In silico Methods for Identification of Potential Therapeutic Targets

Zhang, Xuting; Wu, Feng-Xu; Yang, Nan; Zhan, Xiaowei; Liao, Jianbo; Mai, Shangkang; Huang, Zunnan

doi:10.1007/s12539-021-00491-y

Cited by 38 publications

(15 citation statements)

References 182 publications

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“…Using the KEGG database, we retrieved 28 nonredundant protein sequences from the three distinct pathways. In contrast to earlier studies that dealt with extensive initial protein datasets (Table S1), this outcome underscores how the integration of comparative metabolic pathways not only enhances the precision of target extraction but also effectively tackles the challenge of managing large datasets (Zhang et al, 2022). This approach reduces the initial number of proteins requiring screening, thus streamlining the process.…”

Section: Discussionmentioning

confidence: 81%

“…The parameters of molecular weight (MW) and instability index (II) play pivotal roles in prioritizing candidate target proteins (Mondal et al., 2015; Zaidi et al., 2022). Notably, a substantial portion of potential targets identified through comparative omics studies often remain unexplored experimentally (Zhang et al., 2022). To streamline the selection of experimental targets, optimize resource utilization and reduce costs in the initial stages of pesticide development (Zhang et al., 2022), it is advantageous for proteins to exhibit an II score and MW of less than 40 (Zaidi et al., 2022) and 100 kDa (Mondal et al., 2015), respectively.…”

Section: Discussionmentioning

confidence: 99%

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Comparative omics analysis for novel target discovery in plant pathogens: A case study for Magnaporthe oryzae

Luthfi,

Piapukiew,

Pandey

et al. 2023

Plant Pathology

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The central concern surrounding chemical pesticide application is its potential adverse effects on non‐target organisms. For fungal pathogens, the search for specific targets has been complicated by the similarities in pathways shared between these pathogens and humans. We present a comprehensive strategy, integrating comparative omics and bioinformatics, to pinpoint precise targets for fungicides effective against the fungal pathogen Magnaporthe oryzae, responsible for rice blast disease. Our approach involves subtractive metabolic pathways, homology screening and target prioritization. Through subtractive metabolic analysis, we identified three unique M. oryzae pathways, distinct from human and rice. Nonredundant protein sequences were subsequently subjected to BLASTP screening against human and rice, as well as other databases from diverse organisms. Target subcellular localization was predicted using eight tools, including artificial intelligence and a deep‐learning method. A comprehensive examination of biological processes was conducted, including gene expression, protein–protein interactions, network enrichment, broad‐spectrum activity and physicochemical analysis. Glutamate 5‐kinase emerged as the prime candidate for targeted fungicide development, promising progress in precision‐oriented solutions.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Comparative omics analysis for novel target discovery in plant pathogens: A case study for Magnaporthe oryzae

Luthfi,

Piapukiew,

Pandey

et al. 2023

Plant Pathology

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“…To investigate target prediction for melatonin, we examined the target-disease relationships for melatonin receptors in a study that combined searches from multiple databases that apply machine learning processes [89,95] to establish relationships and score them, including Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations). The databases are described further in more detail.…”

Section: Unsupervised and Supervised Machine Learningmentioning

confidence: 99%

Multiplatform-Integrated Identification of Melatonin Targets for a Triad of Psychosocial-Sleep/Circadian-Cardiometabolic Disorders

Campos

Baltatu

Senar³

et al. 2023

IJMS

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Several psychosocial, sleep/circadian, and cardiometabolic disorders have intricately interconnected pathologies involving melatonin disruption. Therefore, we hypothesize that melatonin could be a therapeutic target for treating potential comorbid diseases associated with this triad of psychosocial-sleep/circadian-cardiometabolic disorders. We investigated melatonin’s target prediction and tractability for this triad of disorders. The melatonin’s target prediction for the proposed psychosocial-sleep/circadian-cardiometabolic disorder triad was investigated using databases from Europe PMC, ChEMBL, Open Targets Genetics, Phenodigm, and PheWAS. The association scores for melatonin receptors MT1 and MT2 with this disorder triad were explored for evidence of target–disease predictions. The potential of melatonin as a tractable target in managing the disorder triad was investigated using supervised machine learning to identify melatonin activities in cardiovascular, neuronal, and metabolic assays at the cell, tissue, and organism levels in a curated ChEMBL database. Target–disease visualization was done by graphs created using “igraph” library-based scripts and displayed using the Gephi ForceAtlas algorithm. The combined Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), PhenoDigm (data type: animal models), and PheWAS (data type: genetic associations) databases yielded types and varying levels of evidence for melatonin-disease triad correlations. Of the investigated databases, 235 association scores of melatonin receptors with the targeted diseases were greater than 0.2; to classify the evidence per disease class: 37% listed psychosocial disorders, 9% sleep/circadian disorders, and 54% cardiometabolic disorders. Using supervised machine learning, 546 cardiovascular, neuronal, or metabolic experimental assays with predicted or measured melatonin activity scores were identified in the ChEMBL curated database. Of 248 registered trials, 144 phase I to IV trials for melatonin or agonists have been completed, of which 33.3% were for psychosocial disorders, 59.7% were for sleep/circadian disorders, and 6.9% were for cardiometabolic disorders. Melatonin’s druggability was evidenced by evaluating target prediction and tractability for the triad of psychosocial-sleep/circadian-cardiometabolic disorders. While melatonin research and development in sleep/circadian and psychosocial disorders is more advanced, as evidenced by melatonin association scores, substantial evidence on melatonin discovery in cardiovascular and metabolic disorders supports continued R&D in cardiometabolic disorders, as evidenced by melatonin activity scores. A multiplatform analysis provided an integrative assessment of the target–disease investigations that may justify further translational research.

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“…In medicinal chemistry, researchers are interested in identifying vital proteins whose biological functions are clearly linked to disease, and these proteins become targets for medication development. Drug development must pass through specific stages consisting of disease-related information collection, target identification, target validation, lead discovery, lead optimization, preclinical studies, and clinical trials [ 1 , 2 , 3 ]. The traditional target identification process identifies binding sites and molecular binding modes through experimental methods, which is time consuming and expensive [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this review, we will focus on the identification of binding sites and assessment of druggability during the target identification process (for the identification of disease-related targets, please refer to the review written by Zhang X et al [ 3 ] in our research group). The binding site can be used to determine whether the target protein and the ligand can interact [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Liao

Wang

et al. 2022

Molecules

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Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

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In silico Methods for Identification of Potential Therapeutic Targets

Cited by 38 publications

References 182 publications

Comparative omics analysis for novel target discovery in plant pathogens: A case study for Magnaporthe oryzae

Comparative omics analysis for novel target discovery in plant pathogens: A case study for Magnaporthe oryzae

Multiplatform-Integrated Identification of Melatonin Targets for a Triad of Psychosocial-Sleep/Circadian-Cardiometabolic Disorders

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

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