, high-quality, openly accessible re-docking benchmark set of 95 covalent complexes bound by 8 chemical reactions to 5 different reactive amino acids. Combination with structures from previous studies resulted in a set of 304 complexes, on which AC obtained a success rate (rmsd ≤ 2 Å) of 78%, outperforming two state-of-the-art covalent docking codes, genetic optimization for ligand docking (GOLD (66%)) and AutoDock (AD (35%)). Using a more stringent success criterion (rmsd ≤ 1.5 Å), AC reached a success rate of 71 vs 55% for GOLD and 26% for AD. We additionally assessed the cross-docking performance of AC on a set of 76 covalent complexes of the SARS-CoV-2 main protease. On this challenging test set of mainly small and highly solvent-exposed ligands, AC yielded success rates of 58 and 28% for re-docking and cross-docking, respectively, compared to 45 and 17% for GOLD.

In Silico Analysis of USP7 Inhibitors Based on Building QSAR Models and Fragment Design for Screening Marine Compound Libraries

USP7 is highly expressed in a variety of tumors and is thought to play a major role in cancer development. However, there are no drugs available to target USP7, so there is a need to develop new USP7 inhibitors. In this study, AutoQSAR, multiple linear regression, and Naive Bayesian models were constructed using 543 compounds and used to analyze marine compounds. After selecting 240 small molecules for molecular docking with Maestro, MOE, and GOLD, better small molecules than the positive compound P217564 were screened. The molecular structure of "1, 2-dibromobenzene" was optimized to improve the binding effect of the protein, and 10 optimized compounds in ADMET performed well during the screening process. To study the dynamic combination of protein-ligand effect consistency with static molecular docking, 100ns molecular dynamics simulations of candidate compound 1008-1, reference compound P217564, and negative-positive GNE2917 were conducted. The results of molecular docking and molecular dynamics simulation analysis showed that compound 1008-1 maintained a stable conformation with the target protein. Thus, the comprehensive analysis suggests that compound 1008-1 could provide new possibilities for USP7 covalent inhibitor candidates.

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.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Analysis of the Chemical Composition and Evaluation of the Antioxidant, Antimicrobial, Anticoagulant, and Antidiabetic Properties of Pistacia lentiscus from Boulemane as a Natural Nutraceutical Preservative

Pistacia lentiscus L. has traditionally been employed as a diuretic and stimulant in the treatment of hypertension. Our interest centered on analyzing the chemical profile of the plant's leaves and its in vitro, in vivo, and in silico antioxidant, antimicrobial, anticoagulant, and antidiabetic effects in order to valorize this species and prepare new high-value products that can be used in the agro-food and pharmaceutical industries. When this species' essential oil was hydrodistilled and subjected to GC-MS analysis, the results showed that the principal components were germacrene D (17.54%), spathulenol (17.38%), bicyclogermacrene (12.52%), and terpinen-4-ol (9.95%). The extraction of phenolic compounds was carried out by decoction and Soxhlet. The determination of total polyphenols, flavonoids, and tannins of aqueous and organic extracts by spectrophotometric methods demonstrated the richness of this species in phenolic compounds. Chromatographic analysis by HPLC/UV-ESI-MS of the aqueous extract of P. lentiscus revealed the presence of 3,5-di-O-galloyl quinic acid, gallic acid, and 3,4,5-tri-O-galloyl quinic acid specific to this species. The study of antioxidant activity by three methods (DPPH, FRAP, and Total Antioxidant Capacity) revealed that P. lentiscus is a very promising source of natural antioxidants. The antimicrobial activity of the essential oil and aqueous extract (E0) was studied by microdilution on the microplate. The results revealed the effectiveness of the aqueous extract compared to the essential oil against Gram-negative bacteria (K. pneumoniae, A. baumannii, E. aerogenes, E. cloacae, P. fluorescence, Salmonella sp., Shigella sp., and Y. enterolitica) and candidoses (C. krusei and C. albicans). The measurements of prothrombin time (PT) and activated partial thromboplastin time (aPTT) of the aqueous extract (E0) can significantly prolong these tests from concentrations of 2.875 and 5.750 mg/mL, respectively. The antihyperglycemic effect of the aqueous extract (E0) showed a strong in vitro inhibitory activity of α-amylase and α-glucosidase compared to acarbose. Thus, it significantly inhibited postprandial hyperglycemia in Wistar albino rats. The in-silico study of the major compounds of the essential oil and extract (E0) carried out using PASS, SwissADME, pkCSM, and molecular docking tools confirmed our in vitro and in vivo results. The studied compounds showed a strong ability to be absorbed by the gastrointestinal tract and to passively diffuse through the blood-brain barrier, a similarity to drugs, and water solubility. Molecular docking experiments deduced the probable mode of action of the identified compounds on their respective target proteins, such as NADPH oxidase, thrombin, α-amylase, and α-glucosidase. Furthermore, given the demonstrated antioxidant, antimicrobial, anticoagulant, and antidiabetic effects, we can affirm the richness of P. lentiscus in bioactive molecules and its use in traditional medicine as a source of preservative agent.

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.

In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity

Pseudomonas stutzeri phosphite dehydrogenase (PTDH) catalyzes the oxidation of phosphite to phosphate in the presence of NAD, resulting in the formation of NADH. The regeneration of NADH by PTDH is greater than any other enzyme due to the substantial change in the free energy of reaction (G°' = - 63.3 kJ/mol). Presently, improving the stability of PTDH is for a great importance to ensure an economically viable reaction process to produce phosphite as a byproduct for agronomic applications. The binding site of NAD⁺ with PTDH includes thirty-four residues; eight of which have been previously mutated and characterized for their roles in catalysis. In the present study, the unexplored twenty-six key residues involved in the binding of NAD⁺ were subjected to in silico mutagenesis based on the physicochemical properties of the amino acids. The effects of these mutations on the structure, stability, activity, and interaction of PTDH with NAD⁺ were investigated using molecular docking, molecular dynamics simulations, free energy calculations, and secondary structure analysis. We identified seven novel mutations, A155I, G157I, L217I, P235A, V262I, I293A, and I293L, that reduce the compactness of the protein while improving PTDH stability and binding to NAD⁺.

HCovDock: an efficient docking method for modeling covalent protein-ligand interactions

Covalent inhibitors have received extensive attentions in the past few decades because of their long residence time, high binding efficiency and strong selectivity. Therefore, it is valuable to develop computational tools like molecular docking for modeling of covalent protein-ligand interactions or screening of potential covalent drugs. Meeting the needs, we have proposed HCovDock, an efficient docking algorithm for covalent protein-ligand interactions by integrating a ligand sampling method of incremental construction and a scoring function with covalent bond-based energy. Tested on a benchmark containing 207 diverse protein-ligand complexes, HCovDock exhibits a significantly better performance than seven other state-of-the-art covalent docking programs (AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro and MOE). With the criterion of ligand root-mean-squared distance < 2.0 Å, HCovDock obtains a high success rate of 70.5% and 93.2% in reproducing experimentally observed structures for top 1 and top 10 predictions. In addition, HCovDock is also validated in virtual screening against 10 receptors of three proteins. HCovDock is computationally efficient and the average running time for docking a ligand is only 5 min with as fast as 1 sec for ligands with one rotatable bond and about 18 min for ligands with 23 rotational bonds. HCovDock can be freely assessed at http://huanglab.phys.hust.edu.cn/hcovdock/.

Advanced approaches of developing targeted covalent drugs

In recent years, the development of targeted covalent inhibitors has gained popularity around the world. Specific groups (electrophilic warheads) form irreversible bonds with the side chain of nucleophilic amino acid residues, thus changing the function of biological targets such as proteins. Since the first targeted covalent inhibitor was disclosed in the 1990s, great efforts have been made to develop covalent ligands from known reversible leads or drugs by addition of tolerated electrophilic warheads. However, high reactivity and "off-target" toxicity remain challenging issues. This review covers the concept of targeted covalent inhibition to diseases, discusses traditional and interdisciplinary strategies of cysteine-focused covalent drug discovery, and exhibits newly disclosed electrophilic warheads majorly targeting the cysteine residue. Successful applications to address the challenges of designing effective covalent drugs are also introduced.

Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery

Fragment based drug discovery has long been used for the identification of new ligands and interest in targeted covalent inhibitors has continued to grow in recent years, with high profile drugs such as osimertinib and sotorasib gaining FDA approval. It is therefore unsurprising that covalent fragment-based approaches have become popular and have recently led to the identification of novel targets and binding sites, as well as ligands for targets previously thought to be 'undruggable'. Understanding the properties of such covalent fragments is important, and characterizing and/or predicting reactivity can be highly useful. This review aims to discuss the requirements for an electrophilic fragment library and the importance of differing warhead reactivity. Successful case studies from the world of drug discovery are then be examined.

Cov_DOX: A Method for Structure Prediction of Covalent Protein-Ligand Bindings

A handful of molecular docking tools have been extended to enable a covalent docking. However, all of them face the challenge brought by the covalent bond between proteins and ligands. Many covalent drug design scenarios still heavily rely on demanding crystallographic experiments for accurate binding structures. Aiming at filling the gap between covalent dockings and crystallographic experiments, we develop and validate a hybrid method, dubbed as Cov_DOX, in this work. Cov_DOX achieves an overall success rate of 81% with RMSD < 2 Å for the Top 1 pose prediction in the validation against a test set including 405 crystal structures for covalent protein-ligand complexes, covering various types of the warhead chemistry and receptors. Such accuracy is not far from the much more demanding crystallographic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40-60%).

Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals’ mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

Calcium Delivery Systems Assembled using Antarctic Krill Derived Heptapeptides: Exploration of the Assembly Mechanism, In Vitro Digestion Profile, and Calcium Absorption Behavior

A novel heptapeptide QEELISK derived from Antarctic krill was used to assemble a calcium delivery system, of which the calcium binding mechanism of QEELISK, in vitro digestion kinetics, and calcium absorption behaviors were explored. QEELISK with continuous Glu possessed higher calcium binding capacity than that of QELEISK and QAALISK. Ca²⁺ bound to the carboxyl oxygen of Glu at position 3 of the QEELISK peptide at a stoichiometric ratio of 1:1 through charge-charge interaction; the formed QEELISK-Ca showed superior stability. Moreover, QEELISK-Ca underwent disaggregation and self-assembly during in vitro digestion reflected by visualization of calcium ions and circular dichroism spectra. QELEISK was partially stable during gastrointestinal digestion, and calcium chelation improved the digestive stability of QELEISK. In addition, a significant enhancement of calcium absorption with QELEISK-Ca occurred in the duodenum and ileum when compared to CaCl₂ absorption, which indicated that QEELISK might carry calcium ions through the gastrointestinal tract.

10 years into the resurgence of covalent drugs

In the first decade of targeted covalent inhibition, scientists have successfully reversed the previous trend that had impeded the use of covalent inhibition in drug development. Successes in the clinic, mainly in the field of kinase inhibitors, are existing proof that safe covalent inhibitors can be designed and employed to develop effective treatments. The case of KRAS_G12C covalent inhibitors entering clinical trials in 2019 has been among the hottest topics discussed in drug discovery, raising expectations for the future of the field. In this perspective, an overview of the milestones hit with targeted covalent inhibitors, as well as the promise and the needs of current research, are presented. While recent results have confirmed the potential that was foreseen, many questions remain unexplored in this branch of precision medicine.

Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors

Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.

Discovery of Targeted Covalent Natural Products against PLK1 by Herb-Based Screening

Natural products (NPs) are a rich source of drug discovery, and some of them act by covalently binding to the targets. Recently, targeted covalent natural product (TCNP) design has gained considerable attention since this approach offers significant benefits in improving biological efficacy and decreasing the off-target side effects. However, most of the known TCNPs were discovered by chance. Rational approaches for a systematic screen of TCNPs are much needed. Here, we developed a combined computational and experimental approach to carry out herb-based screening to identify TCNPs against proper cysteine residues in the target proteins. The herb-based TCNP screening approach (HB-TCNP) starts from a druggable pocket and cysteine residue prediction, followed by virtual screening of a covalent NP database and herb-based mapping to identify candidate herbs for experimental validation. Herbs with time-dependent activity are selected, and their NPs are experimentally tested to further screen covalent NPs. We have successfully applied HB-TCNP to screen anti-PLK1 herbs and NPs with high efficacy. Cys67 and Cys133 in the ATP binding pocket of PLK1 were used in the search. Five herbs were tested and exhibited PLK1 inhibition activity to some extent, among which Scutellaria baicalensis showed the most potent activity with time dependency. Further experimental studies showed that the main active compounds in Scutellaria baicalensis, baicalein and baicalin, covalently bind PLK1 through Cys133. Our study provided an efficient way to rationally design TCNPs and to make better use of herb medicines. The Cys133 residue in PLK1 serves as a novel covalent site for further drug discovery against PLK1.