scholarly journals LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecules

2020 ◽  
Author(s):  
Faraz Shaikh ◽  
Hio Kuan Tai ◽  
Nirali Desai ◽  
Shirley Siu
Keyword(s):  
Success Rate ◽  
Target Identification ◽  
Hybrid Approach ◽  
Prediction Method ◽  
Target Prediction ◽  
Binding Mode ◽  
Structure Based Drug Design ◽  
Modern Drug ◽  
Validation Experiment ◽  
Experimental Approaches

Motivation: Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. <div>Results: The LigTMap server provides a fully automated workflow to identify targets from 17 target classes with >6000 proteins. It is a hybrid approach, combining ligand similarity search with docking and binding similarity analysis, to predict putative targets. In the validation experiment, LigTMap achieved a top-10 success rate of almost 70%, with an average precision rate of 0.34. The class-specific prediction method improved the success rate further with enhanced precision. In an independent benchmarking test, LigTMap showed good performance compared to the currently best target prediction servers. LigTMap provides straightaway the PDB of a predicted target and the optimal ligand binding mode, which could facilitate structure-based drug design and the repurposing of existing drugs. </div>

scholarly journals LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecular Compounds

2021 ◽  
Author(s):  
Faraz Shaikh ◽  
Hio Kuan Tai ◽  
Nirali Desai ◽  
Shirley Siu
Keyword(s):  
Success Rate ◽  
Target Identification ◽  
Hybrid Approach ◽  
Prediction Method ◽  
Target Prediction ◽  
Binding Mode ◽  
Structure Based Drug Design ◽  
Modern Drug ◽  
Validation Experiment ◽  
Experimental Approaches

Motivation: Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. <div>Results: The LigTMap server provides a fully automated workflow to identify targets from 17 target classes with >6000 proteins. It is a hybrid approach, combining ligand similarity search with docking and binding similarity analysis, to predict putative targets. In the validation experiment, LigTMap achieved a top-10 success rate of almost 70%, with an average precision rate of 0.34. The class-specific prediction method improved the success rate further with enhanced precision. In an independent benchmarking test, LigTMap showed good performance compared to the currently best target prediction servers. LigTMap provides straightaway the PDB of a predicted target and the optimal ligand binding mode, which could facilitate structure-based drug design and the repurposing of existing drugs. </div>

scholarly journals LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecules

2020 ◽  
Author(s):  
Faraz Shaikh ◽  
Hio Kuan Tai ◽  
Nirali Desai ◽  
Shirley Siu
Keyword(s):  
Success Rate ◽  
Target Identification ◽  
Hybrid Approach ◽  
Prediction Method ◽  
Target Prediction ◽  
Binding Mode ◽  
Structure Based Drug Design ◽  
Modern Drug ◽  
Validation Experiment ◽  
Experimental Approaches

Motivation: Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. <div>Results: The LigTMap server provides a fully automated workflow to identify targets from 17 target classes with >6000 proteins. It is a hybrid approach, combining ligand similarity search with docking and binding similarity analysis, to predict putative targets. In the validation experiment, LigTMap achieved a top-10 success rate of almost 70%, with an average precision rate of 0.34. The class-specific prediction method improved the success rate further with enhanced precision. In an independent benchmarking test, LigTMap showed good performance compared to the currently best target prediction servers. LigTMap provides straightaway the PDB of a predicted target and the optimal ligand binding mode, which could facilitate structure-based drug design and the repurposing of existing drugs. </div>

scholarly journals LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Faraz Shaikh ◽  
Hio Kuan Tai ◽  
Nirali Desai ◽  
Shirley W. I. Siu
Keyword(s):  
Success Rate ◽  
Target Identification ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Ligand Docking ◽  
Protein Targets ◽  
Link Type ◽  
Modern Drug ◽  
Validation Experiment ◽  
Experimental Approaches

AbstractTarget prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

scholarly journals MDO: A Computational Protocol for Accurate Prediction of Full Flexibility Receptor-Ligand Binding Mode Structures

2021 ◽  
Author(s):  
Amar Y. Al-Ansi ◽  
Zijing Lin
Keyword(s):  
Ligand Binding ◽  
Success Rate ◽  
Md Simulation ◽  
Binding Mode ◽  
Structure Based Drug Design ◽  
Promising Tool ◽  
Local Sampling ◽  
Binding Structure ◽  
Computational Protocol ◽  
Oniom Calculations

Abstract Predicting the binding structure of bio-complex is essential for understanding its properties, functions, and mechanisms, but is rather difficult due to the huge sampling space involved. A new computational protocol, MDO, for finding the ligand binding structure is proposed. MDO consists of global sampling via MD simulation and clustering of the receptor configurations, local sampling via molecular docking and clustering of the ligand conformations, and binding structure optimization by the ONIOM (QM/QM) method. MDO is tested on 15 protein-ligand complexes with known accurate structures. The success rate of MDO predictions, with RMSD < 2 Å, is found to be 67%, substantially higher than the 40% success rate of conventional methods. The MDO success rate can be increased to 83% if the ONIOM calculations are applied only for the starting poses with ligands inside the binding cavities. The MDO protocol is a promising tool for the structure based drug design.

Recent development of bioinformatics tools for microRNA target prediction

2021 ◽  
Vol 28 ◽  
Author(s):  
Mst Shamima Khatun ◽  
Md Ashad Alam ◽  
Watshara Shoombuatong ◽  
Md Nurul Haque Mollah ◽  
Hiroyuki Kurata ◽  
...  
Keyword(s):  
Mirna Target ◽  
Target Identification ◽  
Target Prediction ◽  
Mirna Target Prediction ◽  
Microrna Target ◽  
Rna Sequences ◽  
Target Mrnas ◽  
Mirna Targets ◽  
Bioinformatics Tools ◽  
Experimental Approaches

: MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Optimization of germanium enrichment from waste optical fibers: A validation experiment using a hybrid approach

2018 ◽  
Vol 60 (4) ◽  
pp. 413-417 ◽  
Author(s):  
Soo-Young Lee ◽  
Minseok Lee ◽  
Sungkyu Lee ◽  
Sung-Su Cho ◽  
Minhye Seo
Keyword(s):  
Optical Fibers ◽  
Hybrid Approach ◽  
Validation Experiment

scholarly journals Drug Target Identification with Machine Learning: How to Choose Negative Examples

2021 ◽  
Vol 22 (10) ◽  
pp. 5118
Author(s):  
Matthieu Najm ◽  
Chloé-Agathe Azencott ◽  
Benoit Playe ◽  
Véronique Stoven
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Identification ◽  
Target Prediction ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Statistical Bias ◽  
Protein Targets ◽  
Drug Target Identification ◽  
Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

scholarly journals Some Perspectives on Network Modeling in Therapeutic Target Prediction

2013 ◽  
Vol 5 ◽  
pp. BECB.S10793 ◽  
Author(s):  
Reka Albert ◽  
Bhaskar DasGupta ◽  
Nasim Mobasheri
Keyword(s):  
Therapeutic Target ◽  
Drug Target ◽  
Biological Network ◽  
Target Identification ◽  
Target Prediction ◽  
Identification Problem ◽  
Pharmaceutical Companies ◽  
Specific Behavior ◽  
Drug Target Identification ◽  
Key Steps

Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities.

Modern Paradigm Towards Potential Target Identification for Antiviral (SARS-nCoV-2) and Anticancer Lipopeptides: A Pharmacophore-Based Approach

2022 ◽  
Author(s):  
Manisha Yadav ◽  
J. Satya Eswari
Keyword(s):  
Drug Discovery ◽  
Target Identification ◽  
Target Prediction ◽  
Cost Effective ◽  
Time Saving ◽  
Web Based ◽  
Target Proteins ◽  
Lead Compounds ◽  
Potential Target ◽  
Biopharmaceutical Properties

Background: Lipopeptides are potential microbial metabolites that are abandoned with broad spectrum biopharmaceutical properties ranging from antimicrobial, antiviral and anticancer, etc. Clinical studies are not much explored beyond the experimental methods to understand drug mechanisms on target proteins at the molecular level for large molecules. Due to the less available studies on potential target proteins of lipopeptide based drugs, their potential inhibitory role for more obvious treatment on disease have not been explored in the direction of lead optimization. However, Computational approaches need to be utilized to explore drug discovery aspects on lipopeptide based drugs, which are time saving and cost-effective techniques. Methods: Here a ligand-based drug discovery approach is coupled with reverse pharmacophore-mapping for the prediction of potential targets for antiviral (SARS-nCoV-2) and anticancer lipopeptides. Web-based servers PharmMapper and Swiss Target Prediction are used for the identification of target proteins for lipopeptides surfactin and iturin produced by Bacillus subtilis. Results: The studies have given the insight to treat the diseases with next-generation large molecule therapeutics. Results also indicate the affinity for Angiotensin-Converting Enzymes (ACE) and proteases as the potential viral targets for these categories of peptide therapeutics. A target protein for the Human Papilloma Virus (HPV) has also been mapped. Conclusion: The work will further help in exploring computer-aided drug designing of novel compounds with greater efficiency where the structure of the target proteins and lead compounds are known.  

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

2016 ◽  
pp. 196-219
Author(s):  
Sanchaita Rajkhowa ◽  
Ramesh C. Deka
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
High Throughput Screening ◽  
Docking Studies ◽  
Stable Complex ◽  
Scoring Functions ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Modern Drug

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Sign in / Sign up

Export Citation Format

Share Document