Development and validation of a genetic algorithm for flexible docking

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

In silico identification of D449-0032 compound as a putative SARS-CoV-2 Mpro inhibitor

The SARS-CoV-2 pandemic originated the urgency in developing therapeutic resources for the treatment of COVID-19. Despite the current availability of vaccines and some antivirals, the occurence of severe cases of the disease and the risk of the emergence of new virus variants still motivate research in this field. In this context, this study aimed at the computational prospection of likely inhibitors of the main protease (Mpro) of SARS-CoV-2 since inhibiting this enzyme leads to disruption of the viral replication process. The virtual screening of the antiviral libraries Asinex, ChemDiv, and Enamine targeting SARS-CoV-2 Mpro was performed, indicating the D449-0032 compound as a promising inhibitor. Molecular dynamics simulations showed the stability of the protein-ligand complex and in silico predictions of toxicity and pharmacokinetic parameters indicated the probable drug-like behavior of the compound. In vitro and in vivo studies are essential to confirm the Mpro inhibition by the D449-0032.Communicated by Ramaswamy H. Sarma.

Cyclopenta[b]indoles as novel antimicrotubule agents with antileukemia activity

Acute leukemias present therapeutic challenges despite advances in treatments. Microtubule inhibitors have played a pivotal role in cancer therapy, inspiring exploration into novel compounds like C2E1 from the cyclopenta[b]indole class. In the present study, we investigated C2E1's potential as a therapeutic agent for acute leukemia at molecular, cellular, and genetic levels. C2E1 demonstrated tubulin depolarization activity, significantly reducing leukemia cell viability. Its impact involved multifaceted mechanisms: inducing apoptosis, arrest of cell cycle progression, and inhibition of clonogenicity and migration in leukemia cells. At a molecular level, C2E1 triggered DNA damage, antiproliferative, and apoptosis markers and altered gene expression related to cytoskeletal regulation, disrupting essential cellular processes crucial for leukemia cell survival and proliferation. These findings highlight C2E1's promise as a potential candidate for novel anti-cancer therapies. Notably, its distinct mode of action from conventional microtubule-targeting drugs suggests the potential to bypass common resistance mechanisms encountered with existing treatments. In summary, C2E1 emerges as a compelling compound with diverse effects on leukemia cells, showcasing promising antineoplastic properties. Its ability to disrupt critical cellular functions selective to leukemia cells positions it as a candidate for future therapeutic development.

Synthesis, identification and evaluation of molecular docking and experimental anti-cancer and antioxidant activity of new spiro four membered ring derivatives bearing 5-nitro isatin

Spiro-5-nitro isatino aza-β-lactams were produced by a [2 + 2] cycloaddition of 5-nitro isatin Schiff bases [1-5] with different aromatic isocyanate and thioisocyanate. 1HNMR and 13CNMR as well as FTIR spectroscopies, were used to identify the structures of the obtained compounds. These spiro-5-nitro isatin aza- β-lactams interest to us due to their potential antioxidant and anticancer properties. The MTT assay was used to examine in vitro bioactivity testing against breast cancer (MCF-7) cell lines. From result data, compound 14 displayed IC50 values that were lower than those of the clinically used anticancer drug tamoxifen toward MCF-7 cells after 24 h while compound 9 after 48 h synthesized compounds [6-20] were evaluated for against antioxidant activity by using DPPH assay. In molecular docking, Promising compounds were used to reveal potential cytotoxic activity mechanisms.

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations

CONTEXT

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval.

METHODS

In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.

Antitumoral activity of different Amaryllidaceae alkaloids: In vitro and in silico assays

ETHNOPHARMACOLOGY RELEVANCE

The plants of Amaryllidaceae family, such as Amaryllis belladonna L., have been used as herbal remedies for thousands of years to address various disorders, including diseases that might today be identified as cancer.

AIM OF THE STUDY

The objective of this work was to evaluate the potential of three Amaryllidaceae alkaloids against four cancer cell lines.

MATERIAL AND METHODS

The alkaloids lycorine, 1-O-acetylcaranine, and montanine were evaluated in vitro against colon adenocarcinoma cell line (HCT-116) and breast carcinoma cell lines (MCF-7, MDAMB231, and Hs578T). Computational experiments (target prediction and molecular docking) were conducted to gain a deeper comprehension of possible interactions between these alkaloids and potential targets associated with these tumor cells.

RESULTS

Montanine presented the best results against HCT-116, MDAMB231, and Hs578T cell lines, while lycorine was the most active against MCF-7. In alignment with the target prediction outcomes and existing literature, four potential targets were chosen for the molecular docking analysis: CDK8, EGFR, ER-alpha, and dCK. The docking scores revealed two potential targets for the alkaloids with scores similar to co-crystallized inhibitors and substrates: CDK8 and dCK. A visual analysis of the optimal docked configurations indicates that the alkaloids may interact with some key residues in contrast to the other docked compounds. This observation implies their potential to bind effectively to both targets.

CONCLUSIONS

In vitro and in silico results corroborate with data literature suggesting the Amaryllidaceae alkaloids as interesting molecules with antitumoral properties, especially montanine, which showed the best in vitro results against colorectal and breast carcinoma. More studies are necessary to confirm the targets and pharmaceutical potential of montanine against these cancer cell lines.

Potent anti-coronaviral activity of pateamines and new insights into their mode of action

Pateamines, derived from the sponge Mycale hentscheli, function as inhibitors of the RNA helicase eIF4A and exhibit promising antiviral and anticancer properties. eIF4A plays a pivotal role in unwinding stable RNA structures within the 5'-UTR of selected mRNAs, facilitating the binding of the 43S preinitiation complex during translation initiation. Pateamines function by clamping RNA substrates onto the eIF4A surface, effectively preventing eIF4A from carrying out the unwinding step. Rocaglates, a compound class isolated from plants of the genus Aglaia, target the same binding pocket on eIF4A, and based on structural data, a similar mode of action has been proposed for pateamines and rocaglates. In this study, we conducted a detailed characterization of pateamines' binding mode and assessed their antiviral activity against human pathogenic coronaviruses (human coronavirus 229E (HCoV-229E), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)). Our findings reveal significant differences in the binding behavior of pateamines compared to rocaglates when interacting with an eIF4A-RNA complex. We also observed that pateamines do not depend on the presence of a polypurine tract in the RNA substrate for efficient RNA clamping, as it is the case for rocaglates. Most notably, pateamines demonstrate potent antiviral activity against coronaviruses in the low nanomolar range. Consequently, pateamines broaden our toolbox for combating viruses that rely on the host enzyme eIF4A to conduct their viral protein synthesis, indicating a possible future treatment strategy against new or re-emerging pathogenic viruses.

Are the metal identity and stoichiometry of metal complexes important for colchicine site binding and inhibition of tubulin polymerization?

Quite recently we discovered that copper(II) complexes with isomeric morpholine-thiosemicarbazone hybrid ligands show good cytotoxicity in cancer cells and that the molecular target responsible for this activity might be tubulin. In order to obtain better lead drug candidates, we opted to exploit the power of coordination chemistry to (i) assemble structures with globular shape to better fit the colchicine pocket and (ii) vary the metal ion. We report the synthesis and full characterization of bis-ligand cobalt(III) and iron(III) complexes with 6-morpholinomethyl-2-formylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL1), 6-morpholinomethyl-2-acetylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL2), and 6-morpholinomethyl-2-formylpyridine 4N-phenyl-3-thiosemicarbazone (HL3), and mono-ligand nickel(II), zinc(II) and palladium(II) complexes with HL1, namely [CoIII(HL1)(L1)](NO3)2 (1), [CoIII(HL2)(L2)](NO3)2 (2), [CoIII(HL3)(L3)](NO3)2 (3), [FeIII(L2)2]NO3 (4), [FeIII(HL3)(L3)](NO3)2 (5), [NiII(L1)]Cl (6), [Zn(L1)Cl] (7) and [PdII(HL1)Cl]Cl (8). We discuss the effect of the metal identity and metal complex stoichiometry on in vitro cytotoxicity and antitubulin activity. The high antiproliferative activity of complex 4 correlated well with inhibition of tubulin polymerization. Insights into the mechanism of antiproliferative activity were supported by experimental results and molecular docking calculations.

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

Structure-Based Design and Synthesis of Covalent Inhibitors for Deubiquitinase and Acetyltransferase ChlaDUB1 of Chlamydia trachomatis

Upon infection by an intracellular pathogen, host cells activate apoptotic pathways to limit pathogen replication. Consequently, efficient proliferation of the obligate intracellular pathogen Chlamydia trachomatis, a major cause of trachoma and sexually transmitted diseases, depends on the suppression of host cell apoptosis. C. trachomatis secretes deubiquitinase ChlaDUB1 into the host cell, leading among other interactions to the stabilization of antiapoptotic proteins and, thus, suppression of host cell apoptosis. Targeting the bacterial effector protein may, therefore, lead to new therapeutic possibilities. To explore the active site of ChlaDUB1, an iterative cycle of computational docking, synthesis, and enzymatic screening was applied with the aim of lead structure development. Hereby, covalent inhibitors were developed, which show enhanced inhibition with a 22-fold increase in IC50 values compared to previous work. Comprehensive insights into the binding prerequisites to ChlaDUB1 are provided, establishing the foundation for an additional specific antichlamydial therapy by small molecules.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

In silico evidence of bitopertin's broad interactions within the SLC6 transporter family

The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property.

MolModa: accessible and secure molecular docking in a web browser

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.

Phytochemical composition of aerial parts and roots of Pfaffia glomerata (Spreng.) Pedersen and anticholinesterase, antioxidant, and antiglycation activities

The Pfaffia glomerata, a plant popularly called Brazilian ginseng, is widely used in Brazil for the treatment of various pathologies, including those associated with the Central Nervous System. 20-hydroxyecdysone (20E), a phytosteroid present in this plant, can promote adaptogenic effects in the organism, providing greater body resistance to stressors. This study aimed to evaluate the phytochemical composition and the anticholinesterase, antioxidant, and antiglycation effects of extracts and fractions of aerial parts and roots of P. glomerata, also analyzing their possible cytotoxic effects. The fractions were obtained by partitioning methanol extracts from the aerial part and roots of P. glomerata with hexane, dichloromethane, ethyl acetate, n-butanol, and water. The samples were initially tested in anticholinesterase, antioxidant, and antiglycation assays, and the most promising samples were submitted for cytotoxicity and chromatographic analyses. Mass spectrometry and chromatography methods revealed that 20E was the main compound in the dichloromethane fractions, there being 35% more 20E in the aerial part (APD) than in the roots (RD). Added to the higher concentration of 20E, the APD fraction also presented more promising results than the RD fraction in anticholinesterase and antioxidant analyses, indicating that their effects may be related to the concentration of 20E. These same fractions showed no hemolytic effects but were cytotoxic in high concentrations. These new findings contribute to scientific information about P. glomerata and open more perspectives for the understanding of its therapeutic properties, allowing the association of biological activity with the presence of 20E.

Disrupting TSLP-TSLP receptor interactions via putative small molecule inhibitors yields a novel and efficient treatment option for atopic diseases

Thymic stromal lymphopoietin (TSLP) is a key player in atopic diseases, which has sparked great interest in therapeutically targeting TSLP. Yet, no small-molecule TSLP inhibitors exist due to the challenges of disrupting the protein-protein interaction between TSLP and its receptor. Here, we report the development of small-molecule TSLP receptor inhibitors using virtual screening and docking of >1,000,000 compounds followed by iterative chemical synthesis. BP79 emerged as our lead compound that effectively abrogates TSLP-triggered cytokines at low micromolar concentrations. For in-depth analysis, we developed a human atopic disease drug discovery platform using multi-organ chips. Here, topical application of BP79 onto atopic skin models that were co-cultivated with lung models and Th2 cells effectively suppressed immune cell infiltration and IL-13, IL-4, TSLP, and periostin secretion, while upregulating skin barrier proteins. RNA-Seq analysis corroborate these findings and indicate protective downstream effects on the lungs. To the best of our knowledge, this represents the first report of a potent putative small molecule TSLPR inhibitor which has the potential to expand the therapeutic and preventive options in atopic diseases.

Efficient, rapid, and high-yield synthesis of aryl Schiff base derivatives and their in vitro and in silico inhibition studies of hCA I, hCA II, AChE, and BuChE

This study reports a rapid and efficient synthesis of four novel aryl Schiff base derivatives. Biological activity and molecular modeling studies were conducted to evaluate the inhibitory effects of these compounds on human carbonic anhydrases (hCA) and cholinesterases. The results indicate that the triazole-ring-containing compounds have strong inhibitory effects on hCA I, hCA II, acetylcholinesterase (AChE), and butyrylcholinesterase (BuChE) targets. Besides comparing the Schiff bases synthesized in our study to reference molecules, we conducted in silico investigations to examine how these compounds interact with their targets. Our studies revealed that these compounds can occupy binding sites and establish interactions with crucial residues, thus inhibiting the functions of the targets. These findings have significant implications as they can be utilized to develop more potent compounds for treating the diseases that these target proteins play crucial roles in or to obtain drug precursors with enhanced efficacy.

Interaction with CT-DNA and in vitro cytotoxicity of two new copper(II)-based potential drugs derived from octanoic hydrazide ligands

Two copper(II) complexes [Cu(Hpmoh)(NO3)(NCS)] (1) and [Cu(peoh)(N3)]2 (2) were designed and synthesized by reaction of Cu(NO3)2·3H2O with hydrazone Schiff base ligands,abbreviated with Hpmoh and Hpeoh. Hpmoh and Hpeoh were prepared by condensation reaction of octanoic hydrazide with pyridine-2-carboxyaldehyde and 2-acetylpyridine, respectively. Complexes 1 and 2 were characterized using different analytical techniques such as FT-IR, UV-Vis, IR, EPR and single X-ray diffraction (XRD) analyses as well as computational methods (DFT). The XRD of 1 and 2 shows a mononuclear or a dinuclear structure with the copper(II) centre adopting a slightly distorted square pyramidal geometry. In water-containing solution and in DMSO, 1 and 2 undergo a partial transformation with formation of [Cu(Hpmoh)(NO3)(NCS)] (1) and [Cu(Hpmoh)(NO3)(H2O/DMSO)] (1a) in one system and [Cu(peoh)(N3)] (2a) in the other one, as supported by DFT calculations. Docking simulations confirmed that the intercalation is the preferred binding mode with DNA for 1, 1a and 2a, but suggested that the minor groove binding is also possible. A significant fluorescence quenching of the DNA-ethidium bromide conjugate was observed upon the addition of complexes 1 and 2 with a quenching constant around 104 M-1 s-1. Finally, both 1 and 2 were examined for anti-cancer activity using MDA-MB-231 (human breast adenocarcinoma) and A375 (malignant melanoma) cell lines through in vitro MTT assay which suggest comparable cancer cell killing efficacy, with the higher effectiveness of 2 due to the dissociation into two [Cu(peoh)(N3)] units.

LIKE EARLY STARVATION 1 interacts with amylopectin during starch biosynthesis

Starch is the major energy storage compound in plants. Both transient starch and long-lasting storage starch accumulate in the form of insoluble, partly crystalline granules. The structure of these granules is related to the structure of the branched polymer amylopectin: linear chains of glucose units organized in double helices that align to form semicrystalline lamellae, with branching points located in amorphous regions between them. EARLY STARVATION 1 (ESV1) and LIKE EARLY STARVATION 1 (LESV) proteins are involved in the maintenance of starch granule structure and in the phase transition of amylopectin, respectively, in Arabidopsis (Arabidopsis thaliana). These proteins contain a conserved tryptophan-rich C-terminal domain folded into an antiparallel β-sheet, likely responsible for binding of the proteins to starch, and different N-terminal domains whose structure and function are unknown. In this work, we combined biochemical and biophysical approaches to analyze the structures of LESV and ESV1 and their interactions with the different starch polyglucans. We determined that both proteins interact with amylopectin but not with amylose and that only LESV is capable of interacting with amylopectin during starch biosynthesis. While the C-terminal domain interacts with amylopectin in its semicrystalline form, the N-terminal domain of LESV undergoes induced conformational changes that are probably involved in its specific function of mediating glucan phase transition. These results clarify the specific mechanism of action of these 2 proteins in the biosynthesis of starch granules.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

Repurposing Hsp90 inhibitors as antimicrobials targeting two-component systems identifies compounds leading to loss of bacterial membrane integrity

The discovery of antimicrobials with novel mechanisms of action is crucial to tackle the foreseen global health crisis due to antimicrobial resistance. Bacterial two-component signaling systems (TCSs) are attractive targets for the discovery of novel antibacterial agents. TCS-encoding genes are found in all bacterial genomes and typically consist of a sensor histidine kinase (HK) and a response regulator. Due to the conserved Bergerat fold in the ATP-binding domain of the TCS HK and the human chaperone Hsp90, there has been much interest in repurposing inhibitors of Hsp90 as antibacterial compounds. In this study, we explore the chemical space of the known Hsp90 inhibitor scaffold 3,4-diphenylpyrazole (DPP), building on previous literature to further understand their potential for HK inhibition. Six DPP analogs inhibited HK autophosphorylation in vitro and had good antimicrobial activity against Gram-positive bacteria. However, mechanistic studies showed that their antimicrobial activity was related to damage of bacterial membranes. In addition, DPP analogs were cytotoxic to human embryonic kidney cell lines and induced the cell arrest phenotype shown for other Hsp90 inhibitors. We conclude that these DPP structures can be further optimized as specific disruptors of bacterial membranes providing binding to Hsp90 and cytotoxicity are lowered. Moreover, the X-ray crystal structure of resorcinol, a substructure of the DPP derivatives, bound to the HK CheA represents a promising starting point for the fragment-based design of novel HK inhibitors.

IMPORTANCE

The discovery of novel antimicrobials is of paramount importance in tackling the imminent global health crisis of antimicrobial resistance. The discovery of novel antimicrobials with novel mechanisms of actions, e.g., targeting bacterial two-component signaling systems, is crucial to bypass existing resistance mechanisms and stimulate pharmaceutical innovations. Here, we explore the possible repurposing of compounds developed in cancer research as inhibitors of two-component systems and investigate their off-target effects such as bacterial membrane disruption and toxicity. These results highlight compounds that are promising for further development of novel bacterial membrane disruptors and two-component system inhibitors.

Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives

Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.

A Structure-Activity Investigation of the Fungal Metabolite (-)-TAN-2483B: Inhibition of Bruton's Tyrosine Kinase

The natural product (-)-TAN-2483B is a fungal secondary metabolite which displays promising anti-cancer and immunomodulatory activity. Our previous syntheses of (-)-TAN-2483B and sidechain analogues uncovered inhibitory activity against Bruton's tyrosine kinase (Btk), an established drug target for various leukaemia and immunological diseases. A structure-based computational study using ensemble docking and molecular dynamics was performed to determine plausible binding modes for (-)-TAN-2483B and analogues in the Btk binding site. These hypotheses guided the design of new analogues which were synthesised and their inhibitory activities determined, providing insights into the structural determinants of the furopyranone scaffold that confer both activity and selectivity for Btk. These findings offer new perspectives for generating optimised (-)-TAN-2483B-based kinase inhibitors for the treatment of leukaemia and immunological diseases.

Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme

The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Bioisosteric analogs of MDMA: Improving the pharmacological profile?

3,4-Methylenedioxymethamphetamine (MDMA, 'ecstasy') is re-emerging in clinical settings as a candidate for the treatment of specific neuropsychiatric disorders (e.g. post-traumatic stress disorder) in combination with psychotherapy. MDMA is a psychoactive drug, typically regarded as an empathogen or entactogen, which leads to transporter-mediated monoamine release. Despite its therapeutic potential, MDMA can induce dose-, individual-, and context-dependent untoward effects outside safe settings. In this study, we investigated whether three new methylenedioxy bioisosteres of MDMA improve its off-target profile. In vitro methods included radiotracer assays, transporter electrophysiology, bioluminescence resonance energy transfer and fluorescence-based assays, pooled human liver microsome/S9 fraction incubations, metabolic stability studies, isozyme mapping, and liquid chromatography coupled to high-resolution mass spectrometry. In silico methods included molecular docking. Compared with MDMA, all three MDMA bioisosteres (ODMA, TDMA, and SeDMA) showed similar pharmacological activity at human serotonin, dopamine, and norepinephrine transporters (hSERT, hDAT, and hNET, respectively) but decreased agonist activity at 5-HT2A/2B/2C receptors. Regarding their hepatic metabolism, they differed from MDMA, with N-demethylation being the only metabolic route shared, and without forming phase II metabolites. In addition, TDMA showed an enhanced intrinsic clearance in comparison to its congeners. Additional screening for their interaction with human organic cation transporters (hOCTs) and plasma membrane monoamine transporter (hPMAT) revealed a weaker interaction of the MDMA analogs with hOCT1, hOCT2, and hPMAT. Our findings suggest that these new MDMA bioisosteres might constitute appealing therapeutic alternatives to MDMA, sparing the primary pharmacological activity at hSERT, hDAT, and hNET, but displaying a reduced activity at 5-HT2A/2B/2C receptors and alternative hepatic metabolism. Whether these MDMA bioisosteres may pose lower risk alternatives to the clinically re-emerging MDMA warrants further studies.

Novel Penicillin-Based Sulfone-Siderophore Conjugates for Restoring β-Lactam Antibiotic Efficacy

Membrane permeability is a natural defense barrier that contributes to increased bacterial drug resistance, particularly for Gram-negative pathogens. As such, accurate delivery of the antibacterial agent to the target has become a growing research area in the infectious diseases field as a means of improving drug efficacy. Although the efficient transport of siderophore-antibiotic conjugates into the cytosol still remains challenging, great success has been achieved in the delivery of β-lactam antibiotics into the periplasmic space via bacterial iron uptake pathways. Cefiderocol, the first siderophore-cephalosporin conjugate approved by the US Food and Drug Administration, is a good example. These conjugation strategies have also been applied to the precise delivery of β-lactamase inhibitors, such as penicillin-based sulfone 1, to restore β-lactam antibiotic efficacy in multidrug-resistant bacteria. Herein, we have explored the impact on the bacterial activity of 1 by modifying its iron chelator moiety. A set of derivatives functionalized with diverse iron chelator groups and linkages to the scaffold (compounds 2-8) were synthesized and assayed in vitro. The results on the ability of derivatives 2-8 to recover β-lactam antibiotic efficacy in difficult-to-treat pathogens that produce various β-lactamase enzymes, along with kinetic studies with the isolated enzymes, allowed us to identify compound 2, a novel β-lactamase inhibitor with an expanded spectrum of activity. Molecular dynamics simulation studies provided us with further information regarding the molecular basis of the relative inhibitory properties of the most relevant compound described herein.

Copper(II) Complexes with Isomeric Morpholine-Substituted 2-Formylpyridine Thiosemicarbazone Hybrids as Potential Anticancer Drugs Inhibiting Both Ribonucleotide Reductase and Tubulin Polymerization: The Morpholine Position Matters

The development of copper(II) thiosemicarbazone complexes as potential anticancer agents, possessing dual functionality as inhibitors of R2 ribonucleotide reductase (RNR) and tubulin polymerization by binding at the colchicine site, presents a promising avenue for enhancing therapeutic effectiveness. Herein, we describe the syntheses and physicochemical characterization of four isomeric proligands H2L3-H2L6, with the methylmorpholine substituent at pertinent positions of the pyridine ring, along with their corresponding Cu(II) complexes 3-6. Evidently, the position of the morpholine moiety and the copper(II) complex formation have marked effects on the in vitro antiproliferative activity in human uterine sarcoma MES-SA cells and the multidrug-resistant derivative MES-SA/Dx5 cells. Activity correlated strongly with quenching of the tyrosyl radical (Y•) of mouse R2 RNR protein, inhibition of RNR activity in the cancer cells, and inhibition of tubulin polymerization. Insights into the mechanism of antiproliferative activity, supported by experimental results and molecular modeling calculations, are presented.

Virtual screening of natural products to enhance melanogenosis

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

Molecular modeling of multi-target analogs of huperzine A and applications in Alzheimer's disease

CONTEXT

Given the diverse pathophysiological mechanisms underlying Alzheimer's disease, it is improbable that a single targeted drug will prove successful as a therapeutic strategy. Therefore, exploring various hypotheses in drug design is imperative. The sequestration of Fe(II) and Zn(II) cations stands out as a crucial mechanism based on the mitigation of reactive oxygen species. Moreover, inhibiting acetylcholinesterase represents a pivotal strategy to enhance acetylcholine levels in the synaptic cleft. This research aims to investigate the analogs of Huperzine A, documented in scientific literature, considering of these two hypotheses. Consequently, the speciation chemistry of these structures with Fe(II) and Zn(II) was scrutinized using quantum chemistry calculations, molecular docking simulations, and theoretical predictions of pharmacokinetics properties. From the pharmacokinetic properties, only two analogs, HupA-A1 and HupA-A2, exhibited a theoretical permeability across the blood-brain barrier; on the other hand, from a thermodynamic standpoint, the enantiomers of HupA-A2 showed negligible chelation values. The enantiomers with the most favorable interaction parameters were S'R'HupA-A1 (ΔGBIND = -40.0 kcal mol-1, fitness score = 35.5) and R'R'HupA-A1 (ΔGBIND = -35.5 kcal mol-1, fitness score = 22.61), being compared with HupA (ΔGBIND = -41.75 kcal mol-1, fitness score = 39.95). From this study, some prime candidates for promising drug were S'R'HupA-A1 and R'R'HupA-A1, primarily owing to their favorable thermodynamic chelating capability and potential anticholinesterase mechanism.

METHODS

Quantum chemistry calculations were carried out at B3LYP/6-31G(d) level, considering the IEF-PCM(UFF) implicit solvent model for water. The coordination compounds were assessed using the Gibbs free energy variation and hard and soft acid theory. Molecular docking calculations were conducted using the GOLD program, based on the crystal structure of the acetylcholinesterase protein (PDB code = 4EY5), where the ChemScore function was employed with the active site defined as the region within a 15-Å radius around the centroid coordinates (X = -9.557583, Y = -43.910473, Z = 31.466687). Pharmacokinetic properties were predicted using SwissADME, focusing on Lipinski's rule of five.

Preferential HDAC6 inhibitors derived from HPOB exhibit synergistic antileukemia activity in combination with decitabine

Histone deacetylase 6 (HDAC6) is an emerging drug target to treat oncological and non-oncological conditions. Since highly selective HDAC6 inhibitors display limited anticancer activity when used as single agent, they usually require combination therapies with other chemotherapeutics. In this work, we synthesized a mini library of analogues of the preferential HDAC6 inhibitor HPOB in only two steps via an Ugi four-component reaction as the key step. Biochemical HDAC inhibition and cell viability assays led to the identification of 1g (highest antileukemic activity) and 2b (highest HDAC6 inhibition) as hit compounds. In subsequent combination screens, both 1g and especially 2b showed synergy with DNA methyltransferase inhibitor decitabine in acute myeloid leukemia (AML). Our findings highlight the potential of combining HDAC6 inhibitors with DNA methyltransferase inhibitors as a strategy to improve AML treatment outcomes.

Influence of heterochirality on the structure, dynamics, biological properties of cyclic(PFPF) tetrapeptides obtained by solvent-free ball mill mechanosynthesis

Cyclic tetrapeptides c(Pro-Phe-Pro-Phe) obtained by the mechanosynthetic method using a ball mill were isolated in a pure stereochemical form as a homochiral system (all L-amino acids, sample A) and as a heterochiral system with D configuration at one of the stereogenic centers of Phe (sample B). The structure and stereochemistry of both samples were determined by X-ray diffraction studies of single crystals. In DMSO and acetonitrile, sample A exists as an equimolar mixture of two conformers, while only one is monitored for sample B. The conformational space and energetic preferences for possible conformers were calculated using DFT methods. The distinctly different conformational flexibility of the two samples was experimentally proven by Variable Temperature (VT) and 2D EXSY NMR measurements. Both samples were docked to histone deacetylase HDAC8. Cytotoxic studies proved that none of the tested cyclic peptide is toxic.

Recent advances in computational and experimental protein-ligand affinity determination techniques

INTRODUCTION

Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved.

AREAS COVERED

In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design.

EXPERT OPINION

The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Nature-inspired substituted 3-(imidazol-2-yl) morpholines targeting human topoisomerase IIα: Dynophore-derived discovery

The molecular nanomachine, human DNA topoisomerase IIα, plays a crucial role in replication, transcription, and recombination by catalyzing topological changes in the DNA, rendering it an optimal target for cancer chemotherapy. Current clinical topoisomerase II poisons often cause secondary tumors as side effects due to the accumulation of double-strand breaks in the DNA, spurring the development of catalytic inhibitors. Here, we used a dynamic pharmacophore approach to develop catalytic inhibitors targeting the ATP binding site of human DNA topoisomerase IIα. Our screening of a library of nature-inspired compounds led to the discovery of a class of 3-(imidazol-2-yl) morpholines as potent catalytic inhibitors that bind to the ATPase domain. Further experimental and computational studies identified hit compound 17, which exhibited selectivity against the human DNA topoisomerase IIα versus human protein kinases, cytotoxicity against several human cancer cells, and did not induce DNA double-strand breaks, making it distinct from clinical topoisomerase II poisons. This study integrates an innovative natural product-inspired chemistry and successful implementation of a molecular design strategy that incorporates a dynamic component of ligand-target molecular recognition, with comprehensive experimental characterization leading to hit compounds with potential impact on the development of more efficient chemotherapies.

Emerging structure-based computational methods to screen the exploding accessible chemical space

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

Cyrene™ as a tyrosinase inhibitor and anti-browning agent

The tyrosinase pathway takes part in the enzymatic process of food browning and is primarily responsible for food spoilage - manifesting itself from a decrease in its nutritional value to a deterioration of taste, which consequently leads to a gradual loss of shelf life. Finding safe and bio-based tyrosinase inhibitors and anti-browning agents may be of great importance in agriculture and food industries. Herein, we showed that Cyrene™ exhibits tyrosinase inhibitory activity (IC50: 268.2 µM), the 1.44 times higher than ascorbic acid (IC50: 386.5 μM). Binding mode studies demonstrated that the carbonyl oxygen of Cyrene™ coordinates with both copper ions. Surprisingly, both hydroxyl groups of Cyrene gem-diol perform a monodentate binding mode with both copper ions, at similar distances. This fact suggests that both compounds could have a similar binding mode and, as consequence, similar biological activities in tyrosinase inhibition assays and anti-browning activities.

Data on molecular docking and molecular dynamics targeting Mycobacterium tuberculosis shikimic acid kinase

We have previously performed a hierarchical in silico screening of a Mycobacterium tuberculosis shikimic acid kinase [1]. Specifically, 11 compounds were screened from a library of 154,118 compounds provided by ChemBridge [2] using UCSF DOCK [3] and the GOLD [4] program in the first and second steps, respectively. Molecular dynamic simulations were further performed on compound 2 (2-[(5Z)-5-(1-benzyl-5bromo-2-oxoindol-3-(5Z)-5-(1-benzyl-5-bromo-2-oxoindol-3-(5Z)-4-oxo-2 ylidene)-4oxo-2-sulfanylidene-1,3-thiazolidin-3-yl] acetic acid), which showed antimicrobial efficacy. These processes yielded ligand docking scores and trajectories. In this data article, we have added solvent-accessible surface area and PCA analyses, which were calculated from the raw docking scores and trajectories. Data obtained from molecular docking and molecular dynamic simulations are useful in two ways: (1) Further support for previous work (2) Provides a stepping stone for experimental scientists to conduct in silico studies and research ideas for other drug discovery researchers and computational biologists. We believe that this article will provide an opportunity to develop new Mycobacterium tuberculosis therapeutics through searching for analogs and inhibitors against new targets.

Dual action of benzaldehydes: Inhibiting quorum sensing and enhancing antibiotic efficacy for controlling Pseudomonas aeruginosa biofilms

Quorum sensing (QS) has a central role in biofilm lifestyle and antimicrobial resistance, and disrupting these signaling pathways is a promising strategy to control bacterial pathogenicity and virulence. In this study, the efficacy of three structurally related benzaldehydes (4-hydroxybenzaldehyde, 4-hydroxy-3-methoxybenzaldehyde (vanillin) and 4-hydroxy-3,5-dimethoxybenzaldehyde (syringaldehyde)) in disrupting the las and pqs systems of Pseudomonas aeruginosa was investigated using bioreporter strains and computational simulations. Additionally, these benzaldehydes were combined with tobramycin and ciprofloxacin antibiotics to evaluate their ability to increase antibiotic efficacy in preventing and eradicating P. aeruginosa biofilms. To this end, the total biomass, metabolic activity and culturability of the biofilm cells were determined. In vitro assays results indicated that the aromatic aldehydes have potential to inhibit the las and pqs systems by > 80 %. Molecular docking studies supported these findings, revealing the aldehydes binding in the same pocket as the natural ligands or receptor proteins (LasR, PQSA, PQSE, PQSR). Benzaldehydes were shown to act as virulence factor attenuators, with vanillin achieving a 48 % reduction in pyocyanin production. The benzaldehyde-tobramycin combination led not only to a 60 % reduction in biomass production but also to a 90 % reduction in the metabolic activity of established biofilms. A similar result was observed when benzaldehydes were combined with ciprofloxacin. 4-Hydroxybenzaldehyde demonstrated relevant action in increasing biofilm susceptibility to ciprofloxacin, resulting in a 65 % reduction in biomass. This study discloses, for the first time, that the benzaldehydes studied are potent QS inhibitors and also enhancers of antibiotics antibiofilm activity against P. aeruginosa.

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design

Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

DszA Catalyzes C-S Bond Cleavage through N5-Hydroperoxyl Formation

Due to its detrimental impact on human health and the environment, regulations demand ultralow sulfur levels on fossil fuels, in particular in diesel. However, current desulfurization techniques are expensive and cannot efficiently remove heteroaromatic sulfur compounds, which are abundant in crude oil and concentrate in the diesel fraction after distillation. Biodesulfurization via the four enzymes of the metabolic 4S pathway of the bacterium Rhodococcus erythropolis (DszA-D) is a possible solution. However, the 4S pathway needs to operate at least 500 times faster for industrial applicability, a goal currently pursued through enzyme engineering. In this work, we unveil the catalytic mechanism of the flavin monooxygenase DszA. Surprisingly, we found that this enzyme follows a recently proposed atypical mechanism that passes through the formation of an N5OOH intermediate at the re side of the cofactor, aided by a well-defined, predominantly hydrophobic O2 pocket. Besides clarifying the unusual chemical mechanism of the complex DszA enzyme, with obvious implications for understanding the puzzling chemistry of flavin-mediated catalysis, the result is crucial for the rational engineering of DszA, contributing to making biodesulfurization attractive for the oil refining industry.

The Identification of Peptide Inhibitors of the Coronavirus 3CL Protease from a Fucus ceranoides L. Hydroalcoholic Extract Using a Ligand-Fishing Strategy

Brown seaweeds of the Fucus genus represent a rich source of natural antiviral products. In this study, a Fucus ceranoides hydroalcoholic extract (FCHE) was found to inhibit 74.2 ± 1.3% of the proteolytic activity of the free SARS-CoV-2 3CL protease (3CLpro), an enzyme that plays a pivotal role in polyprotein processing during coronavirus replication and has been identified as a relevant drug discovery target for SARS- and MERS-CoVs infections. To purify and identify 3CLpro ligands with potential inhibitory activity using a one-step approach, we immobilized the enzyme onto magnetic microbeads (3CLpro-MPs), checked that the enzymatic activity was maintained after grafting, and used this bait for a ligand-fishing strategy followed by a high-resolution mass spectrometry analysis of the fished-out molecules. Proof of concept for the ligand-fishing capacity of the 3CLpro-MPs was demonstrated by doping the FCHE extract with the substrate peptide TSAVLQ-pNA, resulting in the preferential capture of this high-affinity peptide within the macroalgal complex matrix. Ligand fishing in the FCHE alone led to the purification and identification via high-resolution mass spectrometry (HRMS) of seven hepta-, octa-, and decapeptides in an eluate mix that significantly inhibited the free 3CLpro more than the starting FCHE (82.7 ± 2.2% inhibition). Molecular docking simulations of the interaction between each of the seven peptides and the 3CLpro demonstrated a high affinity for the enzyme's proteolytic active site surpassing that of the most affine peptide ligand identified so far (a co-crystallographic peptide). Testing of the corresponding synthetic peptides demonstrated that four out of seven significantly inhibited the free 3CLpro (from 46.9 ± 6.4 to 76.8 ± 3.6% inhibition at 10 µM). This study is the first report identifying peptides from Fucus ceranoides with high inhibitory activity against the SARS-CoV-2 3CLprotease which bind with high affinity to the protease's active site. It also confirms the effectiveness of the ligand-fishing strategy for the single-step purification of enzyme inhibitors from complex seaweed matrices.

Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

Suppressed basal mitophagy drives cellular aging phenotypes that can be reversed by a p62-targeting small molecule

Selective degradation of damaged mitochondria by autophagy (mitophagy) is proposed to play an important role in cellular homeostasis. However, the molecular mechanisms and the requirement of mitochondrial quality control by mitophagy for cellular physiology are poorly understood. Here, we demonstrated that primary human cells maintain highly active basal mitophagy initiated by mitochondrial superoxide signaling. Mitophagy was found to be mediated by PINK1/Parkin-dependent pathway involving p62 as a selective autophagy receptor (SAR). Importantly, this pathway was suppressed upon the induction of cellular senescence and in naturally aged cells, leading to a robust shutdown of mitophagy. Inhibition of mitophagy in proliferating cells was sufficient to trigger the senescence program, while reactivation of mitophagy was necessary for the anti-senescence effects of NAD precursors or rapamycin. Furthermore, reactivation of mitophagy by a p62-targeting small molecule rescued markers of cellular aging, which establishes mitochondrial quality control as a promising target for anti-aging interventions.

Modulation of the photobehavior of gefitinib and its phenolic metabolites by human transport proteins

The photobiological damage that certain drugs or their metabolites can photosensitize in proteins is generally associated with the nature of the excited species that are generated upon interaction with UVA light. In this regard, the photoinduced damage of the anticancer drug gefitinib (GFT) and its two main photoactive metabolites GFT-M1 and GFT-M2 in cellular milieu was recently investigated. With this background, the photophysical properties of both the drug and its metabolites have now been studied in the presence of the two main transport proteins of human plasma, i.e., serum albumin (HSA) and α1-acid glycoprotein (HAG) upon UVA light excitation. In general, the observed photobehavior was strongly affected by the confined environment provided by the protein. Thus, GFT-M1 (which exhibits the highest phototoxicity) showed the highest fluorescence yield arising from long-lived HSA-bound phenolate-like excited species. Conversely, locally excited (LE) states were formed within HAG, resulting in lower fluorescence yields. The reserve was true for GFT-M2, which despite being also a phenol, led mainly to formation of LE states within HSA, and phenolate-like species (with a minor contribution of LE) inside HAG. Finally, the parent drug GFT, which is known to form LE states within HSA, exhibited a parallel behavior in the two proteins. In addition, determination of the association constants by both absorption and emission spectroscopy revealed that the two metabolites bind stronger to HSA than the parent drug, whereas smaller differences were observed for HAG. This was further confirmed by studying the competing interactions between GFT or its metabolites with the two proteins using fluorescence measurements. These above experimental findings were satisfactorily correlated with the results obtained by means of molecular dynamics (MD) simulations, which revealed the high affinity binding sites, the strength of interactions and the involved amino acid residues. In general, the differences observed in the photobehavior of the drug and its two photoactive metabolites in protein media are consistent with their relative photosensitizing potentials.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Experimental and Computational Studies on the Interaction of DNA with Hesperetin Schiff Base CuII Complexes

The interactions with calf thymus DNA (CT-DNA) of three Schiff bases formed by the condensation of hesperetin with benzohydrazide (HHSB or L1H3), isoniazid (HIN or L2H3), or thiosemicarbazide (HTSC or L3H3) and their CuII complexes (CuHHSB, CuHIN, and CuHTSC with the general formula [CuLnH2(AcO)]) were evaluated in aqueous solution both experimentally and theoretically. UV-Vis studies indicate that the ligands and complexes exhibit hypochromism, which suggests helical ordering in the DNA helix. The intrinsic binding constants (Kb) of the Cu compounds with CT-DNA, in the range (2.3-9.2) × 106, from CuHTSC to CuHHSB, were higher than other copper-based potential drugs, suggesting that π-π stacking interaction due to the presence of the aromatic rings favors the binding. Thiazole orange (TO) assays confirmed that ligands and Cu complexes displace TO from the DNA binding site, quenching the fluorescence emission. DFT calculations allow for an assessment of the equilibrium between [Cu(LnH2)(AcO)] and [Cu(LnH2)(H2O)]+, the tautomer that binds CuII, amido (am) and not imido (im), and the coordination mode of HTSC (O-, N, S), instead of (O-, N, NH2). The docking studies indicate that the intercalative is preferred over the minor groove binding to CT-DNA with the order [Cu(L1H2am)(AcO)] > [Cu(L2H2am)(AcO)] ≈ TO ≈ L1H3 > [Cu(L3H2am)(AcO)], in line with the experimental Kb constants, obtained from the UV-Vis spectroscopy. Moreover, dockings predict that the binding strength of [Cu(L1H2am)(AcO)] is larger than [Cu(L1H2am)(H2O)]+. Overall, the results suggest that when different enantiomers, tautomers, and donor sets are possible for a metal complex, a computational approach should be recommended to predict the type and strength of binding to DNA and, in general, to macromolecules.