scholarly journals Novel Strategies for Drug Discovery Based on Intrinsically Disordered Proteins (IDPs)

2011 ◽  
Vol 12 (5) ◽  
pp. 3205-3219 ◽  
Author(s):  
Jihua Wang ◽  
Zanxia Cao ◽  
Liling Zhao ◽  
Shuqiang Li
Keyword(s):  
Drug Discovery ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Intrinsically Disordered

Mathematical and Computational Techniques for Drug Discovery: Promises and Developments

2019 ◽  
Vol 18 (32) ◽  
pp. 2774-2799 ◽  
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Quantum Chemical ◽  
Intrinsically Disordered Proteins ◽  
Hepatitis Virus ◽  
Disordered Proteins ◽  
Computational Techniques ◽  
Large Set ◽  
Structural Protein ◽  
Intrinsically Disordered

We review various mathematical and computational techniques for drug discovery exemplifying some recent works pertinent to group theory of nested structures of relevance to phylogeny, topological, computational and combinatorial methods for drug discovery for multiple viral infections. We have reviewed techniques from topology, combinatorics, graph theory and knot theory that facilitate topological and mathematical characterizations of protein-protein interactions, molecular-target interactions, proteomics, genomics and statistical data reduction procedures for a large set of starting chemicals in drug discovery. We have provided an overview of group theoretical techniques pertinent to phylogeny, protein dynamics especially in intrinsically disordered proteins, DNA base permutations and related algorithms. We consider computational techniques derived from high level quantum chemical computations such as QM/MM ONIOM methods, quantum chemical optimization of geometries complexes, and molecular dynamics methods for providing insights into protein-drug interactions. We have considered complexes pertinent to Hepatitis Virus C non-structural protein 5B polymerase receptor binding of C5-Arylidebne rhodanines, complexes of synthetic potential vaccine molecules with dengue virus (DENV) and HIV-1 virus as examples of various simulation studies that exemplify the utility of computational tools. It is demonstrated that these combinatorial and computational techniques in conjunction with experiments can provide promising new insights into drug discovery. These techniques also demonstrate the need to consider a new multiple site or allosteric binding approach to drug discovery, as these studies reveal the existence of multiple binding sites.

ChemInform Abstract: Intrinsically Disordered Proteins: From Sequence and Conformational Toward Drug Discovery

ChemInform ◽  
2012 ◽  
Vol 43 (29) ◽  
pp. no-no
Author(s):  
Nasrollah Rezaei-Ghaleh ◽  
Martin Blackledge ◽  
Markus Zweckstetter
Keyword(s):  
Drug Discovery ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Intrinsically Disordered

scholarly journals Drug Discovery Targeting the Disorder-To-Order Transition Regions through the Conformational Diversity Mimicking and Statistical Analysis

2020 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Insung Na ◽  
Sungwoo Choi ◽  
Seung Han Son ◽  
Vladimir N. Uversky ◽  
Chul Geun Kim
Keyword(s):  
Statistical Analysis ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Binding Site ◽  
Electrostatic Interaction ◽  
Intrinsically Disordered Proteins ◽  
Score Function ◽  
Order Transition ◽  
Disordered Proteins ◽  
Intrinsically Disordered

Intrinsically disordered proteins exist as highly dynamic conformational ensembles of diverse forms. However, the majority of virtual screening only focuses on proteins with defined structures. This means that computer-aided drug discovery is restricted. As a breakthrough, understanding the structural characteristics of intrinsically disordered proteins and its application can open the gate for unrestricted drug discovery. First, we segmented the target disorder-to-order transition region into a series of overlapping 20-amino-acid-long peptides. Folding prediction generated diverse conformations of these peptides. Next, we applied molecular docking, new evaluation score function, and statistical analysis. This approach successfully distinguished known compounds and their corresponding binding regions. Especially, Myc proto-oncogene protein (MYC) inhibitor 10058F4 was well distinguished from others of the chemical compound library. We also studied differences between the two Methyl-CpG-binding domain protein 2 (MBD2) inhibitors (ABA (2-amino-N-[[(3S)-2,3-dihydro-1,4-benzodioxin-3-yl]methyl]-acetamide) and APC ((R)-(3-(2-Amino-acetylamino)-pyrrolidine-1-carboxylic acid tert-butyl ester))). Both compounds bind MBD2 through electrostatic interaction behind its p66α-binding site. ABA is also able to bind p66α through electrostatic interaction behind its MBD2-binding site while APC-p66α binding was nonspecific. Therefore, structural heterogeneity mimicking of the disorder-to-order transition region at the peptide level and utilization of the new docking score function represent a useful approach that can efficiently discriminate compounds for expanded virtual screening toward intrinsically disordered proteins.

scholarly journals Identification of ligand binding sites in intrinsically disordered proteins with a differential binding score

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qiao-Hong Chen ◽  
V. V. Krishnan
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Sites ◽  
Intrinsically Disordered Proteins ◽  
Random Coil ◽  
Disordered Proteins ◽  
Intrinsically Disordered ◽  
Binding Score ◽  
Ligand Binding Sites ◽  
Differential Binding

AbstractScreening ligands directly binding to an ensemble of intrinsically disordered proteins (IDP) to discover potential hits or leads for new drugs is an emerging but challenging area as IDPs lack well-defined and ordered 3D-protein structures. To explore a new IDP-based rational drug discovery strategy, a differential binding score (DIBS) is defined. The basis of DIBS is to quantitatively determine the binding preference of a ligand to an ensemble of conformations specified by IDP versus such preferences to an ensemble of random coil conformations of the same protein. Ensemble docking procedures performed on repeated sampling of conformations, and the results tested for statistical significance determine the preferential ligand binding sites of the IDP. The results of this approach closely reproduce the experimental data from recent literature on the binding of the ligand epigallocatechin gallate (EGCG) to the intrinsically disordered N-terminal domain of the tumor suppressor p53. Combining established approaches in developing a new method to screen ligands against IDPs could be valuable as a screening tool for IDP-based drug discovery.

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