Correlating structure and ligand affinity in drug discovery: a cautionary tale involving second shell residues

2014 ◽  
Vol 395 (7-8) ◽  
pp. 891-903 ◽  
Author(s):  
Anastasia Tziridis ◽  
Daniel Rauh ◽  
Piotr Neumann ◽  
Petr Kolenko ◽  
Anja Menzel ◽  
...  
Keyword(s):  
Binding Site ◽  
Proteinase Inhibitors ◽  
Factor Xa ◽  
Serine Proteinase ◽  
Crystallographic Structure ◽  
Binding Modes ◽  
Ligand Complex ◽  
Structure Based Drug Design ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Abstract A high-resolution crystallographic structure determination of a protein–ligand complex is generally accepted as the ‘gold standard’ for structure-based drug design, yet the relationship between structure and affinity is neither obvious nor straightforward. Here we analyze the interactions of a series of serine proteinase inhibitors with trypsin variants onto which the ligand-binding site of factor Xa has been grafted. Despite conservative mutations of only two residues not immediately in contact with ligands (second shell residues), significant differences in the affinity profiles of the variants are observed. Structural analyses demonstrate that these are due to multiple effects, including differences in the structure of the binding site, differences in target flexibility and differences in inhibitor binding modes. The data presented here highlight the myriad competing microscopic processes that contribute to protein–ligand interactions and emphasize the difficulties in predicting affinity from structure.

scholarly journals ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
Author(s):  
Praveen Anand ◽  
Deepesh Nagarajan ◽  
Sumanta Mukherjee ◽  
Nagasuma Chandra
Keyword(s):  
Binding Site ◽  
In Silico ◽  
Ligand Recognition ◽  
Loss Of Function ◽  
Ligand Complex ◽  
Alanine Scanning Mutagenesis ◽  
Ligand Interaction ◽  
Alanine Scanning ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are computed by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide clues to researchers about residues to obtain loss-of-function mutations and to understand drug resistant mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

scholarly journals ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
Author(s):  
Praveen Anand ◽  
Deepesh Nagarajan ◽  
Sumanta Mukherjee ◽  
Nagasuma Chandra
Keyword(s):  
Binding Site ◽  
In Silico ◽  
Ligand Recognition ◽  
Loss Of Function ◽  
Ligand Complex ◽  
Alanine Scanning Mutagenesis ◽  
Ligand Interaction ◽  
Alanine Scanning ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are calculated by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide a ranked list of residues to the user in order to obtain loss-of-function mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

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.

Using molecular docking and molecular dynamics to investigate protein-ligand interactions

2021 ◽  
Vol 35 (08) ◽  
pp. 2130002
Author(s):  
Connor J. Morris ◽  
Dennis Della Corte
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
State Of The Art ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Art Methods ◽  
Ligand Interactions ◽  
Docking Calculations ◽  
Ligand Bond

Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu
Keyword(s):  
Ligand Binding ◽  
Considerable Improvement ◽  
Ligand Docking ◽  
Water Molecules ◽  
Binding Modes ◽  
Binding Prediction ◽  
Docking Simulations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

scholarly journals Designing interactions by control of protein–ligand complex conformation: tuning arginine–arene interaction geometry for enhanced electrostatic protein–ligand interactions

2018 ◽  
Vol 9 (4) ◽  
pp. 1014-1021 ◽  
Author(s):  
A.-L. Noresson ◽  
O. Aurelius ◽  
C. T. Öberg ◽  
O. Engström ◽  
A. P. Sundin ◽  
...  
Keyword(s):  
Protein Conformation ◽  
Salt Bridges ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Galectin 3 ◽  
Control Protein

3-Benzamido-2-O-sulfo-galactosides can be designed to control protein conformation into forming entropically favourable galectin-3-arginine salt bridges with ligand sulfates.

scholarly journals ProfKin: A Comprehensive Web Server for Structure-based Kinase Selectivity Profiling

2020 ◽  
Author(s):  
Zihao Shen ◽  
Yu-Hang Yan ◽  
Shuo Yang ◽  
Sang Zhu ◽  
Yuan Yuan ◽  
...  
Keyword(s):  
Binding Site ◽  
Drug Targets ◽  
Kinase Inhibitor ◽  
3D Structure ◽  
Web Server ◽  
Binding Modes ◽  
Ligand Complex ◽  
Site Type ◽  
Ligand Type ◽  
Inhibitor Selectivity

Abstract Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase selectivity profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4,219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. For each top-ranked kinase, the additional classification attributes and the phylogenetic tree are given simultaneously.

scholarly journals Identification of Interactions of ABT-341 to Dipeptidyl Peptidase IV during Molecular Dynamics Simulations

2021 ◽  
Vol 7 (2) ◽  
pp. 91-98
Author(s):  
Enade Istyastono ◽  
Michael Gani
Keyword(s):  
Molecular Dynamics ◽  
Design Research ◽  
Hydrophobic Interactions ◽  
Md Simulations ◽  
Dipeptidyl Peptidase ◽  
Dipeptidyl Peptidase Iv ◽  
Structure Based Drug Design ◽  
Protein Ligand Interactions ◽  
Dpp Iv ◽  
Ligand Interactions

Background: Dipeptidyl Peptidase IV (DPP-IV) is an established drug discovery target for type 2 diabetes mellitus (T2DM) therapy. On the other hand, molecular dynamics (MD) simulations have been widely employed to obtain insights of the protein-ligand interactions in structure-based drug design research projects. Moreover, a software to identify protein-ligand interactions called PyPLIF HIPPOS was made publicly available recently. Employing PyPLIF HIPPOS to identify the interactions of DPP-IV and its ligand ABT-341 during MD simulations was then of considerable interest. Objectives: The main aim of this study was to identify protein-ligand interactions of ABT-341 to DPP-IV during MD simulations. Material and Methods: The crystal structure of DPP-IV co-crystallized with ABT-341 obtained from the Protein Data Bank with code of 2I78 was used as the main material. YASARA-Structure was employed for performing 10 ns prodution run MD simulations with snapshots in every 100 ps and PyPLIF HIPPOS was used to identify the protein-ligand interactions. Results: There were 23 interactions involving 13 residues identified by employing PyPLIF HIPPOS during the MD simulations. Two of them identified in all snapshots, i.e., hydrophobic interactions to PHE357 and TYR666. Conclusions: PyPLIF HIPPOS was succesfully employed to identify the interactions of ABT-341 to DPP-IV during MD simulations.

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