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.

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.

NMR probing and visualization of correlated structural fluctuations in intrinsically disordered proteins

2017 ◽  
Vol 19 (16) ◽  
pp. 10651-10656 ◽  
Author(s):  
Dennis Kurzbach ◽  
Andreas Beier ◽  
Agathe Vanas ◽  
Andrea G. Flamm ◽  
Gerald Platzer ◽  
...  
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Statistical Analysis ◽  
Magnetic Resonance ◽  
Intrinsically Disordered Proteins ◽  
Paramagnetic Relaxation ◽  
Disordered Proteins ◽  
Correlation Matrices ◽  
Intrinsically Disordered ◽  
Nmr Data ◽  
Structural Fluctuations

A novel statistical analysis of paramagnetic relaxation enhancement (PRE) and paramagnetic relaxation interference (PRI) based nuclear magnetic resonance (NMR) data is proposed based on the computation of correlation matrices.

scholarly journals Protein disorder-to-order transition enhances the nucleosome-binding affinity of H1

2020 ◽  
Vol 48 (10) ◽  
pp. 5318-5331 ◽  
Author(s):  
Akshay Sridhar ◽  
Modesto Orozco ◽  
Rosana Collepardo-Guevara
Keyword(s):  
Binding Affinity ◽  
Binding Proteins ◽  
Intrinsically Disordered Proteins ◽  
Order Transition ◽  
Disordered Proteins ◽  
Chromatin Binding ◽  
Intrinsically Disordered ◽  
Terminal Domain ◽  
Wide Range ◽  
Nucleosome Binding

Abstract Intrinsically disordered proteins are crucial elements of chromatin heterogenous organization. While disorder in the histone tails enables a large variation of inter-nucleosome arrangements, disorder within the chromatin-binding proteins facilitates promiscuous binding to a wide range of different molecular targets, consistent with structural heterogeneity. Among the partially disordered chromatin-binding proteins, the H1 linker histone influences a myriad of chromatin characteristics including compaction, nucleosome spacing, transcription regulation, and the recruitment of other chromatin regulating proteins. Although it is now established that the long C-terminal domain (CTD) of H1 remains disordered upon nucleosome binding and that such disorder favours chromatin fluidity, the structural behaviour and thereby the role/function of the N-terminal domain (NTD) within chromatin is yet unresolved. On the basis of microsecond-long parallel-tempering metadynamics and temperature-replica exchange atomistic molecular dynamics simulations of different H1 NTD subtypes, we demonstrate that the NTD is completely unstructured in solution but undergoes an important disorder-to-order transition upon nucleosome binding: it forms a helix that enhances its DNA binding ability. Further, we show that the helical propensity of the H1 NTD is subtype-dependent and correlates with the experimentally observed binding affinity of H1 subtypes, suggesting an important functional implication of this disorder-to-order transition.

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 Proteus: A Random Forest Classifier to Predict Disorder-to-Order Transitioning Binding Regions in Intrinsically Disordered Proteins

2016 ◽  
Author(s):  
Sankar Basu ◽  
Fredrik Söderquist ◽  
Björn Wallner
Keyword(s):  
Amino Acid ◽  
Random Forest ◽  
Amino Acid Sequence ◽  
Intrinsically Disordered Proteins ◽  
Random Forest Classifier ◽  
Amino Acid Sequences ◽  
Order Transition ◽  
Disordered Proteins ◽  
Data Set ◽  
Intrinsically Disordered

AbstractThe focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs / IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP / IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents 'Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55% vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible 'disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.Software Availabilityhttps://github.com/bjornwallner/proteus

scholarly journals A comprehensive ensemble model for comparing the allosteric effect of ordered and disordered proteins

2018 ◽  
Author(s):  
Luhao Zhang ◽  
Maodong Li ◽  
Zhirong Liu
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Allosteric Regulation ◽  
Order Transition ◽  
Disordered Proteins ◽  
Regulation Mechanism ◽  
Ensemble Model ◽  
Allosteric Effect ◽  
Allosteric Coupling ◽  
Intrinsically Disordered

AbstractIntrinsically disordered proteins/regions (IDPs/IDRs) are prevalent in allosteric regulation. It was previously thought that intrinsic disorder is favorable for maximizing the allosteric coupling. Here, we propose a comprehensive ensemble model to compare the roles of both order-order transition and order-disorder transition in allosteric effect. It is revealed that the MWC pathway (order-order transition) has a higher probability than the EAM pathway (disorder-order transition) in allostery, suggesting a complicated role of IDPs/IDRs in regulatory proteins. In addition, an analytic formula for the maximal allosteric coupling response is obtained, which shows that too stable or too unstable state is unfavorable to endow allostery, and is thus helpful for rational design of allosteric drugs.Author SummaryAllosteric effect is an important regulation mechanism in biological processes, where the binding of a ligand at one site of a protein influences the function of a distinct site. Conventionally, allostery was thought to originate from structural transition. However, in recent years, intrinsically disordered proteins (IDPs) were found to be widely involved in allosteric regulation in despite of their lack of ordered structure under physiological condition. It is still a mystery why IDPs are prevalent in allosteric proteins and how they differ from ordered proteins in allostery. Here, we propose a comprehensive ensemble model which includes both ordered and disordered states of a two-domain protein, and investigate the role of various state combinations in allosteric effect. By sampling the parameter space, we conclude that disordered proteins are less competitive than ordered proteins in performing allostery from a thermodynamic point of view. The prevalence of IDPs in allosteric regulation is likely determined by all their advantage, but not only by their capacity in endowing allostery.

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