De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks.

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature.

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.

Computationally restoring the potency of a clinical antibody against Omicron.

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3 and revealed how quickly viral escape can curtail effective options4,5. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab4-6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.

Novel resveratrol smart lipids; design, formulation, and biological evaluation of anticancer activity.

PURPOSE: Although resveratrol (RES) is an efficacious molecule, its therapeutic activity is impeded by significant limitations, such as rapid oral absorption, poor oral bioavailability, and low water solubility. Therefore, the preparation of RES in different pharmaceutical carriers represents an important tool to enhance its therapeutic applications. This study aims to potentiate the anti-cancer activity of RES by formulating it into a novel nanocarrier called Smart Lipid. METHODS: RES-loaded Smart Lipids were prepared by high-shear hot homogenization method utilizing a 21 × 32 factorial design with three factors at different levels: the total lipid concentration, the concentration of surfactant, and the type of surfactant. The responses were evaluated based on entrapment efficiency percentages and particle size. RESULTS: Our novel optimized RES-loaded Smart Lipid formula showed small particle size (288.63 ± 5.55 nm), good zeta potential (-16.44 ± 0.99 mV), and an entrapment efficiency of 86.346 ± 3.61% with spherical, clearly distinct, and no signs of fusion by transmission electron microscopy. Further characterization was done using differential scanning calorimetry, which showed no interaction between the drug and other components as the optimum lyophilized formula showed a peak at 54.75°C, which represents the lipid mixture, with an undetectable characteristic peak of the drug, which indicates entrapment of the drug, and the structure of the compounds was confirmed by Fourier transform-infrared spectroscopy, in which the majority of the drug's characteristic peaks disappeared when loaded into Smart Lipid, which may indicate Smart Lipid's ability to reduce the stretching and bending between bonds in RES. In addition, the optimized formula showed a sustained release pattern compared to RES suspension. Finally, the cytotoxic activity of the optimized RES-loaded Smart Lipid on different cell lines (human breast adenocarcinoma (MCF7), human hepatocellular carcinoma (HepG2), and human colon cancer cells (HT29)) was assessed through MTT assay (7-fold reduction in the IC50, from 3.7 ± 0.5 µM for free RES to 0.5 ± 0.033 µM for Smart Lipid loaded formula against MCF7, 3-fold reduction in the IC50 against HepG2 cells, from 10.01 ± 0.35 to 3.16 ± 0.21 µMm, and a more than 10-fold reduction in the IC50 from more than 100 to 10 ± 0.57 µM against HT-29 cells) and its effect on cell cycle progression and apoptosis induction were assessed using flow cytometry and annexin V kit, respectively. Our results showed that RES-loaded Smart Lipid significantly reduced cell viability, induced cell cycle arrest at G0/G1 phase, and apoptosis compared to free formula and free RES suspension. CONCLUSION: Loading RES into this novel kind of nanocarrier enhanced RES absorption, cellular accumulation, and improved its anticancer properties.

Porin-independent accumulation in Pseudomonas enables antibiotic discovery.

Gram-negative antibiotic development has been hindered by a poor understanding of the types of compounds that can accumulate within these bacteria1,2. The presence of efflux pumps and substrate-specific outer-membrane porins in Pseudomonas aeruginosa renders this pathogen particularly challenging3. As a result, there are few antibiotic options for P. aeruginosa infections4 and its many porins have made the prospect of discovering general accumulation guidelines seem unlikely5. Here we assess the whole-cell accumulation of 345 diverse compounds in P. aeruginosa and Escherichia coli. Although certain positively charged compounds permeate both bacterial species, P. aeruginosa is more restrictive compared to E. coli. Computational analysis identified distinct physicochemical properties of small molecules that specifically correlate with P. aeruginosa accumulation, such as formal charge, positive polar surface area and hydrogen bond donor surface area. Mode of uptake studies revealed that most small molecules permeate P. aeruginosa using a porin-independent pathway, thus enabling discovery of general P. aeruginosa accumulation trends with important implications for future antibiotic development. Retrospective antibiotic examples confirmed these trends and these discoveries were then applied to expand the spectrum of activity of a gram-positive-only antibiotic, fusidic acid, into a version that demonstrates a dramatic improvement in antibacterial activity against P. aeruginosa. We anticipate that these discoveries will facilitate the design and development of high-permeating antipseudomonals.

Expanding the Paradigm of Structure-Based Drug Design: Molecular Dynamics Simulations Support the Development of New Pyridine-Based Protein Kinase C-Targeted Agonists.

Protein kinase C (PKC) modulators hold therapeutic potential for various diseases, including cancer, heart failure, and Alzheimer's disease. Targeting the C1 domain of PKC represents a promising strategy; the available protein structures warrant the design of PKC-targeted ligands via a structure-based approach. However, the PKC C1 domain penetrates the lipid membrane during binding, complicating the design of drug candidates. The standard docking-scoring approach for PKC lacks information regarding the dynamics and the membrane environment. Molecular dynamics (MD) simulations with PKC, ligands, and membranes have been used to address these shortcomings. Previously, we observed that less computationally intensive simulations of just ligand-membrane interactions may help elucidate C1 domain-binding prospects. Here, we present the design, synthesis, and biological evaluation of new pyridine-based PKC agonists implementing an enhanced workflow with ligand-membrane MD simulations. This workflow holds promise to expand the approach in drug design for ligands targeted to weakly membrane-associated proteins.

Matched Molecular Pair Analysis in Drug Discovery: Methods and Recent Applications.

Matched molecular pair analysis (MMPA) is a tool to extract knowledge of medicinal chemistry from existing experimental data, and it relates changes in activities or properties to specific structural changes. More recently, MMPA has also been applied in multi-objective optimization and de novo drug design. Here, we discuss the concept, techniques, and case studies of MMPA, which provide an overview of the current development in the field of MMPA. This Perspective also summarizes up-to-date MMPA applications and highlights the successes and opportunities for further MMPA advances.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design.

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Application of kojic acid scaffold in the design of non-tyrosinase enzyme inhibitors.

Kojic acid (KA) is a hydroxypyranone natural metabolite mainly known as tyrosinase inhibitor. Currently, this compound is used as a whitening agent in cosmetics and as an anti-browning agent in food industry. Given the easy-manipulation in different positions of the KA molecule, many investigations have been carried out to find new tyrosinase inhibitors derived from KA. Beside anti-tyrosinase activity, many KA-based compounds have been designed for targeting other enzymes including human neutrophil elastase, catechol-O-methyltransferase, matrix metalloproteinases, monoamine oxidase, human lactate dehydrogenase, endonucleases, D-amino acid oxidase, and receptors such as histamine H3 and apelin (APJ) receptors. This review could help biochemists and medicinal chemists in designing diverse KA-derived enzyme inhibitors.

Designed protease-based signaling networks.

Regulated proteolysis is a pivotal regulatory mechanism in all living organisms from bacteria to mammalian cells and viruses. The ability to design proteases to sense, transmit, or trigger a signal opens up the possibility of construction of sophisticated proteolysis-regulated signaling networks. Cleavage of the polypeptide chain can either activate or inactivate the selected protein or process, often with a fast response. Most designs are based on sequence-selective proteases that can be implemented for transcriptional, translational, and ultimately post-translational control, aiming to engineer complex circuits that can dynamically control cellular functions and enable novel biotechnological and biomedical applications.

Massively parallel, computationally guided design of a proenzyme.

Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to redesign any chosen enzyme to be similarly stimulus responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for proenzyme design. For a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effectively designed variants shows that they are inhibited by ∼80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98% but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing proenzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the prodomain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of proproteins with precise spatial regulation.

Software-designed miniproteins could create new class of drugs.

Small versions of antibodies bind to virtually any target protein.

Application of the All-Hydrocarbon Stapling Technique in the Design of Membrane-Active Peptides.

The threats of drug resistance and new emerging pathogens have led to an urgent need to develop alternative treatment therapies. Recently, considerable research efforts have focused on membrane-active peptides (MAPs), a category of peptides in drug discovery with antimicrobial, anticancer, and cell penetration activities that have demonstrated their potential to be multifunctional agents. Nonetheless, natural MAPs have encountered various disadvantages, which mainly include poor bioavailability, the lack of a secondary structure in short peptides, and high production costs for long peptide sequences. Hence, an "all-hydrocarbon stapling system" has been applied to these peptides and proven to effectively stabilize the helical conformations, improving proteolytic resistance and increasing both the potency and the cell permeability. In this review, we summarized and categorized the advances made using this powerful technique in the development of stapled MAPs. Furthermore, outstanding issues and suggestions for future design within each subcategory were thoroughly discussed.

[Design Strategy of Biologically Active Compounds Using Various Elements].

Almost all conventional drug discovery research has been based on hydrocarbon-based frameworks and common chemical elements such as nitrogen, oxygen, sulfur, and the halogens. However, triggered by the approval of bortezomib, a boronic acid-containing pharmaceutical agent, the incorporation of functionalities that are not native in biological systems has been intensively investigated. Several other boron-containing pharmaceuticals have also been marketed. Therefore, the inclusion of various elements is one of the most promising strategies for the development of novel and distinctive drug candidates. In this symposium review, the author focused on the 'elements chemistry' approaches for the structural development of biologically active compounds, particularly those involving silicon and phosphorus. The isosteric exchange of Si and C (Si/C-exchange) is one of the most-investigated forms of substituting elements. We revealed the detailed physicochemical impact of Si/C-exchange, and we proposed several applications of silyl functionalities other than the simple Si/C-exchange. Regarding phosphorus, we recently revealed that the P-B substructure can function as the isostere of C-C or Si-C substructures. In addition to these isosteric exchanges, the development of biologically active compounds bearing unique substructures such as carboranes, hydrophobic boron clusters, and ferrocene is introduced. These novel strategies provide several options for structural development, offering great potential for expanding the chemical space of medicinal chemistry.

Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents.

Serotonin transporter (SERT) is a membrane transporter which terminates neurotransmission of serotonin through its reuptake. This transporter as well as its substrate have long drawn attention as a key mediator and drug target in a variety of diseases including mental disorders. Accordingly, its structural basis has been studied by X-ray crystallography to gain insights into a design of ligand with high affinity and high specificity over closely related transporters. Recent progress in structural biology including single particle cryo-EM have made big strides also in determination of the structures of human SERT in complex with its ligands. Moreover, rapid progress in machine learning such as deep learning accelerates computer-assisted drug design. Here, we would like to summarize recent progresses in our understanding of SERT using these two rapidly growing technologies, limitations, and future perspectives.

Design, synthesis and biological activities of echinopsine derivatives containing acylhydrazone moiety.

Based on the broad-spectrum biological activities of echinopsine and acylhydrazones, a series of echinopsine derivatives containing acylhydrazone moieties have been designed, synthesized and their biological activities were evaluated for the first time. The bioassay results indicated that most of the compounds showed moderate to good antiviral activities against tobacco mosaic virus (TMV), among which echinopsine (I) (inactivation activity, 49.5 ± 4.4%; curative activity, 46.1 ± 1.5%; protection activity, 42.6 ± 2.3%) and its derivatives 1 (inactivation activity, 44.9 ± 4.6%; curative activity, 39.8 ± 2.6%; protection activity, 47.3 ± 4.3%), 3 (inactivation activity, 47.9 ± 0.9%; curative activity, 43.7 ± 3.1%; protection activity, 44.6 ± 3.3%), 7 (inactivation activity, 46.2 ± 1.6%; curative activity, 45.0 ± 3.7%; protection activity, 41.7 ± 0.9%) showed higher anti-TMV activity in vivo at 500 mg/L than commercial ribavirin (inactivation activity, 38.9 ± 1.4%; curative activity, 39.2 ± 1.8%; protection activity, 36.4 ± 3.4%). Some compounds exhibited insecticidal activities against Plutella xylostella, Mythimna separate and Spodoptera frugiperda. Especially, compounds 7 and 27 displayed excellent insecticidal activities against Plutella xylostell (mortality 67 ± 6% and 53 ± 6%) even at 0.1 mg/L. Additionally, most echinopsine derivatives exhibited high fungicidal activities against Physalospora piricola and Sclerotinia sclerotiorum.

Design and Activity of Novel Oxadiazole Based Compounds That Target Poly(ADP-ribose) Polymerase.

Novel PARP inhibitors with selective mode-of-action have been approved for clinical use. Herein, oxadiazole based ligands that are predicted to target PARP-1 have been synthesized and screened for the loss of cell viability in mammary carcinoma cells, wherein seven compounds were observed to possess significant IC50 values in the range of 1.4 to 25 µM. Furthermore, compound 5u, inhibited the viability of MCF-7 cells with an IC50 value of 1.4µM, when compared to Olaparib (IC50 = 3.2 µM). Compound 5s also decreased cell viability in MCF-7 and MDA-MB-231 cells with IC50 values of 15.3 and 19.2 µM, respectively. Treatment of MCF-7 cells with compounds 5u and 5s produced PARP cleavage, H2AX phosphorylation and CASPASE-3 activation comparable to that observed with Olaparib. Compounds 5u and 5s also decreased foci-formation and 3D Matrigel growth of MCF-7 cells equivalent to or greater than that observed with Olaparib. Finally, in silico analysis demonstrated binding of compound 5s towardsthe catalytic site of PARP-1, indicating that these novel oxadiazoles synthesized herein may serve as exemplars for the development of new therapeutics in cancer.

Newly Emerging Strategies in Antiviral Drug Discovery: Dedicated to Prof. Dr. Erik De Clercq on Occasion of His 80th Anniversary.

Viral infections pose a persistent threat to human health. The relentless epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health problem, with millions of infections and fatalities so far. Traditional approaches such as random screening and optimization of lead compounds by organic synthesis have become extremely resource- and time-consuming. Various modern innovative methods or integrated paradigms are now being applied to drug discovery for significant resistance in order to simplify the drug process. This review provides an overview of newly emerging antiviral strategies, including proteolysis targeting chimera (PROTAC), ribonuclease targeting chimera (RIBOTAC), targeted covalent inhibitors, topology-matching design and antiviral drug delivery system. This article is dedicated to Prof. Dr. Erik De Clercq, an internationally renowned expert in the antiviral drug research field, on the occasion of his 80th anniversary.

Computation-guided asymmetric total syntheses of resveratrol dimers.

Although computational simulation-based natural product syntheses are in their initial stages of development, this concept can potentially become an indispensable resource in the field of organic synthesis. Herein we report the asymmetric total syntheses of several resveratrol dimers based on a comprehensive computational simulation of their biosynthetic pathways. Density functional theory (DFT) calculations suggested inconsistencies in the biosynthesis of vaticahainol A and B that predicted the requirement of structural corrections of these natural products. According to the computational predictions, total syntheses were examined and the correct structures of vaticahainol A and B were confirmed. The established synthetic route was applied to the asymmetric total synthesis of (-)-malibatol A, (-)-vaticahainol B, (+)-vaticahainol A, (+)-vaticahainol C, and (-)-albiraminol B, which provided new insight into the biosynthetic pathway of resveratrol dimers. This study demonstrated that computation-guided organic synthesis can be a powerful strategy to advance the chemical research of natural products.