Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

RNA or DNA? Revisiting the Chemical Nature of the Cenancestral Genome.

One of the central issues in the understanding of early cellular evolution is the characterisation of the cenancestor. This includes the description of the chemical nature of its genome. The disagreements on this question comprise several proposals, including the possibility that AlkB-mediated methylation repair of alkylated RNA molecules may be interpreted as evidence of a cenancestral RNA genome. We present here an evolutionary analysis of the cupin-like protein superfamily based on tertiary structure-based phylogenies that includes the oxygen-dependent AlkB and its homologs. Our results suggest that the repair of methylated RNA molecules is the outcome of the enzyme substrate ambiguity, and doesn´t necessarily indicates that the last common ancestor was endowed with an RNA genome.

FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

Substrate-Based Ligand Design for Phenazine Biosynthesis Enzyme PhzF.

The phenazine pyocyanin is an important virulence factor of the pathogen Pseudomonas aeruginosa, which is on the WHO list of antibiotic resistant "priority pathogens". In this study the isomerase PhzF, a key bacterial enzyme of the pyocyanin biosynthetic pathway, was investigated as a pathoblocker target. The aim of the pathoblocker strategy is to reduce the virulence of the pathogen without killing it, thus preventing the rapid development of resistance. Based on crystal structures of PhzF, derivatives of the inhibitor 3-hydroxyanthranilic acid were designed. Co-crystal structures of the synthesized derivatives with PhzF revealed spacial limitations of the binding pocket of PhzF in the closed conformation. In contrast, ligands aligned to the open conformation of PhzF provided more room for structural modifications. The intrinsic fluorescence of small 3-hydroxyanthranilic acid derivatives enabled direct affinity determinations using FRET assays. The analysis of structure-activity relationships showed that the carboxylic acid moiety is essential for binding to the target enzyme. The results of this study provide fundamental structural insights that will be useful for the design of PhzF-inhibitors.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

BACKGROUND: Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS: This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS: This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS: The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION: Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

New potent EV-A71 antivirals targeting capsid.

The enterovirus is a genus of single-stranded, highly diverse positive-sense RNA viruses, including Human Enterovirus A-D and Human Rhinovirus A-C species. They are responsible for numerous diseases and some infections can progress to life-threatening complications, particularly in children or immunocompromised patients. To date, there is no treatment against enteroviruses on the market, except for polioviruses (vaccine) and EV-A71 (vaccine in China). Following a decrease in enterovirus infections during and shortly after the (SARS-Cov2) lockdown, enterovirus outbreaks were once again detected, notably in young children. This reemergence highlights on the need to develop broad-spectrum treatment against enteroviruses. Over the last year, our research team has identified a new class of small-molecule inhibitors showing anti-EV activity. Targeting the well-known hydrophobic pocket in the viral capsid, these compounds show micromolar activity against EV-A71 and a high selectivity index (SI) (5h: EC50, MRC-5 = 0.57 µM, CC50, MRC-5 >20 µM, SI > 35; EC50, RD = 4.38 µM, CC50, RD > 40 µM, SI > 9; 6c: EC50, MRC-5 = 0.29 µM, CC50, MRC-5 >20 µM, SI > 69; EC50, RD = 1.66 µM, CC50, RD > 40 µM, SI > 24; Reference: Vapendavir EC50, MRC-5 = 0.36 µM, CC50, MRC-5 > 20 µM, EC50, RD = 0.53 µM, CC50, RD > 40 µM, SI > 63). The binding mode of these compounds in complex with enterovirus capsids was analyzed and showed a series of conserved interactions. Consequently, 6c and its derivatives are promising candidates for the treatment of enterovirus infections.

Structure-based screening, optimization and biological evaluation of novel chrysin-based derivatives as selective PPARγ modulators for the treatment of T2DM and hepatic steatosis.

In consideration of several serious side effects induced by the classical AF-2 involved "lock" mechanism, recently disclosed PPARγ-Ser273 phosphorylation mode of action has become an alternative and mainstream mechanism for currently PPARγ-based drug discovery and development with an improved therapeutic index. In this study, by virtue of structure-based virtual high throughput screening (SB-VHTS), structurally chemical optimization by targeting the inhibition of the PPARγ-Ser273 phosphorylation as well as in vitro biological evaluation, which led to the final identification of a chrysin-based potential hit (YGT-31) as a novel selective PPARγ modulator with potent binding affinity and partial agonism. Further in vivo evaluation demonstrated that YGT-31 possessed potent glucose-lowering and relieved hepatic steatosis effects without involving the TZD-associated side effects. Mechanistically, YGT-31 presented such desired therapeutic index, mainly because it effectively inhibited the CDK5-mediated PPARγ-Ser273 phosphorylation, selectively elevated the level of insulin sensitivity-related Glut4 and adiponectin but decreased the expression of insulin-resistance-associated genes PTP1B and SOCS3 as well as inflammation-linked genes IL-6, IL-1ß and TNFα. Finally, the molecular docking study was also conducted to uncover an interesting hydrogen-bonding network of YGT-31 with PPARγ-Ser273 phosphorylation-related key residues Ser342 and Glu343, which not only gave a clear verification for our targeting modification but also provided a proof of concept for the abovementioned molecular mechanism.

Structure-Based Design of Bicyclic Helical Peptides That Target the Oncogene ß-Catenin.

The inhibition of intracellular protein-protein interactions is challenging, in particular, when involved interfaces lack pronounced cavities. The transcriptional co-activator protein and oncogene ß­catenin is a prime example of such a challenging target. Despite extensive targeting efforts, available high-affinity binders comprise only large molecular weight Inhibitors. This hampers the further development of therapeutically useful compounds. Herein, we report the design of a considerably smaller peptidomimetic scaffold derived from the α-helical ß­catenin-binding motif of Axin. Sequence maturation and bicyclization provided a stitched peptide with an unprecedented crosslink architecture. The binding mode and site were confirmed by a crystal structure. Further derivatization yielded a ß-catenin inhibitor with single-digit micromolar activity in a cell-based assay. This study sheds a light on how to design helix mimetics with reduced molecular weight thereby improving their biological activity.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

Exploration of novel human neutrophil elastase inhibitors from natural compounds: Virtual screening, in vitro, molecular dynamics simulation and in vivo study.

BACKGROUND: Human neutrophil elastase (HNE) is an important contributor to lung diseases such as acute lung injury (ALI) or acute respiratory distress syndrome. Therefore, this study aimed to identify natural HNE inhibitors with anti-inflammatory activity through machine learning algorithms, in vitro assays, molecular dynamic simulation, and an in vivo ALI assay. METHODS: Based on the optimized Discovery Studio two-dimensional molecular descriptors, combined with different molecular fingerprints, six machine learning models were established using the Naïve Bayesian (NB) method to identify HNE inhibitors. Subsequently, the optimal model was utilized to screen 6925 drug-like compounds obtained from the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform (TCMSP), followed by ADMET analysis. Finally, 10 compounds with reported anti-inflammatory activity were selected to determine their inhibitory activities against HNE in vitro, and the compounds with the best activity were selected for a 100 ns molecular dynamics simulation and its anti-inflammatory effect was evaluated using Poly (I:C)-induced ALI model. RESULTS: The evaluation of the in vitro HNE inhibition efficiency of the 10 selected compounds showed that the flavonoid tricetin had the strongest inhibitory effect on HNE. The molecular dynamics simulation indicated that the binding of tricetin to HNE was relatively stable throughout the simulation. Importantly, in vivo experiments indicated that tricetin treatment substantially improved the Poly (I:C)-induced ALI. CONCLUSION: The proposed NB model was proved valuable for exploring novel HNE inhibitors, and natural tricetin was screened out as a novel HNE inhibitor, which was confirmed by in vitro and in vivo assays for its inhibitory activities.

Surface-mutagenesis strategies to enable structural biology crystallization platforms.

A key prerequisite for the successful application of protein crystallography in drug discovery is to establish a robust crystallization system for a new drug-target protein fast enough to deliver crystal structures when the first inhibitors have been identified in the hit-finding campaign or, at the latest, in the subsequent hit-to-lead process. The first crucial step towards generating well folded proteins with a high likelihood of crystallizing is the identification of suitable truncation variants of the target protein. In some cases an optimal length variant alone is not sufficient to support crystallization and additional surface mutations need to be introduced to obtain suitable crystals. In this contribution, four case studies are presented in which rationally designed surface modifications were key to establishing crystallization conditions for the target proteins (the protein kinases Aurora-C, IRAK4 and BUB1, and the KRAS-SOS1 complex). The design process which led to well diffracting crystals is described and the crystal packing is analysed to understand retrospectively how the specific surface mutations promoted successful crystallization. The presented design approaches are routinely used in our team to support the establishment of robust crystallization systems which enable structure-guided inhibitor optimization for hit-to-lead and lead-optimization projects in pharmaceutical research.

Discovery of Antibacterial Compounds with Potential Multi-Pharmacology against Staphylococcus Mur ligase Family Members by In Silico Structure-Based Drug Screening.

Staphylococcus aureus (S. aureus) is a major bacterial infection in humans, leading to severe disease and causing death. The stagnation of antibiotic development in recent decades has made it difficult to combat drug-resistant infections. In this study, we performed an in silico structure-based drug screening (SBDS) targeting the S. aureus MurE (saMurE) enzyme involved in cell wall synthesis of S. aureus. saMurE is an enzyme that is essential for the survival of S. aureus but not present in humans. SBDS identified nine saMurE inhibitor candidates, Compounds 1-9, from a structural library of 154,118 compounds. Among them, Compound 2 showed strong antibacterial activity against Staphylococcus epidermidis (S. epidermidis) used as a model bacterium. Amino acid sequence homology between saMurE and S. epidermidis MurE is 87.4%, suggesting that Compound 2 has a similar inhibitory effect on S. aureus. Compound 2 showed an IC50 value of 301 nM for S. epidermidis in the dose-dependent growth inhibition assay. Molecular dynamics simulation showed that Compound 2 binds stably to both S. aureus MurD and S. aureus MurF, suggesting that it is a potential multi-pharmacological pharmacological inhibitor. The structural and bioactivity information of Compound 2, as well as its potential multiple-target activity, could contribute to developing new antimicrobial agents based on MurE inhibition.

Advances in structure-based drug design targeting membrane protein markers in prostate cancer.

Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.

A Phenotypic Approach to the Discovery of Potent G-Quadruplex Targeted Drugs.

G-quadruplex (G4) sequences, which can fold into higher-order G4 structures, are abundant in the human genome and are over-represented in the promoter regions of many genes involved in human cancer initiation, progression, and metastasis. They are plausible targets for G4-binding small molecules, which would, in the case of promoter G4s, result in the transcriptional downregulation of these genes. However, structural information is currently available on only a very small number of G4s and their ligand complexes. This limitation, coupled with the currently restricted information on the G4-containing genes involved in most complex human cancers, has led to the development of a phenotypic-led approach to G4 ligand drug discovery. This approach was illustrated by the discovery of several generations of tri- and tetra-substituted naphthalene diimide (ND) ligands that were found to show potent growth inhibition in pancreatic cancer cell lines and are active in in vivo models for this hard-to-treat disease. The cycles of discovery have culminated in a highly potent tetra-substituted ND derivative, QN-302, which is currently being evaluated in a Phase 1 clinical trial. The major genes whose expression has been down-regulated by QN-302 are presented here: all contain G4 propensity and have been found to be up-regulated in human pancreatic cancer. Some of these genes are also upregulated in other human cancers, supporting the hypothesis that QN-302 is a pan-G4 drug of potential utility beyond pancreatic cancer.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Dscovery.

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Cryo-electron microscopy in the study of virus entry and infection.

Viruses have been responsible for many epidemics and pandemics that have impacted human life globally. The COVID-19 pandemic highlighted both our vulnerability to viral outbreaks, as well as the mobilization of the scientific community to come together to combat the unprecedented threat to humanity. Cryo-electron microscopy (cryo-EM) played a central role in our understanding of SARS-CoV-2 during the pandemic and continues to inform about this evolving pathogen. Cryo-EM with its two popular imaging modalities, single particle analysis (SPA) and cryo-electron tomography (cryo-ET), has contributed immensely to understanding the structure of viruses and interactions that define their life cycles and pathogenicity. Here, we review how cryo-EM has informed our understanding of three distinct viruses, of which two - HIV-1 and SARS-CoV-2 infect humans, and the third, bacteriophages, infect bacteria. For HIV-1 and SARS-CoV-2 our focus is on the surface glycoproteins that are responsible for mediating host receptor binding, and host and cell membrane fusion, while for bacteriophages, we review their structure, capsid maturation, attachment to the bacterial cell surface and infection initiation mechanism.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Structure-based design, synthesis, and biological characterization of indolylarylsulfone derivatives as novel human immunodeficiency virus type 1 inhibitors with potent antiviral activities and favorable drug-like profiles.

In the current antiretroviral landscape, continuous efforts are still needed to search for novel chemotypes of human immunodeficiency virus type 1 (HIV-1) inhibitors with improved drug resistance profiles and favorable drug-like properties. Herein, we report the design, synthesis, biological characterization, and druggability evaluation of a class of non-nucleoside reverse transcriptase inhibitors. Guided by the available crystallographic information, a series of novel indolylarylsulfone derivatives were rationally discovered via the substituent decorating strategy to fully explore the chemical space of the entrance channel. Among them, compound 11h bearing the cyano-substituted benzyl moiety proved to be the most effective inhibitor against HIV-1 wild-type and mutant strains (EC50 = 0.0039-0.338 µM), being far more potent than or comparable to etravirine and doravirine. Besides, 11h did not exhibit cytotoxicity at the maximum test concentration. Meanwhile, the binding target of 11h was further confirmed to be reverse transcriptase (IC50 = 0.055 µM). Preliminary structure-activity relationship were discussed to guide further optimization work. Molecular docking and dynamics simulation studies were investigated in detail to rationalize the biological evaluation results. Further drug-likeness assessment indicated that 11h possessed excellent physicochemical properties. Moreover, no apparent hERG blockade liability and cytochrome P450 inhibition were observed for 11h. Notably, 11h was characterized by favorable in vitro metabolic stability with moderate clearance rates and long half-lives in human plasma and liver microsomes. Overall, 11h holds great promise as an ideal Anti-HIV-1 lead compound due to its potent antiviral efficacy, low toxicity, and favorable drug-like profiles.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors.

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

Computational Methods to Target Protein-Protein Interactions.

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.