An intrinsic FRET sensor of protein–ligand interactions

Mutations in the genes encoding Leucine Rich Repeat (LRR) containing proteins are associated with over sixty human diseases; these include high myopia, mitochondrial encephalomyopathy, and Crohn’s disease. These mutations occur frequently within the LRR domains and within the regions that shield the hydrophobic core of the LRR domain. The amino acid sequences of fifty-five LRR proteins have been published. They include Nod-Like Receptors (NLRs) such as NLRP1, NLRP3, NLRP14, and Nod-2, Small Leucine Rich Repeat Proteoglycans (SLRPs) such as keratocan, lumican, fibromodulin, PRELP, biglycan, and nyctalopin, and F-box/LRR-repeat proteins such as FBXL2, FBXL4, and FBXL12. For example, 363 missense mutations have been identified. Replacement of arginine, proline, or cysteine by another amino acid, or the reverse, is frequently observed. The diverse effects of the mutations are discussed based on the known structures of LRR proteins. These mutations influence protein folding, aggregation, oligomerization, stability, protein-ligand interactions, disulfide bond formation, and glycosylation. Most of the mutations cause loss of function and a few, gain of function.

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Novel Few-shots Learning Neural Network for Predicting Carbohydrate-active Enzyme (CAZyme) Affinity Towards Fructo-oligosaccharides

10.21203/rs.3.rs-92730/v1 ◽

2020 ◽

Author(s):

Shaoxun Liu ◽

Yi Kou ◽

Lin Chen

Keyword(s):

Neural Network ◽

Amino Acid ◽

Binding Affinity ◽

Amino Acid Sequences ◽

Active Enzyme ◽

Binding Interaction ◽

Carbohydrate Active Enzyme ◽

Protein Ligand Interactions ◽

Enzyme Substrate ◽

Ligand Interactions

Abstract Background: The enzymatic activity of the microbiome toward carbohydrates in the human digestive system is of enormous health significance (Zou, Y., et al., 2019; Pinard, D., et al., 2015). Predicting how carbohydrates through food intake may affect the distribution and balance of gut microbiota remains a major challenge. Understanding the enzyme-substrate specificity relationship of the carbohydrate-active enzyme (CAZyme) encoded by the vast gut microbiome will be an important step to address this question. In this study, we seek to establish an in-silico approach to studying the enzyme-substrate binding interaction. Results: We focused on the key carbohydrate-active enzyme (CAZyme) and established a novel Poisson noise-based few-shots learning neural network (pFSLNN) for predicting the binding affinity of indigestible carbohydrates. This approach achieved higher accuracy than other classic FSLNNs, and we have also formulated new algorithms for feature generation using only a few amino acid sequences. Sliding bin regression is integrated with mRMR for feature selection. Conclusion: The resulting pFSLNN is an efficient model to predict the binding affinity between CAZyme and common oligosaccharides. This model can be potentially applied to binding affinity prediction of other protein-ligand interactions based on limited amino acid sequences.

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19F-NMR in Target-based Drug Discovery

Current Medicinal Chemistry ◽

10.2174/0929867326666190610160534 ◽

2019 ◽

Vol 26 (26) ◽

pp. 4964-4983 ◽

Cited By ~ 7

Author(s):

CongBao Kang

Keyword(s):

Drug Discovery ◽

Conformational Changes ◽

Protein Structures ◽

19F Nmr ◽

Binding Affinities ◽

Protein Ligand Interactions ◽

Compound Libraries ◽

Ligand Interactions ◽

Reference Ligand ◽

Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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Synthetic Peptide Arrays and Peptide Combinatorial Libraries for the Exploration of Protein-Ligand Interactions and the Design of Protein Inhibitors

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207053258497 ◽

2005 ◽

Vol 8 (2) ◽

pp. 135-143 ◽

Cited By ~ 25

Author(s):

Jutta Eichler

Keyword(s):

Synthetic Peptide ◽

Combinatorial Libraries ◽

Peptide Arrays ◽

Protein Inhibitors ◽

Protein Ligand Interactions ◽

Ligand Interactions

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Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

Current Drug Discovery Technologies ◽

10.2174/1570163815666181008165822 ◽

2020 ◽

Vol 17 (2) ◽

pp. 233-247

Author(s):

Krishna A. Gajjar ◽

Anuradha K. Gajjar

Keyword(s):

Molecular Docking ◽

Structural Information ◽

Docking Studies ◽

Structural Motif ◽

Roc Curve Analysis ◽

Ligand Complex ◽

Discovery Studio ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Pharmacophore Models

Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Results and Conclusion: Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

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Xanthine Oxidase and Transforming Growth Factor Beta-activated Kinase 1: potential Targets for Gout Intervention

Current Rheumatology Reviews ◽

10.2174/1573397116666201126162202 ◽

2020 ◽

Vol 16 ◽

Author(s):

Rajesh Basnet ◽

Sandhya Khadka ◽

Buddha Bahadur Basnet ◽

Til Bahadur Basnet ◽

Buddhi Bal Chidi ◽

...

Keyword(s):

Growth Factor ◽

Xanthine Oxidase ◽

Transforming Growth Factor Beta ◽

Transforming Growth Factor ◽

Developed Countries ◽

Structure Based Drug Design ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Moderate Condition ◽

Growth Factor Beta

Background: Gout, an inflammatory arthritis, caused by the deposition of monosodium urate crystals into affected joints and other tissues has become one of the major health problems of today's world. The main risk factor for gout is hyperuricemia, which may be caused by excessive or insufficient excretion of uric acid. The incidence is usually in the age group of 30- 50 years, commonly in males. In developed countries, the incidence of gout ranges from 1 to 4%. Despite effective treatments, there has been an increase in the number of cases over the past few decades. Objective: In recent years, the development of targeted drugs in gout has made significant achievements. The global impact of gout continues to increase, and as a result, the focus of disease-modifying therapies remains elusive. In addition, the characterization of available instrumental compounds is urgently needed to explore the use of novel selective and key protein-ligand interactions for the effective treatment of gout. Xanthine oxidase (XO) is a key target in gout to consider the use of XO inhibitors in patients with mild to moderate condition, however, the costs are high and no other direct progress has been made. Despite many XO inhibitors, a selective potent inhibitor for XO is limited. Likewise, in recent years, attention has been focused on different strategies for the discovery and development of new selectivity ligands against transforming growth factor beta-activated kinase 1 (TAK1), a potential therapeutic target for gout. Therefore the insight on human XO structure and TAK1 provides a clue into protein-ligand interactions and provides the basis for molecular modeling and structure-based drug design. Conclusion: In this review, we briefly introduce the clinical characteristics, the development of crystal, inhibitors, and crystal structure of XO and TAK1 protein.

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ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Journal of Cheminformatics ◽

10.1186/s13321-021-00486-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Xujun Zhang ◽

Chao Shen ◽

Xueying Guo ◽

Zhe Wang ◽

Gaoqi Weng ◽

...

Keyword(s):

High Efficiency ◽

Low Cost ◽

Pearson Correlation ◽

Scoring Function ◽

Web Server ◽

Scoring Functions ◽

Protein Ligand Interactions ◽

Prediction Module ◽

Ligand Interactions ◽

Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

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