scholarly journals De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to aid drug discovery

2020 ◽  
Author(s):  
Ramya Rangan ◽  
Andrew M. Watkins ◽  
Jose Chacon ◽  
Wipapat Kladwang ◽  
Ivan N. Zheludev ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Computational Models ◽  
De Novo ◽  
Hypervariable Region ◽  
Chemical Mapping ◽  
Link Type ◽  
Reverse Complement ◽  
Rna Elements ◽  
Rna Binders

AbstractThe rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta’s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5’ UTR; the reverse complement of the 5’ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3’ UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m, and 3’ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.

Towards structure-based protein drug design

2011 ◽  
Vol 39 (5) ◽  
pp. 1382-1386 ◽  
Author(s):  
Changsheng Zhang ◽  
Luhua Lai
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Small Molecule ◽  
Protein Interactions ◽  
De Novo ◽  
Protein Docking ◽  
Erythropoietin Receptor ◽  
Protein Drug ◽  
Protein Protein Interactions ◽  
Structure Based Drug Design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

scholarly journals Drug Target Ontology to Classify and Integrate Drug Discovery Data

2017 ◽  
Author(s):  
Yu Lin ◽  
Saurabh Mehta ◽  
Hande Küçük-McGinty ◽  
John Paul Turner ◽  
Dusica Vidovic ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Drug Target ◽  
Disease Association ◽  
Formal Ontology ◽  
Protein Target ◽  
Link Type ◽  
Drug Mechanism ◽  
Druggable Targets ◽  
Druggable Genome

AbstractBackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.ResultsAs part of that effort, we have been developing a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.ConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the websitehttp://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

scholarly journals Quantification of Histone H3 Lys27 Trimethylation (H3K27me3) by High-Throughput Microscopy Enables Cellular Large-Scale Screening for Small-Molecule EZH2 Inhibitors

2014 ◽  
Vol 20 (2) ◽  
pp. 190-201 ◽  
Author(s):  
Svenja Luense ◽  
Philip Denner ◽  
Amaury Fernández-Montalván ◽  
Ingo Hartung ◽  
Manfred Husemann ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
De Novo ◽  
Histone H3 ◽  
Epigenetic Therapy ◽  
Cellular Assays ◽  
Cancer Intervention

EZH2 inhibition can decrease global histone H3 lysine 27 trimethylation (H3K27me3) and thereby reactivates silenced tumor suppressor genes. Inhibition of EZH2 is regarded as an option for therapeutic cancer intervention. To identify novel small-molecule (SMOL) inhibitors of EZH2 in drug discovery, trustworthy cellular assays amenable for phenotypic high-throughput screening (HTS) are crucial. We describe a reliable approach that quantifies changes in global levels of histone modification marks using high-content analysis (HCA). The approach was validated in different cell lines by using small interfering RNA and SMOL inhibitors. By automation and miniaturization from a 384-well to 1536-well plate, we demonstrated its utility in conducting phenotypic HTS campaigns and assessing structure-activity relationships (SAR). This assay enables screening of SMOL EZH2 inhibitors and can advance the mechanistic understanding of H3K27me3 suppression, which is crucial with regard to epigenetic therapy. We observed that a decrease in global H3K27me3, induced by EZH2 inhibition, comprises two distinct mechanisms: (1) inhibition of de novo DNA methylation and (II) inhibition of dynamic, replication-independent H3K27me3 turnover. This report describes an HCA assay for primary HTS to identify, profile, and optimize cellular active SMOL inhibitors targeting histone methyltransferases, which could benefit epigenetic drug discovery.

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Small Molecule ◽  
Brownian Dynamics ◽  
Model Simulation ◽  
Computational Cost ◽  
Lead Selection ◽  
Delta G ◽  
Dynamics Simulations

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Growing by Sharing - Story of Ryvu and Selvita

2020 ◽  
Vol 7 (1) ◽  
pp. 33-47
Author(s):  
Magdalena Marciniak
Keyword(s):  
Drug Discovery ◽  
Business Model ◽  
Small Molecule ◽  
Contract Research ◽  
Industrial Enterprises ◽  
Strong Position ◽  
Small Molecule Therapeutics

Ryvu Therapeutics and Selvita originated in 2007, a time when drug discovery in Poland was still not pursued by industrial enterprises. For many years, both entities operated one company and were known under a common name Selvita S.A., combining their efforts on both innovative small-molecule therapeutics for oncology and expertise in Contract Research Services (CRO). Following more than a decade of such a hybrid business model, Selvita established a strong position in the field of drug discovery and built trust among partners, clients, and investors globally. This encouraged the leaders of the company to separate the two divisions into fully autonomous units, which in fact, had already been operating quite independently and both were successful in diverse areas of drug discovery activities. At the beginning of October 2019, two new companies were established and both parts were given independence and more opportunities for growth. Discovery and development engine was named as Ryvu Therapeutics, and the CRO part of the company remained with the name Selvita. To reach this stage, both the divisions went through an interesting journey together, supporting and strengthening each other for the benefit of both.

Targeted Protein Degradation: "The Gold Rush is On!"

2020 ◽  
Vol 7 (1) ◽  
pp. 4-16
Author(s):  
Daria Kotlarek ◽  
Agata Pawlik ◽  
Maria Sagan ◽  
Marta Sowała ◽  
Alina Zawiślak-Architek ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Small Molecule ◽  
Gold Rush ◽  
European Company ◽  
Drug Candidates ◽  
Protein Inhibition ◽  
Small Molecule Drug ◽  
Therapeutic Benefits ◽  
Talent Pool

Targeted Protein Degradation (TPD) is an emerging new modality of drug discovery that offers unprecedented therapeutic benefits over traditional protein inhibition. Most importantly, TPD unlocks the untapped pool of the proteome that to date has been considered undruggable. Captor Therapeutics (Captor) is the fourth global, and first European, company that develops small molecule drug candidates based on the principles of targeted protein degradation. Captor is located in Basel, Switzerland and Wroclaw, Poland and exploits the best opportunities of the two sites – experience and non-dilutive European grants, and talent pool, respectively. Through over $38 M of funding, Captor has been active in three areas of TPD: molecular glues, bi-specific degraders and direct degraders, ObteronsTM.

scholarly journals Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 546
Author(s):  
Miroslava Nedyalkova ◽  
Vasil Simeonov
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
De Novo ◽  
Target Prediction ◽  
Natural Compounds ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Unique Source ◽  
The Times ◽  
Partitioning Pattern

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

scholarly journals General synthetic strategy for regioselective ultrafast formation of disulfide bonds in peptides and proteins

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shay Laps ◽  
Fatima Atamleh ◽  
Guy Kamnesky ◽  
Hao Sun ◽  
Ashraf Brik
Keyword(s):  
Drug Discovery ◽  
Disulfide Bonds ◽  
Unsolved Problem ◽  
De Novo ◽  
Therapeutic Potential ◽  
Selective Activation ◽  
One Pot ◽  
Synthetic Strategy ◽  
The One ◽  
Game Changer

AbstractDespite six decades of efforts to synthesize peptides and proteins bearing multiple disulfide bonds, this synthetic challenge remains an unsolved problem in most targets (e.g., knotted mini proteins). Here we show a de novo general synthetic strategy for the ultrafast, high-yielding formation of two and three disulfide bonds in peptides and proteins. We develop an approach based on the combination of a small molecule, ultraviolet-light, and palladium for chemo- and regio-selective activation of cysteine, which enables the one-pot formation of multiple disulfide bonds in various peptides and proteins. We prepare bioactive targets of high therapeutic potential, including conotoxin, RANTES, EETI-II, and plectasin peptides and the linaclotide drug. We anticipate that this strategy will be a game-changer in preparing millions of inaccessible targets for drug discovery.

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