scholarly journals Multi-Conformer Ensemble Docking to Difficult Protein Targets

2014 ◽  
Vol 119 (3) ◽  
pp. 1026-1034 ◽  
Author(s):  
Sally R. Ellingson ◽  
Yinglong Miao ◽  
Jerome Baudry ◽  
Jeremy C. Smith
Keyword(s):  
Protein Targets ◽  
Ensemble Docking ◽  
Conformer Ensemble

scholarly journals Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

2020 ◽  
Author(s):  
Atanu Acharya ◽  
Rupesh Agarwal ◽  
Matthew Baker ◽  
Jerome Baudry ◽  
Debsindhu Bhowmik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
National Laboratory ◽  
Massively Parallel ◽  
Enhanced Sampling ◽  
Autodock Vina ◽  
Protein Targets ◽  
Oak Ridge ◽  
Preliminary Results ◽  
Ensemble Docking ◽  
Discovery Pipeline

We present a supercomputer-driven pipeline for <i>in-silico</i> drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

scholarly journals Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

2020 ◽  
Author(s):  
Atanu Acharya ◽  
Rupesh Agarwal ◽  
Matthew Baker ◽  
Jerome Baudry ◽  
Debsindhu Bhowmik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
National Laboratory ◽  
Massively Parallel ◽  
Enhanced Sampling ◽  
Autodock Vina ◽  
Protein Targets ◽  
Oak Ridge ◽  
Preliminary Results ◽  
Ensemble Docking ◽  
Discovery Pipeline

We present a supercomputer-driven pipeline for <i>in-silico</i> drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

Dicarbonyl derived post-translational modifications: chemistry bridging biology and aging-related disease

2020 ◽  
Vol 64 (1) ◽  
pp. 97-110
Author(s):  
Christian Sibbersen ◽  
Mogens Johannsen
Keyword(s):  
Mass Spectrometry ◽  
Future Research ◽  
Amino Acid Residues ◽  
Post Translational Modification ◽  
Dicarbonyl Compounds ◽  
Protein Targets ◽  
Post Translational Modifications ◽  
Reactive Protein ◽  
Glycation End Products ◽  
Direct Mass Spectrometry

Abstract In living systems, nucleophilic amino acid residues are prone to non-enzymatic post-translational modification by electrophiles. α-Dicarbonyl compounds are a special type of electrophiles that can react irreversibly with lysine, arginine, and cysteine residues via complex mechanisms to form post-translational modifications known as advanced glycation end-products (AGEs). Glyoxal, methylglyoxal, and 3-deoxyglucosone are the major endogenous dicarbonyls, with methylglyoxal being the most well-studied. There are several routes that lead to the formation of dicarbonyl compounds, most originating from glucose and glucose metabolism, such as the non-enzymatic decomposition of glycolytic intermediates and fructosyl amines. Although dicarbonyls are removed continuously mainly via the glyoxalase system, several conditions lead to an increase in dicarbonyl concentration and thereby AGE formation. AGEs have been implicated in diabetes and aging-related diseases, and for this reason the elucidation of their structure as well as protein targets is of great interest. Though the dicarbonyls and reactive protein side chains are of relatively simple nature, the structures of the adducts as well as their mechanism of formation are not that trivial. Furthermore, detection of sites of modification can be demanding and current best practices rely on either direct mass spectrometry or various methods of enrichment based on antibodies or click chemistry followed by mass spectrometry. Future research into the structure of these adducts and protein targets of dicarbonyl compounds may improve the understanding of how the mechanisms of diabetes and aging-related physiological damage occur.

In Search of Novel Cancer Therapeutics: Assessing the NCI Natural Product Library against Ten Anti-Apoptotic Protein Targets

Planta Medica ◽  
2013 ◽  
Vol 79 (10) ◽  
Author(s):  
DB Divlianska ◽  
AE Wright ◽  
S Francis ◽  
MA Walters ◽  
CE Salomon ◽  
...  
Keyword(s):  
Natural Product ◽  
Cancer Therapeutics ◽  
Protein Targets ◽  
Apoptotic Protein ◽  
Natural Product Library

Drug Repurposing Studies Targeting SARS-nCoV2: An Ensemble Docking Approach on Drug Target 3C-like Protease (3CLpro)

2020 ◽  
Author(s):  
Shruti Koulgi ◽  
Vinod Jani ◽  
Mallikarjunachari Uppuladinne ◽  
Uddhavesh Sonavane ◽  
Asheet Kumar Nath ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Drug Binding ◽  
Ensemble Docking ◽  
Drug Molecules ◽  
Drug Binding Site ◽  
Main Protease ◽  
Docking Approach ◽  
Novel Coronavirus ◽  
Markov State Modeling ◽  
Viral Polyprotein

<p>The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2 (SARS-nCoV2). SARS-nCoV2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-03. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-nCoV2. The high-resolution crystal structure of 3CL<sup>pro</sup> (main protease) of SARS-nCoV2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration (FDA) approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for main protease helped in exploring the conformational variation in the drug binding site of the main protease leading to efficient binding of more relevant drug molecules. The drugs obtained as best hits from the ensemble docking possessed anti-bacterial and anti-viral properties. Small molecules with these properties may prove to be useful to treat symptoms exhibited in COVID-19. This <i>in-silico</i> ensemble docking approach would support identification of potential candidates for repurposing against COVID-19.</p>

Rapid Elaboration of Fragments into Leads Applied to Bromodomain-3 Extra Terminal Domain

2020 ◽  
Author(s):  
Luke Adams ◽  
Lorna E. Wilkinson-White ◽  
Menachem J. Gunzburg ◽  
Stephen J. Headey ◽  
Martin J. Scanlon ◽  
...  
Keyword(s):  
Binding Site ◽  
Systematic Approach ◽  
Structural Information ◽  
Peptide Binding ◽  
Chemical Diversity ◽  
Chemical Probes ◽  
Protein Targets ◽  
Structure Activity ◽  
Peptide Binding Site ◽  
Selection Of

The development of low-affinity fragment hits into higher affinity leads is a major hurdle in fragment-based drug design. Here we demonstrate an approach for the Rapid Elaboration of Fragments into Leads (REFiL) applying an integrated workflow that provides a systematic approach to generate higher-affinity binders without the need for structural information. The workflow involves the selection of commercial analogues of fragment hits to generate preliminary structure-activity relationships. This is followed by parallel microscale chemistry using chemoinformatically designed reagent libraries to rapidly explore chemical diversity. Upon completion of a fragment screen against Bromodomain-3 extra terminal (BRD3-ET) domain we applied the REFiL workflow, which allowed us to develop a series of tetrahydrocarbazole ligands that bind to the peptide binding site of BRD3-ET. With REFiL we were able to rapidly improve binding affinity >30-fold. The REFiL workflow can be applied readily to a broad range of protein targets without the need of a structure, allowing the efficient evolution of low-affinity fragments into higher affinity leads and chemical probes.<br>

Conjugates of Classical DNA/RNA Binder with Nucleobase: Chemical, Biochemical and Biomedical Applications

2019 ◽  
Vol 26 (30) ◽  
pp. 5609-5624
Author(s):  
Dijana Saftić ◽  
Željka Ban ◽  
Josipa Matić ◽  
Lidija-Marija Tumirv ◽  
Ivo Piantanida
Keyword(s):  
Molecular Weight ◽  
Small Molecules ◽  
Biomedical Applications ◽  
Protein Targets ◽  
New Class ◽  
Rna Targets ◽  
Covalently Linked ◽  
Structural Aspects

: Among the most intensively studied classes of small molecules (molecular weight < 650) in biomedical research are small molecules that non-covalently bind to DNA/RNA, and another intensively studied class is nucleobase derivatives. Both classes have been intensively elaborated in many books and reviews. However, conjugates consisting of DNA/RNA binder covalently linked to nucleobase are much less studied and have not been reviewed in the last two decades. Therefore, this review summarized reports on the design of classical DNA/RNA binder – nucleobase conjugates, as well as data about their interactions with various DNA or RNA targets, and even in some cases protein targets are involved. According to these data, the most important structural aspects of selective or even specific recognition between small molecule and target are proposed, and where possible related biochemical and biomedical aspects were discussed. The general conclusion is that this, rather new class of molecules showed an amazing set of recognition tools for numerous DNA or RNA targets in the last two decades, as well as few intriguing in vitro and in vivo selectivities. Several lead research lines show promising advancements toward either novel, highly selective markers or bioactive, potentially druggable molecules.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

2020 ◽  
Vol 27 (35) ◽  
pp. 5856-5886 ◽  
Author(s):  
Chen Wang ◽  
Lukasz Kurgan
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Future Development ◽  
High Impact ◽  
Therapeutic Protein ◽  
Knowledge Gap ◽  
Therapeutic Activity ◽  
Drug Molecule ◽  
Protein Targets ◽  
Predictive Tools

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Detectability of Plasma Proteins in SRM Measurements

2018 ◽  
Vol 16 (1) ◽  
pp. 74-81 ◽  
Author(s):  
Olga I. Kiseleva ◽  
Elena A. Ponomarenko ◽  
Yulia A. Romashova ◽  
Ekaterina V. Poverennaya ◽  
Andrey V. Lisitsa
Keyword(s):  
Blood Plasma ◽  
Human Plasma ◽  
Limit Of Detection ◽  
Human Blood Plasma ◽  
Analytical Sensitivity ◽  
Plasma Proteome ◽  
Protein Targets ◽  
Blood Plasma Sample ◽  
Specificity And Sensitivity ◽  
Human Plasma Proteome

Background: Liquid chromatography coupled with targeted mass spectrometry underwent rapid technical evolution during last years and has become widely used technology in clinical laboratories. It offers confident specificity and sensitivity superior to those of traditional immunoassays. However, due to controversial reports on reproducibility of SRM measurements, the prospects of clinical appliance of the method are worth discussing. </P><P> Objective: The study was aimed at assessment of capabilities of SRM to achieve a thorough assembly of the human plasma proteome. </P><P> Method: We examined set of 19 human blood plasma samples to measure 100 proteins, including FDA-approved biomarkers, via SRM-assay. </P><P> Results: Out of 100 target proteins 43 proteins were confidently detected in at least two blood plasma sample runs, 36 and 21 proteins were either not detected in any run or inconsistently detected, respectively. Empiric dependences on protein detectability were derived to predict the number of biological samples required to detect with certainty a diagnostically relevant quantum of the human plasma proteome. </P><P> Conclusion: The number of samples exponentially increases with an increase in the number of protein targets, while proportionally decreasing to the logarithm of the limit of detection. Analytical sensitivity and enormous proteome heterogeneity are major bottlenecks of the human proteome exploration.

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