scholarly journals The relationship between effective molarity and affinity governs rate enhancements in tethered kinase-substrate reactions

2020 ◽  
Author(s):  
Elizabeth B. Speltz ◽  
Jesse G. Zalatan
Keyword(s):  
Significant Contribution ◽  
Reaction Rates ◽  
Model Systems ◽  
Kinase Substrate ◽  
Protein Ligand Interactions ◽  
Endogenous Protein ◽  
Ligand Interactions ◽  
Effective Molarity ◽  
Molecule Model ◽  
The Relationship

AbstractScaffold proteins are thought to accelerate protein phosphorylation reactions by tethering kinases and substrates together, but there is little quantitative data on their functional effects. To assess the contribution of tethering to kinase reactivity, we compared intramolecular and intermolecular kinase reactions in a minimal model system. We find that tethering can enhance reaction rates in a flexible tethered kinase system, and the magnitude of the effect is sensitive to the structure of the tether. The largest effective molarity we obtained was ∼0.08 µM, which is much lower than the effects observed in small molecule model systems and tethered protein-ligand interactions. We further demonstrate that the tethered, intramolecular reaction only makes a significant contribution to observed rates when the scaffolded complex assembles at concentrations below the effective molarity. These findings provide a quantitative framework that can be applied to understand endogenous protein scaffolds and to engineer synthetic networks.

scholarly journals Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

2021 ◽  
Author(s):  
H. Tomas Rube ◽  
Chaitanya Rastogi ◽  
Siqian Feng ◽  
Judith Franziska Kribelbauer ◽  
Allyson Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biological Networks ◽  
Specific Protein ◽  
Biophysical Parameters ◽  
Kinase Substrate ◽  
Protein Ligand Interactions ◽  
Sequence Recognition ◽  
Ligand Interactions ◽  
The Impact

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

scholarly journals Pillar[5]arene-Derived endo-Functionalized Molecular Tube for Mimicking Protein-Ligand Interactions

2021 ◽  
Author(s):  
Ke Wen ◽  
Zhuo Wang ◽  
Tao Chen ◽  
Hua Liu ◽  
Yahu Liu ◽  
...  
Keyword(s):  
Protein Binding ◽  
Binding Affinity ◽  
Binding Pocket ◽  
Model Systems ◽  
Enzymatic Reactions ◽  
Protein Ligand Interactions ◽  
Donor And Acceptor ◽  
Inner Surface ◽  
Ligand Interactions ◽  
Molecular Tube

Abstract Artificial tubular molecular pockets bearing polar functionalities on their inner surface are useful model systems for understanding the mechanisms of protein-ligand interactions in living systems. We herein report a pillar[5]arene-derived molecular tube, [P4-(OH)BPO], whose endo conformational isomer endo-[P4-(OH)BPO] possesses an inwardly pointing hydrogen-bond (H-bond) donor (OH) in its deep cavity, a strong H-bond acceptor (C=O) on the predominantly hydrophobic inner surface, rendering it a perfect protein binding pocket mimetic. By measuring the binding affinity of this pocket-mimetic tube, we screened a library of various shape-complementary organic guests (1–38) resembling the fragment ligands in fragment-based drug design (FBDD). On the basis of the data for “fragment-pocket” complexes (1–38)⊂endo-[P4-(OH)BPO], two rationally designed “lead molecules” (39 and 40) were identified to be able to enhance binding affinity significantly by forming H-bonds with both the donor and acceptor of endo-[P4-(OH)BPO]. The described work opens new avenues for developing pillar[n]arene-derived protein binding pocket-mimetic systems for studies on protein-ligand interactions and mechanisms of enzymatic reactions.

Benchmark of electronic structure methods for protein–ligand interactions based on high-level reference data

2015 ◽  
Vol 14 (01) ◽  
pp. 1540001 ◽  
Author(s):  
Nusret Duygu Yilmazer ◽  
Pascal Heitel ◽  
Tobias Schwabe ◽  
Martin Korth
Keyword(s):  
Density Functional ◽  
Difficult Problem ◽  
Model Systems ◽  
Quantum Mechanical ◽  
Test Cases ◽  
Functional Theory ◽  
Protein Ligand Interactions ◽  
Mechanical Methods ◽  
Ligand Interactions ◽  
High Level

The accurate prediction of the strength of protein–ligand interactions is a very difficult problem despite impressive advances in the field of biomolecular modeling. There are good reasons to believe that quantum mechanical methods can help with this task, but the application of such methods in the context of scoring is still in its infancy. Here we benchmark several wave function theory (WFT), density functional theory (DFT) and semiempirical quantum mechanical (SQM) approaches against high-level theoretical references for realistic test cases. Based on our findings for systematically generated model systems of real protein/ligand complexes from the PDB-bind database, we can recommend SCS-MP2 and B2-PLYP-D3 as reference methods, TPSS-D3+Dabc/def-TZVPP as the best DFT approach and PM6-DH+ as a fast and accurate alternative to full ab initio treatments.

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
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.

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

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.

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

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.

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

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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