Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide

Identification of a Selective Inhibitor of Human NFS1, a Cysteine Desulfurase Involved in Fe-S Cluster Assembly, via Structure-Based Virtual Screening

Human cysteine desulfurase (NFS1) participates in numerous critical cellular processes, including iron-sulfur (Fe-S) cluster biosynthesis and tRNA thiolation. NFS1 overexpression has been observed in a variety of cancers, and thus it has been considered a promising anti-tumor therapeutic target. To date, however, no inhibitors targeting NFS1 have been identified. Here, we report the identification of the first potent small-molecule inhibitor (Compound 53, PubChem CID 136847320) of NFS1 through a combination of virtual screening and biological validation. Compound 53 exhibited good selectivity against two other pyridoxal phosphate (PLP)-dependent enzymes. Treatment with Compound 53 inhibited the proliferation of lung cancer (A549) cells (IC50 = 16.3 ± 1.92 μM) and caused an increase in cellular iron levels due to the disruption of Fe-S cluster biogenesis. Furthermore, Compound 53, in combination with 2-AAPA, an inhibitor of glutathione reductase (GR) that elevates cellular reactive oxygen species (ROS) levels, further suppressed the proliferation of A549 cells by triggering ferroptotic cell death. Additionally, the key residues involved in the binding of the inhibitor to the active center of NFS1 were identified through a combination of molecular docking and site-directed mutagenesis. Taken together, we describe the identification of the first selective small-molecule inhibitor of human NFS1.

Mechanisms for translating chiral enantiomers separation research into macroscopic visualization

Chirality is a common phenomenon in nature, including the dominance preference of small biomolecules, the special spatial conformation of biomolecules, and the biological and physiological processes triggered by chirality. The selective chiral recognition of molecules in nature from up-bottom or bottom-up is of great significance for living organisms. Such as the transcription of DNA, the recognition of membrane proteins, and the catalysis of enzymes all involve chiral recognition processes. The selective recognition between these macromolecules is mainly achieved through non covalent interactions such as hydrophobic interactions, ammonia bonding, electrostatic interactions, metal coordination, van der Waals forces, and π-π stacking. Researchers have been committed to studying how to convert this weak non covalent interaction into macroscopic visualization, which has further understood of the interactions between chiral molecules and is of great significance for simulating the interactions between molecules in living organisms. This article reviews several models of chiral recognition mechanisms, the interaction forces involved in the chiral recognition process, and the research progress of chiral recognition mechanisms. The outlook in this review points out that studying chiral recognition interactions provides an important bridge between chiral materials and the life sciences, providing an ideal platform for studying chiral phenomena in biological systems.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Interaction of Glutathione with MMACHC Arginine-Rich Pocket Variants Associated with Cobalamin C Disease: Insights from Molecular Modeling

Methylmalonic aciduria and homocystinuria type C protein (MMACHC) is required by the body to metabolize cobalamin (Cbl). Due to its complex structure and cofactor forms, Cbl passes through an extensive series of absorptive and processing steps before being delivered to mitochondrial methyl malonyl-CoA mutase and cytosolic methionine synthase. Depending on the cofactor attached, MMACHC performs either flavin-dependent reductive decyanation or glutathione (GSH)-dependent dealkylation. The alkyl groups of Cbl have to be removed in the presence of GSH to produce intermediates that can later be converted into active cofactor forms. Pathogenic mutations in the GSH binding site, such as R161Q, R161G, R206P, R206W, and R206Q, have been reported to cause Cbl diseases. The impact of these variations on MMACHC's structure and how it affects GSH and Cbl binding at the molecular level is poorly understood. To better understand the molecular basis of this interaction, mutant structures involving the MMACHC-MeCbl-GSH complex were generated using in silico site-directed point mutations and explored using molecular dynamics (MD) simulations. The results revealed that mutations in the key arginine residues disrupt GSH binding by breaking the interactions and reducing the free energy of binding of GSH. Specifically, variations at position 206 appeared to produce weaker GSH binding. The lowered binding affinity for GSH in the variant structures could impact metabolic pathways involving Cbl and its trafficking.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.

Simulation Reveals the Chameleonic Behavior of Macrocycles

Conformational analysis is central to the design of bioactive molecules. It is particularly challenging for macrocycles due to noncovalent transannular interactions, steric interactions, and ring strain that are often coupled. Herein, we simulated the conformations of five macrocycles designed to express a progression of increasing complexity in environment-dependent intramolecular interactions and verified the results against NMR measurements in chloroform and dimethyl sulfoxide. Molecular dynamics using an explicit solvent model, but not the Monte Carlo method with implicit solvation, handled both solvents correctly. Refinement of conformations at the ab initio level was fundamental to reproducing the experimental observations─standard state-of-the-art molecular mechanics force fields were insufficient. Our simulations correctly predicted the intramolecular interactions between side chains and the macrocycle and revealed an unprecedented solvent-induced conformational switch of the macrocyclic ring. Our results provide a platform for the rational, prospective design of molecular chameleons that adapt to the properties of the environment.

An Effective Swarm Intelligence Optimization Algorithm for Flexible Ligand Docking

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors

Antimalarial drug resistance has thrown a spanner in the works of malaria elimination. New drugs are required for ancillary support of existing malaria control efforts. Plasmodium falciparum requires host glucose for survival and proliferation. On this basis, P. falciparum hexose transporter 1 (PfHT1) protein involved in hexose permeation is considered a potential drug target. In this study, we tested the antimalarial activity of some compounds against PfHT1 using computational techniques. We performed high throughput virtual screening of 21,352 small-molecule compounds against PfHT1. The stability of the lead compound complexes was evaluated via molecular dynamics (MD) simulation for 100 nanoseconds. We also investigated the pharmacodynamic, pharmacokinetic and physiological characteristics of the compounds in accordance with Lipinksi rules for drug-likeness to bind and inhibit PfHT1. Molecular docking and free binding energy analyses were carried out using Molecular Mechanics with Generalized Born and Surface Area (MMGBSA) solvation to determine the selectivity of the hit compounds for PfHT1 over the human glucose transporter (hGLUT1) orthologue. Five important PfHT1 inhibitors were identified: Hyperoside (CID5281643); avicularin (CID5490064); sylibin (CID5213); harpagoside (CID5481542) and quercetagetin (CID5281680). The compounds formed intermolecular interaction with the binding pocket of the PfHT1 target via conserved amino acid residues (Val314, Gly183, Thr49, Asn52, Gly183, Ser315, Ser317, and Asn48). The MMGBSA analysis of the complexes yielded high free binding energies. Four (CID5281643, CID5490064, CID5213, and CID5481542) of the identified compounds were found to be stable within the PfHT1 binding pocket throughout the 100 nanoseconds simulation run time. The four compounds demonstrated higher affinity for PfHT1 than the human major glucose transporter (hGLUT1). This investigation demonstrates the inhibition potential of sylibin, hyperoside, harpagoside, and avicularin against PfHT1 receptor. Robust preclinical investigations are required to validate the chemotherapeutic properties of the identified compounds.

Discovery and characterization of dual inhibitors of human Vanin-1 and Vanin-2 enzymes through molecular docking and dynamic simulation-based approach

The vanins are ectoenzymes with pantetheinase activity and are involved in recycling pantothenic acid (vitamin B5) from pantetheine. Elevated levels of vanin have been linked with the development and severity of several diseases, including steatosis, diabetes, skin diseases, cancer, inflammatory diseases etc. Therefore, vanins have previously been used as a potential drug target to combat related diseases. In this study, we used a molecular docking and molecular dynamic simulation-based approach to screen dual inhibitors of hVnn1, and hVnn2 from a library of 120 chemical candidates. Molecular docking of drug candidates with hVnn1, and hVnn2 using GOLD and MOE revealed that the chemical compound "methotrexate (CID: 126941)" has the highest binding affinity against both the target enzymes which was further validated through molecular dynamic simulation. Toxicity profiling of drug candidates evaluated using Lipinski's rule of five and Molsoft tool, and AdmetSar 2.0 confirms the drug suitability of methotrexate, therefore, suggesting its use as a potential therapeutic agent to inhibit the activity of vainin enzyme in related disease conditions.

Evaluating the Conformations and Dynamics of Peptoid Macrocycles

Peptoid macrocycles are versatile and chemically diverse peptidomimetic oligomers. However, the conformations and dynamics of these macrocycles have not been evaluated comprehensively and require extensive further investigation. Recent studies indicate that two degrees of freedom, and four distinct conformations, adequately describe the behavior of each monomer backbone unit in most peptoid oligomers. On the basis of this insight, we conducted molecular dynamics simulations of model macrocycles using an exhaustive set of idealized possible starting conformations. Simulations of various sizes of peptoid macrocycles yielded a limited set of populated conformations. In addition to reproducing all relevant experimentally determined conformations, the simulations accurately predicted a cyclo-octamer conformation for which we now present the first experimental observation. Sets of three adjacent dihedral angles (ϕ_i, ψ_i, ω_i+1) exhibited correlated crankshaft motions over the course of simulation for peptoid macrocycles of six residues and larger. These correlated motions may occur in the form of an inversion of one amide bond and the concerted rotation of the preceding ϕ and ψ angles to their mirror-image conformation, a variation on "crankshaft flip" motions studied in polymers and peptides. The energy landscape of these peptoid macrocycles can be described as a network of conformations interconnected by transformations of individual crankshaft flips. For macrocycles of up to eight residues, our mapping of the landscape is essentially complete.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

Molecular Structure-Based Screening of the Constituents of Calotropis procera Identifies Potential Inhibitors of Diabetes Mellitus Target Alpha Glucosidase

Diabetes mellitus is a disorder characterized by higher levels of blood glucose due to impaired insulin mechanisms. Alpha glucosidase is a critical drug target implicated in the mechanisms of diabetes mellitus and its inhibition controls hyperglycemia. Since the existing standard synthetic drugs have therapeutic limitations, it is imperative to identify new potent inhibitors of natural product origin which may slow carbohydrate digestion and absorption via alpha glucosidase. Since plant extracts from Calotropis procera have been extensively used in the treatment of diabetes mellitus, the present study used molecular docking and dynamics simulation techniques to screen its constituents against the receptor alpha glucosidase. Taraxasterol, syriogenin, isorhamnetin-3-O-robinobioside and calotoxin were identified as potential novel lead compounds with plausible binding energies of −40.2, −35.1, −34.3 and −34.3 kJ/mol against alpha glucosidase, respectively. The residues Trp⁴⁸¹, Asp⁵¹⁸, Leu⁶⁷⁷, Leu⁶⁷⁸ and Leu⁶⁸⁰ were identified as critical for binding and the compounds were predicted as alpha glucosidase inhibitors. Structurally similar compounds with Tanimoto coefficients greater than 0.7 were reported experimentally to be inhibitors of alpha glucosidase or antidiabetic. The structures of the molecules may serve as templates for the design of novel inhibitors and warrant in vitro assaying to corroborate their antidiabetic potential.

Recurrent neural network (RNN) model accelerates the development of antibacterial metronidazole derivatives

Metronidazole is a specific drug against trichomonas and anaerobic bacteria, and is widely used in the clinic. However, extensive clinical application is often accompanied by extensive side effects, so it is still of great significance to develop metronidazole derivatives with a new skeleton. Compared with other traditional receptor-based drug design methods, the computational model based on a neural network has higher accuracy and reliability. In this work, a Recurrent Neural Network (RNN) model is applied to the discovery of metronidazole drugs with a new skeleton. Firstly, the generation model based on a Gated Recurrent Unit (GRU) is trained to generate an effective Simplified Molecular-Input Line-Entry System (SMILES) string library with high precision. Then, transfer learning is introduced to fine-tune the GRU model, and many molecules with structures similar to known active drugs are generated. After cluster analysis of the structures of the new compounds, 20 small molecular compounds with metronidazole structures of all different categories were selected, of which 19 may not belong to any published patents or applications. Through prediction and personal experience, the difficulty of synthesizing these 20 new structures was analyzed, and compound 0001 was chosen as our synthetic target, and a series of structures (8a–l) similar to compound 0001 were synthesized. Finally, the inhibitory activities of these compounds against bacteria E. coli, P. aeruginosa, B. subtilis and S. aureus were determined. The results showed that compound 8a–l had obvious inhibitory activity against these four bacteria, which proved the accuracy of our compound generation model.

Generating antibacterial metronidazole derivatives using a recurrent neural network model.

Discovery of TGFBR1 (ALK5) as a potential drug target of quercetin glycoside derivatives (QGDs) by reverse molecular docking and molecular dynamics simulation

Quercetin glycoside derivatives (QGDs) are a class of common compounds with a wide range of biological activities, such as antitumor activities. However, their molecular targets associated with biological activities have not been investigated. In this study, four common QGDs with mutual bioconversion were selected, and studied in the large-scale reverse docking experiments. Network pharmacology analysis showed that most of the four QGDs can bind several potential protein targets that were closely related to breast cancer disease. Among them, a druggable protein, transforming growth factor beta receptor I (TGFBR1/ALK5) was screened via high docking scores for the four QGDs. This protein has been proven to be an important target for the treatment of breast cancer by regulating the proliferation and migration of cancer cells in the past. Subsequently, the molecular dynamics (MD) simulation and MM/GBSA calculation demonstrated that all QGDs could thermodynamically bind with TGFBR1, indicating that TGFBR1 might be one of the potential protein targets of QGDs. Finally, the cytotoxicity test and wound-healing migration assay displayed that isoquercetin, which can perform best in MD experiment, might be a promising agent in the treatment of breast cancer metastasis.

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein-ligand complexes containing "non-macrocyclic" ("linear") ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

Milk Peptides as Novel Multi-Targeted Therapeutic Candidates for SARS-CoV2

The COVID-19, an acute respiratory syndrome caused by SARS-CoV2 is a major catastrophic event of the twenty first century. Relentless efforts for the development of effective pharmaco-therapeutics are in progress but the respite is the development of effective vaccines. However, monotherapy might not always exhibit complete efficacy and may culminate in the rapid evolution of drug-resistant viral strains. Hence, simultaneous modulation of multiple druggable targets not only enhances therapeutic efficacy but also quell the prospects of mutant viruses. Currently, milk peptides have bloomed beyond just being a quintessential part of nutrition to prominent therapeutic implications in human health and diseases. Hence, we have focused on colostrum/milk peptides as they have already been acknowledged for their high potency, target specificity with significantly low or no side effects and bio-toleration. The results presented provide a conceptual strategy for the rational designing of prospective multitargeted peptide inhibitors for SARS-CoV2.

First COVID-19 molecular docking with a chalcone-based compound: synthesis, single-crystal structure and Hirshfeld surface analysis study

The first example of molecular docking of the SARS-CoV-2 main protease for COVID-19 [M^pro, Protein Data Bank (PDB) code 7BQY] by a chalcone-based ligand, namely, (E)-1-(2,4-dichlorophenyl)-3-[4-(morpholin-4-yl)phenyl]prop-2-en-1-one, C₁₉H₁₇Cl₂NO₂, I, is presented. Two-dimensional (2D) LIGPLOT representations calculated for the inhibitor N3, viz. N-{[(5-methylisoxazol-3-yl)carbonyl]alanyl}-L-valyl-N₁-((1R,2Z)-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl]methyl}but-2-enyl)-L-leucinamide, and 7BQY are included for comparison with our chalcone-based complexes. The binding affinity of our chalcone ligand with 7BQY is -7.0 kcal mol^-1, a high value which was attributed to the presence of a hydrogen bond, together with many hydrophobic interactions between the drug and the active amino acid residues of the receptor. Docking studies were also performed, employing rigid and flexible binding modes for the ligand. The superposition of N3 and the chalcone docked into the binding pocket of 7BQY is also presented. The synthesis, single-crystal structure, Hirshfeld surface analysis (HSA) and spectral characterization of heterocyclic chalcone-based compound I, are also presented. The molecules are stacked, with normal π-π interactions, in the crystal.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.

Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5′-ends of viral genomic RNA and sub genomic RNAs, to escape the host’s innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5’,5’-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5’-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.

Communicated by Ramaswamy H. Sarma.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

In silico and In vivo Evaluation of Oxidative Stress Inhibitors Against Parkinson's Disease using the C. elegans Model

BACKGROUND

Parkinson's disease ranks second, after Alzheimer's as the major neurodegenerative disorder, for which no cure or disease-modifying therapies exist. Ample evidence indicate that PD manifests as a result of impaired anti-oxidative machinery leading to neuronal death wherein Cullin-3 has ascended as a potential therapeutic target for diseases involving damaged anti-oxidative machinery.

OBJECTIVE

The design of target specific inhibitors for the Cullin-3 protein might be a promising strategy to increase the Nrf2 levels and to decrease the possibility of "off-target" toxic properties.

METHODS

In the present study, an integrated computational and wet lab approach was adopted to identify small molecule inhibitors for Cullin-3. The rational drug designing process comprised homology modeling and derivation of the pharmacophore for Cullin-3, virtual screening of Zinc natural compound database, molecular docking and Molecular dynamics based screening of ligand molecules. In vivo validations of an identified lead compound were conducted in the PD model of C. elegans.

RESULTS AND DISCUSSION

Our strategy yielded a potential inhibitor; (Glide score = -12.31), which was evaluated for its neuroprotective efficacy in the PD model of C. elegans. The inhibitor was able to efficiently defend against neuronal death in PD model of C. elegans and the neuroprotective effects were attributed to its anti-oxidant activities, supported by the increase in superoxide dismutase, catalase and the diminution of acetylcholinesterase and reactive oxygen species levels. In addition, the Cullin-3 inhibitor significantly restored the behavioral deficits in the transgenic C. elegans.

CONCLUSION

Taken together, these findings highlight the potential utility of Cullin-3 inhibition to block the persistent neuronal death in PD. Further studies focusing on Cullin-3 and its mechanism of action would be interesting.

Conformational analysis of macrocycles: comparing general and specialized methods

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray_ppw), and (v) to the X-ray_ppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray_ppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray_ppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray_ppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.

The Spectrum of Thiazolidinediones against Respiratory Tract Pathogenic Bacteria: An In Vitro and In Silico Approach

BACKGROUND AND OBJECTIVES

Drug design strategies to develop novel broad-spectrum antibacterial agents for the treatment of respiratory tract infections that can combat bacterial resistance are currently gaining momentum. 2,4-thiazolidinedione is a structural scaffold that contains pharmacophores similar to β-lactam and non- β-lactam antibiotics. The objective of the study was to synthesize newer 3,5-Disubstituted-2,4-Thiazolidinediones (DTZDs) and subject them to in vitro antibacterial screening against bacterial pathogens. Also, we performed in silico docking of selected compounds to penicillin-binding proteins and beta-lactamases.

METHODS

Intermediate Schiff bases were prepared by the reaction between 2,4-thiazolidinedione and an appropriate aldehyde followed by acylation of the ring nitrogen with 3-brompropanoyl chloride resulting in DTZDs. Minimum inhibitory concentrations were determined against few bacteria infecting the respiratory tract by the broth tube dilution method. Zones of inhibitions against the bacteria were also determined using agar well diffusion technique. Molecular docking of the compounds to all types of Penicillin-Binding Proteins (PBPs) and β-lactamases was also carried out.

RESULTS

Compounds DTZD12 and DTZD16 exhibited broad-spectrum antibacterial activity. The minimum inhibitory concentrations of the compounds were 175μg/100μL. Measurements of the zones of inhibitions indicated that compound DTZD12 was more active than DZTD16. E. coli was the most susceptible organism. Docking results established that both the compounds were able to interact with PBPs and β-lactamases through strong hydrogen bonds, especially the unique interaction with active serine residue of the PBP for inhibition of cell wall synthesis.

CONCLUSION

DTZD12 and DTZD16 can be developed into antibacterial drugs for respiratory tract infections to oppose bacterial resistance, or can also be used as leads for repurposing the existing 2,4- thiazolidinediones.

Conformation of the Macrocyclic Drug Lorlatinib in Polar and Nonpolar Environments: A MD Simulation and NMR Study

The replica exchange molecular dynamics (REMD) simulation is demonstrated to readily predict the conformations of the macrocyclic drug lorlatinib, as validated by solution NMR studies. In aqueous solution, lorlatinib adopts a conformer identical to its target bound structure. This conformer is stabilized by an extensive hydrogen bond network to the solvents. In chloroform, lorlatinib populates two conformers with the second one being less polar, which may contribute to lorlatinib's ability to cross cell membranes.

Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Modelling the binding mode of macrocycles: Docking and conformational sampling

Drug discovery is increasingly tackling challenging protein binding sites regarding molecular recognition and druggability, including shallow and solvent-exposed protein-protein interaction interfaces. Macrocycles are emerging as promising chemotypes to modulate such sites. Despite their chemical complexity, macrocycles comprise important drugs and offer advantages compared to non-cyclic analogs, hence the recent impetus in the medicinal chemistry of macrocycles. Elaboration of macrocycles, or constituent fragments, can strongly benefit from knowledge of their binding mode to a target. When such information from X-ray crystallography is elusive, computational docking can provide working models. However, few studies have explored docking protocols for macrocycles, since conventional docking methods struggle with the conformational complexity of macrocycles, and also potentially with the shallower topology of their binding sites. Indeed, macrocycle binding mode prediction with the mainstream docking software GOLD has hardly been explored. Here, we present an in-depth study of macrocycle docking with GOLD and the ChemPLP scores. First, we summarize the thorough curation of a test set of 41 protein-macrocycle X-ray structures, raising the issue of lattice contacts with such systems. Rigid docking of the known bioactive conformers was successful (three top ranked poses) for 92.7% of the systems, in absence of crystallographic waters. Thus, without conformational search issues, scoring performed well. However, docking success dropped to 29.3% with the GOLD built-in conformational search. Yet, the success rate doubled to 58.5% when GOLD was supplied with extensive conformer ensembles docked rigidly. The reasons for failure, sampling or scoring, were analyzed, exemplified with particular cases. Overall, binding mode prediction of macrocycles remains challenging, but can be much improved with tailored protocols. The analysis of the interplay between conformational sampling and docking will be relevant to the prospective modelling of macrocycles in general.

Docking Flexible Cyclic Peptides with AutoDock CrankPep

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Predicting the bioactive conformations of macrocycles: a molecular dynamics-based docking procedure with DynaDock

Macrocyclic compounds are of growing interest as a new class of therapeutics, especially as inhibitors binding to protein-protein interfaces. As molecular modeling is a well-established complimentary tool in modern drug design, the number of attempts to develop reliable docking strategies and algorithms to accurately predict the binding mode of macrocycles is rising continuously. Standard molecular docking approaches need to be adapted to this application, as a comprehensive yet efficient sampling of all ring conformations of the macrocycle is necessary. To overcome this issue, we designed a molecular dynamics-based docking protocol for macrocycles, in which the challenging sampling step is addressed by conventional molecular dynamics (750 ns) simulations performed at moderately high temperature (370 K). Consecutive flexible docking with the DynaDock approach based on multiple, pre-sampled ring conformations yields highly accurate poses with ligand RMSD values lower than 1.8 Å. We further investigated the value of molecular dynamics-based complex stability estimations for pose selection and discuss its applicability in combination with standard binding free energy estimations for assessing the quality of poses in future blind docking studies.

Conformational Sampling of Macrocyclic Drugs in Different Environments: Can We Find the Relevant Conformations?

Conformational flexibility is a major determinant of the properties of macrocycles and other drugs in beyond rule of 5 (bRo5) space. Prediction of conformations is essential for design of drugs in this space, and we have evaluated three tools for conformational sampling of a set of 10 bRo5 drugs and clinical candidates in polar and apolar environments. The distance-geometry based OMEGA was found to yield ensembles spanning larger structure and property spaces than the ensembles obtained by MOE-LowModeMD (MOE) and MacroModel (MC). Both MC and OMEGA but not MOE generated different ensembles for polar and apolar environments. All three conformational search methods generated conformers similar to the crystal structure conformers for 9 of the 10 compounds, with OMEGA performing somewhat better than MOE and MC. MOE and OMEGA found all six conformers of roxithromycin that were identified by NMR in aqueous solutions, whereas only OMEGA sampled the three conformers observed in chloroform. We suggest that characterization of conformers using molecular descriptors, e.g., the radius of gyration and polar surface area, is preferred to energy- or root-mean-square deviation-based methods for selection of biologically relevant conformers in drug discovery in bRo5 space.

Simulation-Guided Design of Cytochrome P450 for Chemo- and Regioselective Macrocyclic Oxidation

Engineering high chemo-, regio-, and stereoselectivity is a prerequisite for enzyme usage in organic synthesis. Cytochromes P450 can oxidize a broad range of substrates, including macrocycles, which are becoming popular scaffolds for therapeutic agents. However, a large conformational space explored by macrocycles not only reduces the selectivity of oxidation but also impairs computational enzyme design strategies based on docking and molecular dynamics (MD) simulations. We present a novel design workflow that uses enhanced-sampling Hamiltonian replica exchange (HREX) MD and focuses on quantifying the substrate binding for suggesting the mutations to be made. This computational approach is applied to P450 BM3 with the aim to shift regioselectively toward one of the numerous possible positions during β-cembrenediol oxidation. The predictions are experimentally tested and the resulting product distributions validate our design strategy, as single mutations led up to 5-fold regioselectivity increases. We thus conclude that the HREX-MD-based workflow is a promising tool for the identification of positions for mutagenesis aiming at P450 enzymes with improved regioselectivity.

Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein-Protein Interactions

Macrocyclic peptides can interfere with challenging biomolecular targets including protein–protein interactions. Whereas there are various approaches that facilitate the identification of peptide-derived ligands, their evolution into higher affinity binders remains a major hurdle. We report a virtual screen based on molecular docking that allows the affinity maturation of macrocyclic peptides taking non-natural amino acids into consideration. These macrocycles bear large and flexible substituents that usually complicate the use of docking approaches. A virtual library containing more than 1400 structures was screened against the target focusing on docking poses with the core structure resembling a known bioactive conformation. Based on this screen, a macrocyclic peptide 22 involving two non-natural amino acids was evolved showing increased target affinity and biological activity. Predicted binding modes were verified by X-ray crystallography. The presented workflow allows the screening of large macrocyclic peptides with diverse modifications thereby expanding the accessible chemical space and reducing synthetic efforts.

Novel Peripherally Restricted Cannabinoid 1 Receptor Selective Antagonist TXX-522 with Prominent Weight-Loss Efficacy in Diet Induced Obese Mice

The clinical development of the first generation of globally active cannabinoid 1 receptor (CB1R) antagonists was suspended because of their adverse neuropsychiatric effects. Selective blockade of peripheral CB1Rs has the potential to provide a viable strategy for the treatment of severe obesity while avoiding these central nervous system side effects. In the current study, a novel compound (TXX-522) was rationally designed based on the parent nucleus of a classical CB1R-selective antagonist/inverse agonist, rimonabant (SR141716A). Docking assays indicate that TXX-522 was bound with the CB1R in a mode similar to that of SR141716A. TXX-522 showed good binding, CB1R-selectivity (over the CB2R), and functional antagonist activities in a range of in vitro molecular and cellular assays. In vivo analysis of the steady state distribution of TXX-522 in the rat brain and blood tissues and the assay of its functional effects on CB1R activity collectively showed that TXX-522 showed minimal brain penetration. Moreover, the in vivo pharmacodynamic study further revealed that TXX-522 had good oral bioavailability and a potent anti-obesity effect, and ameliorated insulin resistance in high-fat diet-induced obese mice. No impact on food intake was observed in this model, confirming the limited brain penetration of this compound. Thus, the current study indicates that TXX-522 is a novel and potent peripherally acting selective CB1R antagonist with the potential to control obesity and related metabolic disorders.

Chemical optimization of macrocyclic HIV-1 inactivators for improving potency and increasing the structural diversity at the triazole ring

HIV-1 entry inhibition remains an urgent need for AIDS drug discovery and development. We previously reported the discovery of cyclic peptide triazoles (cPTs) that retain the HIV-1 irreversible inactivation functions of the parent linear peptides (PTs) and have massively increased proteolytic resistance. Here, in an initial structure-activity relationship investigation, we evaluated the effects of variations in key structural and functional components of the cPT scaffold in order to produce a platform for developing next-generation cPTs. Some structural elements, including stereochemistry around the cyclization residues and Ile and Trp side chains in the gp120-binding pharmacophore, exhibited relatively low tolerance for change, reflecting the importance of these components for function. In contrast, in the pharmacophore-central triazole position, the ferrocene moiety could be successfully replaced with smaller aromatic rings, where a p-methyl-phenyl methylene moiety gave cPT 24 with an IC50 value of 180 nM. Based on the observed activity of the biphenyl moiety when installed on the triazole ring (cPT 23, IC50 ∼ 269 nM), we further developed a new on-resin synthetic method to easily access the bi-aryl system during cPT synthesis, in good yields. A thiophene-containing cPT AAR029N2 (36) showed enhanced entropically favored binding to Env gp120 and improved antiviral activity (IC50 ∼ 100 nM) compared to the ferrocene-containing analogue. This study thus provides a crucial expansion of chemical space in the pharmacophore to use as a starting point, along with other allowable structural changes, to guide future optimization and minimization for this important class of HIV-1 killing agents.