Therapeutic Protein Targets for Drug Discovery and Clinical Evaluation

10.1142/12791 ◽  
2022 ◽  
Author(s):  
D Velmurugan ◽  
Atanu Bhattacharjee ◽  
D Gayathri
Keyword(s):  
Drug Discovery ◽  
Clinical Evaluation ◽  
Therapeutic Protein ◽  
Protein Targets

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.

scholarly journals Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

2021 ◽  
Author(s):  
Giang Nguyen ◽  
Jack Bennett ◽  
Sherrie Liu ◽  
Sarah Hancock ◽  
Daniel Winter ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Drug Discovery ◽  
Small Molecules ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Native Mass Spectrometry ◽  
Protein Targets ◽  
Rapid Measurement

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, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

scholarly journals Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

2018 ◽  
Author(s):  
Jeffrey R. Wagner ◽  
Christopher P. Churas ◽  
Shuai Liu ◽  
Robert V. Swift ◽  
Michael Chiu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Unmet Need ◽  
Specific Protein ◽  
Protein Targets ◽  
Continuous Evaluation ◽  
Docking Algorithm ◽  
Docking Calculations ◽  
Automated Docking

1SummaryDocking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein. However, studies in the literature use varied docking methods, and it is not clear which work best, either in general or for specific protein targets. In addition, a complete docking calculation requires components beyond the docking algorithm itself, such as preparation of the protein and ligand for calculations, and it is difficult to isolate which aspects of a method are most in need of improvement. To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows. Participants in CELPP create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new (never before released) protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow’s predictions and posts the scores online. CELPP is a new cyberinfrastructure resource to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

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 Checkpoint therapeutic target database (CKTTD): the first comprehensive database for checkpoint targets and their modulators in cancer immunotherapy

2020 ◽  
Vol 8 (2) ◽  
pp. e001247
Author(s):  
Yixiao Zhang ◽  
Yuan Yao ◽  
Peng Chen ◽  
Yu Liu ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Cancer Immunotherapy ◽  
Therapeutic Target ◽  
Immune Escape ◽  
Depth Information ◽  
Biological Databases ◽  
Mining System ◽  
Protein Targets ◽  
Comprehensive Database ◽  
Target Database

BackgroundCheckpoint targets play a key role in tumor-mediated immune escape and therefore are critical for cancer immunotherapy. Unfortunately, there is a lack of bioinformatics resource that compile all the checkpoint targets for translational research and drug discovery in immuno-oncology.MethodsTo this end, we developed checkpoint therapeutic target database (CKTTD), the first comprehensive database for immune checkpoint targets (proteins, miRNAs and LncRNAs) and their modulators. A scoring system was adopted to filter more relevant targets with high confidence. In addition, a few biological databases such as Oncomine, Drugbank, miRBase and Lnc2Cancer database were integrated into CKTTD to provide an in-depth information. Moreover, we computed and provided ligand-binding site information for all the targets which may support bench scientists for drug discovery efforts.ResultsIn total, CKTTD compiles 105 checkpoint protein targets, 53 modulators (small-molecules and antibody), 30 miRNAs and 18 LncRNAs in cancer immunotherapy with validated experimental evidences curated from 10 649 literatures via an enhanced text-mining system.ConclusionsIn conclusion, the CKTTD may serve as a useful platform for the research of cancer immunotherapy and drug discovery. The CKTTD database is freely available to public at http://www.ckttdb.org/.

Multiplexed screening of thousands of natural products for protein-ligand binding in native mass spectrometry

2021 ◽  
Author(s):  
Giang Nguyen ◽  
Jack Bennett ◽  
Sherrie Liu ◽  
Sarah Hancock ◽  
Daniel Winter ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Natural Products ◽  
Drug Discovery ◽  
Small Molecules ◽  
Natural Product ◽  
Drug Targets ◽  
Structural Diversity ◽  
Native Mass Spectrometry ◽  
Protein Targets ◽  
Rapid Measurement

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, novel natural product ligands of human drug targets could be identified without fractionation. This method should significantly increase the efficiency of target-based natural product drug discovery workflows.

Sign in / Sign up

Export Citation Format

Share Document