In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

AbstractNew treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase mononucleotide of the FADS, and some of them were able to inhibit growth of three bacterial species: Corynebacterium ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the former two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets.Author summaryDeveloping cures for antimicrobial-resistant microorganisms is a pressing necessity. Addressing this problem requires the discovery of novel therapeutic targets -for example, bacterial proteins with no human homologues- and the development of cost-effective drug screening protocols. In this work, we tackled the problem on both sides. We developed an efficient and successful integrative computational protocol for screening inhibitory-molecules for unexplored targets. We used it to discover five novel inhibitors of flavin-adenine dinucleotide synthase (FADS), a promising protein target of pathogens causing tuberculosis and pneumonia.

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In Silico De Novo Curcuminoid Derivatives From the Compound Library of Natural Products Research Laboratories Inhibit COVID-19 3CLpro Activity

Natural Product Communications ◽

10.1177/1934578x20953262 ◽

2020 ◽

Vol 15 (9) ◽

pp. 1934578X2095326

Author(s):

Jai-Sing Yang ◽

Jo-Hua Chiang ◽

Shih‑Chang Tsai ◽

Yuan-Man Hsu ◽

Da-Tian Bau ◽

...

Keyword(s):

Natural Products ◽

Virtual Screening ◽

Binding Affinity ◽

High Throughput ◽

In Silico ◽

De Novo ◽

Compound Library ◽

Therapeutic Success ◽

High Throughput Virtual Screening

The coronavirus disease 2019 (COVID‐19) outbreak caused by the 2019 novel coronavirus (2019-nCOV) is becoming increasingly serious. In March 2019, the Food and Drug Administration (FDA) designated remdesivir for compassionate use to treat COVID-19. Thus, the development of novel antiviral agents, antibodies, and vaccines against COVID-19 is an urgent research subject. Many laboratories and research organizations are actively investing in the development of new compounds for COVID-19. Through in silico high-throughput virtual screening, we have recently identified compounds from the compound library of Natural Products Research Laboratories (NPRL) that can bind to COVID-19 3Lpro polyprotein and block COVID-19 3Lpro activity through in silico high-throughput virtual screening. Curcuminoid derivatives (including NPRL334, NPRL339, NPRL342, NPRL346, NPRL407, NPRL415, NPRL420, NPRL472, and NPRL473) display strong binding affinity to COVID-19 3Lpro polyprotein. The binding site of curcuminoid derivatives to COVID-19 3Lpro polyprotein is the same as that of the FDA-approved human immunodeficiency virus protease inhibitor (lopinavir) to COVID-19 3Lpro polyprotein. The binding affinity of curcuminoid derivatives to COVID-19 3Lpro is stronger than that of lopinavir and curcumin. Among curcuminoid derivatives, NPRL-334 revealed the strongest binding affinity to COVID-19 3Lpro polyprotein and is speculated to have an anti-COVID-19 effect. In vitro and in vivo ongoing experiments are currently underway to confirm the present findings. This study sheds light on the drug design for COVID-19 3Lpro polyprotein. Basing on lead compound development, we provide new insights on inhibiting COVID-19 attachment to cells, reducing COVID-19 infection rate and drug side effects, and increasing therapeutic success rate.

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In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials

10.26434/chemrxiv.12123204.v3 ◽

2020 ◽

Author(s):

Seref Gul ◽

Onur Ozcan ◽

sinan asar ◽

Alper Okyar ◽

Ibrahim Barıs ◽

...

Keyword(s):

Clinical Trials ◽

Rna Polymerase ◽

Protein Interactions ◽

In Silico ◽

Cost Effective ◽

Md Simulations ◽

Viral Rna ◽

Free Energies ◽

Rna Dependent Rna Polymerase ◽

Approved Drugs

Despite strict measures taken by many countries, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be an issue of global concern. Currently, there are no clinically proven pharmacotherapies for coronavirus disease 2019, despite promising initial results obtained from drugs such as azithromycin and hydroxyquinoline. Therefore, the repurposing of clinically approved drugs for use against SARS-CoV-2 has become a viable strategy. Here, we searched for drugs that target SARS-CoV-2 3C-like protease (3CLpro) and viral RNA-dependent RNA polymerase (RdRp) by in silico screening of the U.S. Food and Drug Administration approved drug library. Well-tolerated and widely used drugs were selected for molecular dynamics (MD) simulations to evaluate drug-protein interactions and their persistence under physiological conditions. Tetracycline, dihydroergotamine, ergotamine, dutasteride, nelfinavir, and paliperidone formed stable interactions with 3CLpro based on MD simulation results. Similar analysis with RdRp showed that eltrombopag, tipranavir, ergotamine, and conivaptan bound to the enzyme with high binding free energies. Docking results suggest that ergotamine, dihydroergotamine, bromocriptine, dutasteride, conivaptan, paliperidone, and tipranavir can bind to both enzymes with high affinity. As these drugs are well tolerated, cost-effective, and widely used, our study suggests that they could potentially to be used in clinical trials for the treatment of SARS-CoV-2-infected patients.

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Application of computer-aided drug design for identification of P. falciparum inhibitors

10.21504/10962/192798 ◽

2021 ◽

Author(s):

◽

Bakary N’tji Diallo

Keyword(s):

Molecular Dynamics ◽

Virtual Screening ◽

In Silico ◽

Artemisinin Combination Therapy ◽

Cost Effective ◽

World Health ◽

Health Concern ◽

Effective Vaccine ◽

Continuous Growth ◽

The Past

Malaria is a millennia-old disease with the first recorded cases dating back to 2700 BC found in Chinese medical records, and later in other civilizations. It has claimed human lives to such an extent that there are a notable associated socio-economic consequences. Currently, according to the World Health Organization (WHO), Africa holds the highest disease burden with 94% of deaths and 82% of cases with P. falciparum having ~100% prevalence. Chemotherapy, such as artemisinin combination therapy, has been and continues to be the work horse in the fight against the disease, together with seasonal malaria chemoprevention and the use of insecticides. Natural products such as quinine and artemisinin are particularly important in terms of their antimalarial activity. The emphasis in current chemotherapy research is the need for time and cost-effective workflows focussed on new mechanisms of action (MoAs) covering the target candidate profiles (TCPs). Despite a decline in cases over the past decades with, countries increasingly becoming certified malaria free, a stalling trend has been observed in the past five years resulting in missing the 2020 Global Technical Strategy (GTS) milestones. With no effective vaccine, a reduction in funding, slower drug approval than resistance emergence from resistant and invasive vectors, and threats in diagnosis with the pfhrp2/3 gene deletion, malaria remains a major health concern. Motivated by these reasons, the primary aim of this work was a contribution to the antimalarial pipeline through in silico approaches focusing on P. falciparum. We first intended an exploration of malarial targets through a proteome scale screening on 36 targets using multiple metrics to account for the multi-objective nature of drug discovery. The continuous growth of structural data offers the ideal scenario for mining new MoAs covering antimalarials TCPs. This was combined with a repurposing strategy using a set of orally available FDA approved drugs. Further, use was made of time- and cost-effective strategies combining QVina-W efficiency metrics that integrate molecular properties, GRIM rescoring for molecular interactions and a hydrogen mass repartitioning (HMR) molecular dynamics (MD) scheme for accelerated development of antimalarials in the context of resistance. This pipeline further integrates a complex ranking for better drug-target selectivity, and normalization strategies to overcome docking scoring function bias. The different metrics, ranking, normalization strategies and their combinations were first assessed using their mean ranking error (MRE). A version combining all metrics was used to select 36 unique protein-ligand complexes, assessed in MD, with the final retention of 25. From the 16 in vitro tested hits of the 25, fingolimod, abiraterone, prazosin, and terazosin showed antiplasmodial activity with IC50 2.21, 3.37, 16.67 and 34.72 μM respectively and of these, only fingolimod was found to be not safe with respect to human cell viability. These compounds were predicted active on different molecular targets, abiraterone was predicted to interact with a putative liver-stage essential target, hence promising as a transmission-blocking agent. The pipeline had a promising 25% hit rate considering the proteome-scale and use of cost-effective approaches. Secondly, we focused on Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) using a more extensive screening pipeline to overcome some of the current in silico screening limitations. Starting from the ZINC lead-like library of ~3M, hierarchical ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches with molecular docking and re-scoring using eleven scoring functions (SFs) were used. Later ranking with an exponential consensus strategy was included. Selected hits were further assessed through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), advanced MD sampling in a ligand pulling simulations and (Weighted Histogram Analysis Method) WHAM analysis for umbrella sampling (US) to derive binding free energies. Four leads had better predicted affinities in US than LC5, a 280 nM potent PfDXR inhibitor with ZINC000050633276 showing a promising binding of -20.43 kcal/mol. As shown with fosmidomycin, DXR inhibition offers fast acting compounds fulfilling antimalarials TCP1. Yet, fosmidomycin has a high polarity causing its short half-life and hampering its clinical use. These leads scaffolds are different from fosmidomycin and hence may offer better pharmacokinetic and pharmacodynamic properties and may also be promising for lead optimization. A combined analysis of residues’ contributions to the free energy of binding in MM-PBSA and to steered molecular dynamics (SMD) Fmax indicated GLU233, CYS268, SER270, TRP296, and HIS341 as exploitable for compound optimization. Finally, we updated the SANCDB library with new NPs and their commercially available analogs as a solution to NP availability. The library is extended to 1005 compounds from its initial 600 compounds and the database is integrated to Mcule and Molport APIs for analogs automatic update. The new set may contribute to virtual screening and to antimalarials as the most effective ones have NP origin.

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Multitarget Virtual Screening for Drug Repurposing in COVID19

10.26434/chemrxiv.12652997 ◽

2020 ◽

Author(s):

carlos oscar Sorzano ◽

Enrique Crisman ◽

Jose Maria Carazo ◽

rafael leon

Keyword(s):

Clinical Trials ◽

Virtual Screening ◽

Drug Screening ◽

Binding Sites ◽

Drug Repurposing ◽

Viral Proteins ◽

Binding Affinities ◽

Compound Library ◽

Screening Analysis ◽

Virtual Drug Screening

Therapeutic or preventive research for coronavirus SARS-CoV2 is an extremely active topic of research since its outbreak in January 2020. In this paper we report the results from a virtual drug screening analysis that, to the best of our knowledge, is the widest work in terms of target proteins and compound library. Our study was focused on the repurposing of currently commercialized drugs, and especially those that can interact with multiple viral proteins and several binding sites within each protein. Additionally, we performed a second virtual screening analysis in which we compared our results to the predicted binding affinities for the drugs currently in clinical trials. We show that the best molecules in our screening compares favorably to those in clinical trials, suggesting their suitability for therapeutic or preventive applications.

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Multitarget Virtual Screening for Drug Repurposing in COVID19

10.26434/chemrxiv.12652997.v2 ◽

2020 ◽

Author(s):

carlos oscar Sorzano ◽

Enrique Crisman ◽

Jose Maria Carazo ◽

rafael leon

Keyword(s):

Clinical Trials ◽

Virtual Screening ◽

Drug Screening ◽

Binding Sites ◽

Drug Repurposing ◽

Viral Proteins ◽

Binding Affinities ◽

Compound Library ◽

Screening Analysis ◽

Virtual Drug Screening

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A Review on Some Indigenous Medicinal Plants and their Key Applications

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i53a33648 ◽

2021 ◽

pp. 160-166

Author(s):

Rhydum Sharma ◽

Ashutosh Tripathi

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Medicinal Plants ◽

Plant Species ◽

In Silico ◽

Molecular Similarity ◽

Cost Effective ◽

New Drugs ◽

Health Food ◽

Traditional Medicines

In India, 7,500 plant species out of 17,000 are officially integrated in ayurvedic pharmacopeia for over a millennium. There are many industrial uses of medicinal plants which include traditional medicines, phytopharmaceuticals, herbal teas, health food etc. Now days, in silico approaches have been developed which is used in virtual screening and analysis of medicinal plants to be used pharmacologically. It is a cost effective and efficient way for the production of new drugs which is done in three basic steps i.e. molecular docking, developing pharmacophores and determining molecular similarity in shape. WHO has also acknowledged the importance of medicinal plants and has created various guidelines and strategies to encourage its use. Agro industrial technologies also encourage the use of medicinal plants. India has wide variety of plant species in its ecosystem. Out of 17,000 species of plants 7,500 species are used as medicinal plants by the tribal groups, villagers and in traditional medicinal systems like Ayurveda. The aim of the review is to summarize the information on the recent development in the field of medicinal plants and their key applications.

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In silico identification of widely used and well-tolerated drugs as potential SARS-CoV-2 3C-like protease and viral RNA-dependent RNA polymerase inhibitors for direct use in clinical trials

10.26434/chemrxiv.12123204 ◽

2020 ◽

Cited By ~ 1

Author(s):

Seref Gul ◽

Onur Ozcan ◽

sinan asar ◽

Alper Okyar ◽

Ibrahim Barıs ◽

...

Keyword(s):

Clinical Trials ◽

Rna Polymerase ◽

Protein Interactions ◽

In Silico ◽

Cost Effective ◽

Md Simulations ◽

Viral Rna ◽

Free Energies ◽

Rna Dependent Rna Polymerase ◽

Approved Drugs

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Logic-Based Modeling and Virtual Drug Screening for the Prediction of Novel Therapeutic Targets and Combination Regimens Against E2F1-driven Melanoma Progression

10.20944/preprints202108.0327.v1 ◽

2021 ◽

Author(s):

Nivedita Singh ◽

Faiz M Khan ◽

Lakshmi Bala ◽

Julio Vera ◽

Olaf Wolkenhauer ◽

...

Keyword(s):

Virtual Screening ◽

Regulatory Network ◽

Epithelial Mesenchymal Transition ◽

Therapeutic Targets ◽

Compound Library ◽

Mesenchymal Transition ◽

Lead Compounds ◽

Protein Signatures ◽

Combination Regimens ◽

Novel Therapeutic

Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream pro-metastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds potential to prevent metastasis, however these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which challenge the characterization of drug targetablemake it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and comprehensively identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identifiedy that simultaneous perturbation of AKT1 and MDM2 drastically reduces EMT in metastatic melanoma. Using the structures of the two protein signatures along with virtual screening of lead-like compound library available in ZINC12 database, we identified a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. We propose that these compounds could be taken into account in the design of novel therapeutic strategies for the management of aggressive melanoma. were identified using virtual screening of lead-like compound library available in ZINC12 database. Subsequent high-throughput virtual screening of drug library using the structures of the two protein signatures predicted a number of lead compounds that efficiently inhibit AKT1 and MDM2 without eliciting toxicities. These can be experimentally evaluated and further considered as new anti-melanoma metastatic agents, in monotherapies or combination regimens.

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