Relevant Protein Targets Research Articles

Accounting for Solvation Correlation Effects on the Thermodynamics of Water Networks in Protein Cavities

Macromolecular recognition and ligand binding are at the core of biological function and drug discovery efforts. Water molecules play a significant role in mediating the protein-ligand interaction, acting as more than just the surrounding medium by affecting the thermodynamics and thus the outcome of the binding process. As individual water contributions are impossible to measure experimentally, a range of computational methods have emerged to identify hydration sites in protein pockets and characterize their energetic contributions for drug discovery applications. Even though several methods model solvation effects explicitly, they focus on determining the stability of specific water sites independently and neglect solvation correlation effects upon replacement of clusters of water molecules, which typically happens in hit-to-lead optimization. In this work, we rigorously determine the conjoint effects of replacing all combinations of water molecules in protein binding pockets through the use of the RE-EDS multistate free-energy method, which combines Hamiltonian replica exchange (RE) and enveloping distribution sampling (EDS). Applications on the small bovine pancreatic trypsin inhibitor and four proteins of the bromodomain family illustrate the extent of solvation correlation effects on water thermodynamics, with the favorability of replacement of the water sites by pharmacophore probes highly dependent on the composition of the water network and the pocket environment. Given the ubiquity of water networks in biologically relevant protein targets, we believe our approach can be helpful for computer-aided drug discovery by providing a pocket-specific and a priori systematic consideration of solvation effects on ligand binding and selectivity.

Role of microRNA in hydroxyurea mediated HbF induction in sickle cell anaemia patients

Hydroxyurea (HU) is found to be beneficial in sickle cell anaemia (SCA) patients, due to its ability to increase foetal haemoglobin (HbF), however, patients show a variable response. Differences in HbF levels are attributed to many factors; but, the role of miRNA in HbF regulation is sparsely investigated. In this study, we evaluated the effect of miRNA expression on HbF induction in relation to hydroxyurea therapy in 30 normal controls, 30 SCA patients at baseline, 20 patients after 3 and 6 months of hydroxyurea (HU) therapy. HbF levels were measured by HPLC. Total RNA and miRNA were extracted from CD71+ erythroid cells and the expression was determined using Taqman probes. The mean HbF level increased 7.54 ± 2.44 fold, after 3 months of HU therapy. After the HU therapy 8 miRNAs were significantly up-regulated while 2 were down-regulated. The increase in miR-210, miR16-1, and miR-29a expression and decrease in miR-96 expression were strongly associated with the HU mediated HbF induction. Post HU therapy, decreased miR-96 expression negatively correlate with HbF and γ-globin gene while increased expression of miR-210, miR-16-1 and miR-29a post HU therapy positively corelate with HbF and γ-globin gene. Thus, suggest that miR-210, miR-16-1 and miR-29a are positive regulator of γ-globin gene and miR-96 is negative regulator of γ-globin gene. The study suggests the role of miR-210, miR16-1, miR-29a, and miR-96 in γ-globin gene regulation leading to HbF induction. Identification of the relevant protein targets might be useful for understanding the HU mediated HbF induction.

Ginsenoside Rd protects transgenic Caenorhabditis elegans from β-amyloid toxicity by activating oxidative resistant.

Alzheimer's disease (AD) is a serious public health issue but few drugs are currently available for the disease, and these only target the symptoms. It is well established that oxidative stress plays a crucial role in AD, and there is compelling evidence linking oxidative stress to β-amyloid (Aβ). An exciting source of potential new AD therapeutic medication possibilities is medicinal plants. Ginsenoside Rd (GS-Rd) is one of the main bioactive substances in ginseng extracts. In our study, we used a network pharmacology analysis to identify overlapping GS-Rd (therapeutic) and AD (disease)-relevant protein targets, gene ontology (GO) and bio-process annotation, and the KEGG pathway analysis data predicted that GS-Rd impacts multiple targets and pathways, such as the MAPK signal pathway and the JAT-STAT3 signaling pathway. We then assessed the role of GS-Rd in C. elegans and found that GS-Rd prolongs lifespan, improves resistance to heat stress, delays physical paralysis and increases oxidative stress responses. Overall, these results suggest that GS-Rd protects against the toxicity of Aβ. The RNA-seq analysis revealed that GS-Rd achieves its effects by regulating gene expressions like daf-16 and skn-1, as well as by participating in many AD-related pathways like the MAPK signaling pathway. In addition, in CL4176 worms, GS-Rd decreased reactive oxygen species (ROS) levels and increased SOD activity. Additional research with transgenic worms showed that GS-Rd aided in the movement of DAF-16 from the cytoplasm to the nucleus. Taken together, the results indicate that GS-Rd significantly reduces Aβ aggregation by targeting the MAPK signal pathway, induces nuclear translocation of DAF-16 to activate downstream signaling pathways and increases resistance to oxidative stress in C. elegans to protect against Aβ-induced toxicity.

Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence.

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Identifying and Targeting Prediction of the PI3K-AKT Signaling Pathway in Drug-Induced Thrombocytopenia in Infected Patients Receiving Linezolid Therapy: A Network Pharmacology-Based Analysis.

The pharmacological mechanisms underlying the adverse effects of linezolid on thrombocytopenia have not been conclusively determined. This network pharmacology study aimed at investigating the potential pharmacological mechanisms of linezolid-induced adverse reactions in thrombocytopenia. In this study, target genes for linezolid and thrombocytopenia were compared and analyzed. Overlapping thrombocytopenia-associated targets and predicted targets of linezolid were imported to establish protein-protein interaction networks. Gene Ontology and the Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were performed to determine the enriched biological terms and pathways. The mechanisms involved in linezolid-induced thrombocytopenia were established to be associated with various biological processes, including T cell activation, peptidyl serine modification, and peptidyl serine phosphorylation. The top five relevant protein targets were obtained, including ALB, AKT1, EGFR, IL6, and MTOR. Enrichment analysis showed that the targets of linezolid were positively correlated with T cell activation responses. The mechanism of action of linezolid was positively correlated with the PI3K-AKT signaling pathway and negatively correlated with the Ras signaling pathway. We identified the important protein targets and signaling pathways involved in linezolid-induced thrombocytopenia in anti-infection therapy, providing new information for subsequent studies on the pathogenesis of drug-induced thrombocytopenia and potential therapeutic strategies for rational use of linezolid in clinical settings.

Identifying medically relevant xenon protein targets by in silico screening of the structural proteome.

In a previous study, in silico screening of the binding of almost all proteins in the Protein Data Bank to each of the five noble gases xenon, krypton, argon, neon, and helium was reported. This massive and rich data set requires analysis to identify the gas-protein interactions that have the best binding strengths, those where the binding of the noble gas occurs at a site that can modulate the function of the protein, and where this modulation might generate clinically relevant effects. Here, we report a preliminary analysis of this data set using a rational, heuristic score based on binding strength and location. We report a partial prioritized list of xenon protein targets and describe how these data can be analyzed, using arginase and carbonic anhydrase as examples. Our aim is to make the scientific community aware of this massive, rich data set and how it can be analyzed to accelerate future discoveries of xenon-induced biological activity and, ultimately, the development of new “atomic” drugs.

Photolability of NO in ruthenium nitrosyls with pentadentate ligand induces exceptional cytotoxicity towards VCaP, 22Rv1 and A549 cancer cells under therapeutic condition

Pentadentate electron rich MePBITA ligand in [RuII(MePBITA)(NO)]n+ (n = 3, 2 and MePBITA = 1-(6-(1-methyl-1H-benzo[d]imidazol-2-yl)pyridin-2-yl)-N,N-bis(pyridin-2-ylmethyl)methanamine) permits the isolation of both the redox states of nitrosyls with Enemark–Feltham notation {RuNO}6 and {RuNO}7. The nitrosyl derivative [RuII(MePBITA)(NO)](ClO4)3: [4](ClO4)3 was synthesized by stepwise synthetic manner from the chloro precursor [RuII(MePBITA)(Cl)](PF6): [1](PF6), via the acetonitrile derivative [RuII(MePBITA)(CH3CN)](PF6)2: [2](PF6)2 followed by nitro complex [RuII(MePBITA)(NO2)](PF6): [3](PF6). All the complexes were fully characterized by different analytical and spectroscopic techniques. Single crystal X-ray structures of the complexes [1](PF6), [2](PF6)2, [3](PF6), and [4](ClO4)3 were profitably determined for understanding the molecular integrity. Ru−NO stretching frequency observed at 1931 cm−1 for [4](ClO4)3 suggests a moderately electrophilic character of NO. The huge shift in νNO frequency, Δν (solid) = 325 cm−1 was observed by reducing [4](ClO4)3 to [4](ClO4)2. The conversion of [3]+ from [4]3+ was examined both electrochemically and spectrophotometrically with the addition of 0.5 M NaOH solution. Rate constants of the first order photorelease (kNO) have been found to be 8.99 × 10−3 min−1; half-life (t1/2) = 77 min and 3.84 × 10−2 min−1; half-life (t1/2) = 18 min for [4]3+ and [4]2+, respectively with visible Xenon light (200 W) source. The photo liberated NO has been scavenged by biologically relevant target protein reduced myoglobin as Mb−NO adduct. Photoactivation of [4]3+ and [4]2+ by visible light induces significant cytotoxicity in prostate cancer cell lines; VCaP (IC50 29.74 and 4.42 µM) and 22Rv1 (IC50 29.96 and 6.88 µM), and lung cancer cell line; A549 (IC50 2.24 and 0.12 µM). Collectively our results pave the way for the development of metallodrugs as potential therapeutics for a variety of cancers. Additionally, our results also demonstrate how ligand modification could enhance the photolability of metal nitrosyl, adding a new dimension for future efficient photoactive metal nitrosyl design.

Contribution of MicroRNAs in Chemoresistance to Cisplatin in the Top Five Deadliest Cancer: An Updated Review.

Cisplatin (DDP) is a well-known anticancer drug used for the treatment of numerous human cancers in solid organs, including bladder, breast, cervical, head and neck squamous cell, ovarian, among others. Its most important mode of action is the DNA-platinum adducts formation, inducing DNA damage response, silencing or activating several genes to induce apoptosis; these mechanisms result in genetics and epigenetics modifications. The ability of DDP to induce tumor cell death is often challenged by the presence of anti-apoptotic regulators, leading to chemoresistance, wherein many patients who have or will develop DDP-resistance. Cancer cells resist the apoptotic effect of chemotherapy, being a problem that severely restricts the successful results of treatment for many human cancers. In the last 30 years, researchers have discovered there are several types of RNAs, and among the most important are non-coding RNAs (ncRNAs), a class of RNAs that are not involved in protein production, but they are implicated in gene expression regulation, and representing the 98% of the human genome non-translated. Some ncRNAs of great interest are long ncRNAs, circular RNAs, and microRNAs (miRs). Accumulating studies reveal that aberrant miRs expression can affect the development of chemotherapy drug resistance, by modulating the expression of relevant target proteins. Thus, identifying molecular mechanisms underlying chemoresistance development is fundamental for setting strategies to improve the prognosis of patients with different types of cancer. Therefore, this review aimed to identify and summarize miRs that modulate chemoresistance in DDP-resistant in the top five deadliest cancer, both in vitro and in vivo human models.

Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design.

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Drug delivery system based on PVA and clay for potential treatment of COVID-19

Tremendous efforts are being made around the world to develop efficient ways to prevent/treat the novel β-coronavirus disease (COVID-19) caused by SARS-CoV-2. There are currently many studies and clinical trials (either based on computational predictions or clinical experiences), in progress, focusing on finding relevant protein targets and medications anti-COVID-19. In the present study, we report a hydrogel drug delivery system based on Laponite® RD (Lap) entrapped in a poly(vinyl alcohol) (PVA) matrix obtained by freezing/thawing method. The PVA/Lap hydrogels were characterized in terms of their morphological, rheological, and swelling properties. Moreover, in vitro drug delivery investigations were performed with a promising drug, Rifampicin (Rif). Rif is an antitubercular drug and was selected for this study in order to demonstrate its efficacy as an anti-COVID-19 repurposed drug. In vitro studies were supplemented by in silico molecular docking simulations of Rif capacity of inhibition against SARS-CoV-2 target protein 3-chymotrypsin-like protease (3CLpro). The results obtained in this study are encouraging and we propose the Rif loaded PVA/Lap hydrogels as promising drug delivery systems for COVID-19 treatment, promoting a synergistic therapeutic effect by dual targeting of viral 3CLpro and S proteins.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10965-022-02927-5.

MiR-133a targets YES1 to reduce cisplatin resistance in ovarian cancer by regulating cell autophagy

BackgroundAccumulating evidence has revealed that aberrant microRNA (miRNA) expression can affect the development of chemotherapy drug resistance by modulating the expression of relevant target proteins. Emerging evidence has demonstrated that miR-133a participates in the tumorigenesis of various cancers. However, whether miR-133a is associated with cisplatin resistance in ovarian cancer remains unclear.ObjectiveTo investigate the role of miR-133a in the development of cisplatin resistance in ovarian cancer.MethodsMiR-133a expression in cisplatin-resistant ovarian cancer cell lines was assessed by reverse-transcription quantitative PCR (RT–qPCR). A cell counting kit-8 (CCK-8) assay was used to evaluate the viability of tumour cells treated with cisplatin in the presence or absence of miR-133a. A luciferase reporter assay was used to analyse the binding of miR-133a with the 3′ untranslated region (3′UTR) of YES proto-oncogene 1 (YES1). The YES1 expression level was analysed using a dataset from the International Cancer Genome Consortium (ICGC) and assessed by RT–qPCR and western blotting in vitro. The roles and mechanisms of YES1 in cell functions were further probed via gain- and loss-of-function analysis.ResultsThe expression of miR-133a was significantly decreased in cisplatin-resistant ovarian cancer cell lines (A2780-DDP and SKOV3-DDP), and the overexpression of the miR-133a mimic reduced cisplatin resistance in A2780-DDP and SKOV3-DDP cells. Treatment with the miR-133a inhibitor increased cisplatin sensitivity in normal A2780 and SKOV3 cells. MiR-133a binds the 3’UTR of YES1 and downregulates its expression. Bioinformatics analysis revealed that YES1 expression was upregulated in recurrent cisplatin-resistant ovarian cancer tissue, and in vitro experiments also verified its upregulation in cisplatin-resistant cell lines. Furthermore, we discovered that miR-133a downregulated the expression of YES1 and thus inhibited cell autophagy to reduce cisplatin resistance. Yes1 knockdown significantly suppressed the cisplatin resistance of ovarian cancer cells by inhibiting autophagy in vitro. Xenograft tumour implantation further demonstrated that Yes1 overexpression promoted ovarian tumour development and cisplatin resistance.ConclusionsOur results suggest that the miR-133a/YES1 axis plays a critical role in cisplatin resistance in human ovarian cancer by regulating cell autophagy, which might serve as a promising therapeutic target for ovarian cancer chemotherapy treatment in the future.

Insect RDL Receptor Models for Virtual Screening: Impact of the Template Conformational State in Pentameric Ligand-Gated Ion Channels.

The RDL receptor is one of the most relevant protein targets for insecticide molecules. It belongs to the pentameric ligand-gated ion channel (pLGIC) family. Given that the experimental structures of pLGICs are difficult to obtain, homology modeling has been extensively used for these proteins, particularly for the RDL receptor. However, no detailed assessments of the usefulness of homology models for virtual screening (VS) have been carried out for pLGICs. The aim of this study was to evaluate which are the determinant factors for a good VS performance using RDL homology models, specially analyzing the impact of the template conformational state. Fifteen RDL homology models were obtained based on different pLGIC templates representing the closed, open, and desensitized states. A retrospective VS process was performed on each model, and their performance in the prioritization of active ligands was assessed. In addition, the three best-performing models among each of the conformations were subjected to molecular dynamics simulations (MDS) in complex with a representative active ligand. The models showed variations in their VS performance parameters that were related to the structural properties of the binding site. VS performance tended to improve in more constricted binding cavities. The best performance was obtained with a model based on a template in the closed conformation. MDS confirmed that the closed model was the one that best represented the interactions with an active ligand. These results imply that different templates should be evaluated and the structural variations between their channel conformational states should be specially examined, providing guidelines for the application of homology modeling for VS in other proteins of the pLGIC family.

Trends of antimalarial marine natural products: progresses, challenges and opportunities.

Covering 1972 to 2021Malaria remains a significant public health problem in some regions of the world. The great efforts to control malaria have been severely compromised due to the widespread resistance of Plasmodium falciparum to nearly all frontline drugs. Pursuit of novel molecules from the sea will potentially result in new interventions against malaria, which are urgently needed to combat the increase of resistance. Focusing on the strategy of the "Blue Drug Bank", the molecules highlighted here can serve as an inspiration for future medicinal chemistry campaigns. This review covers the developments in the field of antimalarial marine lead compounds reported between 1972 and July 2021, and offers a comprehensive overview on their progresses and potentials. We selected 60 representative potential candidate molecules from 361 marine natural products, and highlighted their structure-activity relationships, molecular mechanisms of targets, and drug-like properties in order to assess their full potential to be developed. We summarized 107 clinically proven or potential antimalarial targets and their subcellular locations in the relevant target proteins, which linked the molecules to the target proteins at the subcellular level. Hence, it could be expected that natural products targeting different mechanisms may prove to be an effective strategy in antimalarial drug research and development in the future.

Macrocyclic DNA-encoded chemical libraries: a historical perspective.

While macrocyclic peptides are extensively researched for therapeutically relevant protein targets, DNA-encoded chemical libraries (DELs) are developed at a quick pace to discover novel small molecule binders. The combination of both fields has been explored since 2004 and the number of macrocyclic peptide DELs is steadily increasing. Macrocycles with high affinity and potency were identified for diverse classes of proteins, revealing DEL's huge potential. By giving a historical perspective, we would like to review the methods which permitted the rise of macrocyclic peptide DELs, describe the different DELs which were created and discuss the achievements and challenges of this emerging field.

Computational drug discovery under RNA times.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

Spectral, Anti-Inflammatory, Anti-Pyretic, Leishmanicidal, and Molecular Docking Studies, Against Selected Protein Targets, of a New Bisbenzylisoquinoline Alkaloid.

A new bisbenzylisoquinoline named as chondrofolinol (1) and four reported compounds (2–5) were isolated and characterized from the roots of Berberis glaucocarpa Stapf. Anti-inflammatory, anti-pyretic, and leishmanicidal studies were performed against carrageenan-induced paw edema, yeast-induced pyrexia, and the promastigotes of Leishmania tropica, respectively. The new compound significantly reduced the paw volume in carrageenan-induced paw edema and rectal temperature in yeast-induced pyrexia at 10 and 20 mg/ kg of body weight. Chondrofolinol caused almost 100% inhibition of the promastigotes of Leishmania tropica. All the compounds displayed minimal cytotoxicity against THP-1 monocytic cells. In order to ascertain the potential macromolecular targets of chondrofolinol responsible for the observed anti-inflammatory and anti-leishmanial activities, a molecular docking study was carried out on relevant protein targets of inflammation and Leishmania. Protein targets of human endoplasmic reticulum aminopeptidase 2 (ERAP2) and human matrix metalloproteinase-1 (MMP-1) for inflammation and protein targets of N-myristoyltransferase (NMT), tyrosyl-tRNA synthetase (TyrRS), and uridine diphosphate-glucose pyrophosphorylase (UGPase) for Leishmania major were selected after thorough literature search about protein targets responsible for inflammation and Leishmania major. Chondrofolinol showed excellent docking to ERAP2 and to MMP-1. The Leishmania major protein targets with the most favorable docking scores to chondrofolinol were NMT, TyrRS, and UGPase. The study indicated that bisbenzylisoquinoline and isoquinoline alkaloids possess anti-pyretic, anti-inflammatory, and anti-leishmanial properties with minimal cytotoxicity and therefore, need to be further explored for their therapeutic potential.

Multiplexed Screening of Thousands of Natural Products for Protein-Ligand Binding in Native Mass Spectrometry.

The structural diversity of natural products offers unique opportunities for drug discovery, but challenges associated with their isolation and screening can hinder the identification of drug-like molecules from complex natural product extracts. Here we introduce a mass spectrometry-based approach that integrates untargeted metabolomics with multistage, high-resolution native mass spectrometry to rapidly identify natural products that bind to therapeutically relevant protein targets. By directly screening crude natural product extracts containing thousands of drug-like small molecules using a single, rapid measurement, we could identify novel natural product ligands of human drug targets without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

DockECR: Open consensus docking and ranking protocol for virtual screening of small molecules

The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR.

Targeted Redesign of Suramin Analogs for Novel Antimicrobial Lead Development.

The emergence of new viral infections and drug-resistant bacteria urgently necessitates expedient therapeutic development. Repurposing and redesign of existing drugs against different targets are one potential way in which to accelerate this process. Suramin was initially developed as a successful antiparasitic drug but has also shown promising antiviral and antibacterial activities. However, due to its high conformational flexibility and negative charge, suramin is considered quite promiscuous toward positively charged sites within nucleic acid binding proteins. Although some suramin analogs have been developed against specific targets, only limited structure-activity relationship studies were performed, and virtual screening has yet to be used to identify more specific inhibitor(s) based on its scaffold. Using available structures, we investigated suramin's target diversity, confirming that suramin preferentially binds to protein pockets that are both positively charged and enriched in aromatic or leucine residues. Further, suramin's high conformational flexibility allows adaptation to structurally diverse binding surfaces. From this platform, we developed a framework for structure- and docking-guided elaboration of suramin analog scaffolds using virtual screening of suramin and heparin analogs against a panel of diverse therapeutically relevant viral and bacterial protein targets. Use of this new framework to design potentially specific suramin analogs is exemplified using the SARS-CoV-2 RNA-dependent RNA polymerase and nucleocapsid protein, identifying leads that might inhibit a wide range of coronaviruses. The approach presented here establishes a computational framework for designing suramin analogs against different bacterial and viral targets and repurposing existing drugs for more specific inhibitory activity.