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.

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.

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.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic.

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Leveraging nonstructural data to predict structures and affinities of protein-ligand complexes.

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands-i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target's three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand's pose-the 3D structure of the ligand bound to its target-that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

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.

Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-quality molecular expression with chemical intuition helps to promote many boundary problems of drug discovery. At present, molecular representation still faces several urgent problems, such as the polysemy of substructures and unsmooth information flow between atomic groups. In this research, we propose a deep contextualized Bi-LSTM architecture, Mol2Context-vec, which can integrate different levels of internal states to bring dynamic representations of molecular substructures. And the obtained molecular context representation can capture the interactions between any atomic groups, especially a pair of atomic groups that are topologically distant. Experiments show that Mol2Context-vec achieves state-of-the-art performance on multiple benchmark datasets. In addition, the visual interpretation of Mol2Context-vec is very close to the structural properties of chemical molecules as understood by humans. These advantages indicate that Mol2Context-vec can be used as a reliable and effective tool for molecular expression. Availability: The source code is available for download in https://github.com/lol88/Mol2Context-vec.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Molecular design in drug discovery: a comprehensive review of deep generative models.

Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets.

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

Semi-continuous Propagation of Influenza A Virus and Its Defective Interfering Particles: Analyzing the Dynamic Competition To Select Candidates for Antiviral Therapy.

Defective interfering particles (DIPs) of influenza A virus (IAV) are naturally occurring mutants that have an internal deletion in one of their eight viral RNA (vRNA) segments, rendering them propagation-incompetent. Upon coinfection with infectious standard virus (STV), DIPs interfere with STV replication through competitive inhibition. Thus, DIPs are proposed as potent antivirals for treatment of the influenza disease. To select corresponding candidates, we studied de novo generation of DIPs and propagation competition between different defective interfering (DI) vRNAs in an STV coinfection scenario in cell culture. A small-scale two-stage cultivation system that allows long-term semi-continuous propagation of IAV and its DIPs was used. Strong periodic oscillations in virus titers were observed due to the dynamic interaction of DIPs and STVs. Using next-generation sequencing, we detected a predominant formation and accumulation of DI vRNAs on the polymerase-encoding segments. Short DI vRNAs accumulated to higher fractions than longer ones, indicating a replication advantage, yet an optimum fragment length was observed. Some DI vRNAs showed breaking points in a specific part of their bundling signal (belonging to the packaging signal), suggesting its dispensability for DI vRNA propagation. Over a total cultivation time of 21 days, several individual DI vRNAs accumulated to high fractions, while others decreased. Using reverse genetics for IAV, purely clonal DIPs derived from highly replicating DI vRNAs were generated. We confirm that these DIPs exhibit a superior in vitro interfering efficacy compared to DIPs derived from lowly accumulated DI vRNAs and suggest promising candidates for efficacious antiviral treatment. IMPORTANCE Defective interfering particles (DIPs) emerge naturally during viral infection and typically show an internal deletion in the viral genome. Thus, DIPs are propagation-incompetent. Previous research suggests DIPs as potent antiviral compounds for many different virus families due to their ability to interfere with virus replication by competitive inhibition. For instance, the administration of influenza A virus (IAV) DIPs resulted in a rescue of mice from an otherwise lethal IAV dose. Moreover, no apparent toxic effects were observed when only DIPs were administered to mice and ferrets. IAV DIPs show antiviral activity against many different IAV strains, including pandemic and highly pathogenic avian strains, and even against nonhomologous viruses, such as SARS-CoV-2, by stimulation of innate immunity. Here, we used a cultivation/infection system, which exerted selection pressure toward accumulation of highly competitive IAV DIPs. These DIPs showed a superior interfering efficacy in vitro, and we suggest them for effective antiviral therapy.

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.

New approaches to the design of analgesic medicinal substances.

A gamma-pyrone derivative, comenic acid, activates the opioid-like receptor-mediated signaling pathway that modulates the NaV1.8 channels in the primary sensory neuron membrane. These channels are responsible for the generation of the nociceptive signal; therefore, gamma-pyrones have great therapeutic potential as analgesics, and this effect deserves a deeper understanding. The novelty of our approach to the design of a medicinal substance is based on a combination of the data obtained from living neurons using very sensitive physiological methods and the results of quantum chemical calculations. This approach allows the correlation of the molecular structure of gamma-pyrones with their ability to evoke a physiological response of the neuron. Comenic acid can bind to two calcium cations. One of them is chelated by the carbonyl and hydroxyl functional groups, while the other forms a salt bond with the carboxylate anion. Calcium-bound gamma-pyrones have fundamentally different electrostatic properties from free gamma-pyrone molecules. These two calcium ions are key elements involved in ligand-receptor binding. It is very likely that ion-ionic interactions between these cations and anionic functional groups of the opioid-like receptor activate the latter. The calculated intercationic distance of 9.5 Å is a structural criterion for effective ligand-receptor binding of calcium-bound gamma-pyrones.

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.

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.

Drugging the Undruggable: How Isoquinolines and PKA Initiated the Era of Designed Protein Kinase Inhibitor Therapeutics.

In 1984, Japanese researchers led by the biochemist Hiroyoshi Hidaka described the first synthetic protein kinase inhibitors based on an isoquinoline sulfonamide structure (Hidaka et al. Biochemistry, 1984 Oct 9; 23(21): 5036-41. doi: 10.1021/bi00316a032). These led to the first protein kinase inhibitor approved for medical use (fasudil), an inhibitor of the AGC subfamily Rho kinase. With potencies strong enough to compete against endogenous ATP, the isoquinoline compounds established the druggability of the ATP binding site. Crystal structures of their protein kinase complexes, including with cAMP-dependent protein kinase (PKA), showed interactions that, on the one hand, could mimic ATP but, on the other hand, could be optimized for high potency binding, kinase selectivity, and diversification away from adenosine. They also showed the flexibility of the glycine-rich loop, and PKA became a major prototype for crystallographic and nuclear magnetic resonance (NMR) studies of protein kinase mechanism and dynamic activity control. Since fasudil, more than 70 kinase inhibitors have been approved for clinical use, involving efforts that progressively have introduced new paradigms of data-driven drug discovery. Publicly available data alone comprise over 5000 protein kinase crystal structures and hundreds of thousands of binding data. Now, new methods, including artificial intelligence techniques and expansion of protein kinase targeting approaches, together with the expiration of patent protection for optimized inhibitor scaffolds, promise even greater advances in drug discovery. Looking back to the time of the first isoquinoline hinge binders brings the current state-of-the-art into stark contrast. Appropriately for this Perspective article, many of the milestone papers during this time were published in Biochemistry (now ACS Biochemistry).

A fragment-based approach to discovery of Receptor for Advanced Glycation End products inhibitors.

The Receptor for Advanced Glycation End products (RAGE) is a pattern recognition receptor that signals for inflammation via the NF-κB pathway. RAGE has been pursued as a potential target to suppress symptoms of diabetes and is of interest in a number of other diseases associated with chronic inflammation, such as inflammatory bowel disease and bronchopulmonary dysplasia. Screening and optimization have previously produced small molecules that inhibit the activity of RAGE in cell-based assays, but efforts to develop a therapeutically viable direct-binding RAGE inhibitor have yet to be successful. Here, we show that a fragment-based approach can be applied to discover fundamentally new types of RAGE inhibitors that specifically target the ligand-binding surface. A series of systematic assays of structural stability, solubility, and crystallization were performed to select constructs of the RAGE ligand-binding domain and optimize conditions for NMR-based screening and co-crystallization of RAGE with hit fragments. An NMR-based screen of a highly curated ~14 000-member fragment library produced 21 fragment leads. Of these, three were selected for elaboration based on structure-activity relationships generated through cycles of structural analysis by X-ray crystallography, structure-guided design principles, and synthetic chemistry. These results, combined with crystal structures of the first linked fragment compounds, demonstrate the applicability of the fragment-based approach to the discovery of RAGE inhibitors.

Designing bugs as drugs: exploiting the gut microbiome.

The extensive investigation of the human microbiome and the accumulating evidence regarding its critical relationship to human health and disease has advanced recognition of its potential as the next frontier of drug development. The rapid development of technologies, directed at understanding the compositional and functional dynamics of the human microbiome, and the ability to mine for novel therapeutic targets and biomarkers are leading innovative efforts to develop microbe-derived drugs that can prevent and treat autoimmune, metabolic, and infectious diseases. Increasingly, academics, biotechs, investors, and large pharmaceutical companies are partnering to collectively advance various therapeutic modalities ranging from live bacteria to small molecules. We review the leading platforms in current development focusing on live microbial consortia, engineered microbes, and microbial-derived metabolites. We will also touch on how the field is addressing and challenging the traditional definitions of pharmacokinetics and pharmacodynamics, dosing, toxicity, and safety to advance the development of these novel and cutting-edge therapeutics into the clinic.

Comparative Hepatic and Intestinal Efflux Transport of Statins.

Previous studies have shown that lipid-lowering statins are transported by various ATP-binding cassette (ABC) transporters. However, because of varying methods, it is difficult to compare the transport profiles of statins. Therefore, we investigated the transport of 10 statins or statin metabolites by six ABC transporters using human embryonic kidney cell-derived membrane vesicles. The transporter protein expression levels in the vesicles were quantified with liquid chromatography-tandem mass spectrometry and used to scale the measured clearances to tissue levels. In our study, apically expressed breast cancer resistance protein (BCRP) and P-glycoprotein (P-gp) transported atorvastatin, fluvastatin, pitavastatin, and rosuvastatin. Multidrug resistance-associated protein 3 (MRP3) transported atorvastatin, fluvastatin, pitavastatin, and, to a smaller extent, pravastatin. MRP4 transported fluvastatin and rosuvastatin. The scaled clearances suggest that BCRP contributes to 87%-91% and 84% of the total active efflux of rosuvastatin in the small intestine and the liver, respectively. For atorvastatin, the corresponding values for P-gp-mediated efflux were 43%-79% and 66%, respectively. MRP3, on the other hand, may contribute to 23%-26% and 25%-37% of total active efflux of atorvastatin, fluvastatin, and pitavastatin in jejunal enterocytes and liver hepatocytes, respectively. These data indicate that BCRP may play an important role in limiting the intestinal absorption and facilitating the biliary excretion of rosuvastatin and that P-gp may restrict the intestinal absorption and mediate the biliary excretion of atorvastatin. Moreover, the basolateral MRP3 may enhance the intestinal absorption and sinusoidal hepatic efflux of several statins. Taken together, the data show that statins differ considerably in their efflux transport profiles. SIGNIFICANCE STATEMENT: This study characterized and compared the transport of atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin acid and four atorvastatin metabolites by six ABC transporters (BCRP, MRP2, MRP3, MRP4, MRP8, P-gp). Based on in vitro findings and protein abundance data, the study concludes that BCRP, MRP3, and P-gp have a major impact in the efflux of various statins. Together with in vitro metabolism, uptake transport, and clinical data, our findings are applicable for use in comparative systems pharmacology modeling of statins.