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.

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.

scholarly journals Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

2019 ◽  
Vol 73 (12) ◽  
pp. 713-725 ◽  
Author(s):  
Ruth Hendus-Altenburger ◽  
Catarina B. Fernandes ◽  
Katrine Bugge ◽  
Micha B. A. Kunze ◽  
Wouter Boomsma ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Secondary Structure ◽  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Random Coil ◽  
Disordered Proteins ◽  
Phosphoryl Group ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Secondary Chemical

Abstract Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.

scholarly journals Structural elucidation of ligand binding sites in family B GPCRs and their application in drug discovery 

2016 ◽  
Vol 72 (a1) ◽  
pp. s24-s24
Author(s):  
Ali Jazayeri ◽  
Andrew Doré ◽  
Daniel Lamb ◽  
Harini Krishnamurthy ◽  
Stacey Southall ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Elucidation ◽  
Ligand Binding Sites

scholarly journals CheSPI: Chemical shift Secondary structure Population Inference

2021 ◽  
Author(s):  
Jakob Toudahl Nielsen ◽  
Frans A.A. Mulder
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Random Coil ◽  
Disordered Proteins ◽  
Residual Structure ◽  
Intrinsically Disordered ◽  
Population Inference ◽  
Local Protein Structure ◽  
Structural Classes

AbstractNMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI can assign up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

scholarly journals Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions

2021 ◽  
Vol 8 ◽  
Author(s):  
Irina Ritsch ◽  
Laura Esteban-Hofer ◽  
Elisabeth Lehmann ◽  
Leonidas Emmanouilidis ◽  
Maxim Yulikov ◽  
...  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Distance Distribution ◽  
Protein Domain ◽  
Random Coil ◽  
Disordered Proteins ◽  
Primary Data ◽  
Spin Labels ◽  
Conformational Ensemble ◽  
Intrinsically Disordered ◽  
Distance Distributions

Function of intrinsically disordered proteins may depend on deviation of their conformational ensemble from that of a random coil. Such deviation may be hard to characterize and quantify, if it is weak. We explored the potential of distance distributions between spin labels, as they can be measured by electron paramagnetic resonance techniques, for aiding such characterization. On the example of the intrinsically disordered N-terminal domain 1–267 of fused in sarcoma (FUS) we examined what such distance distributions can and cannot reveal on the random-coil reference state. On the example of the glycine-rich domain 188–320 of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) we studied whether deviation from a random-coil ensemble can be robustly detected with 19 distance distribution restraints. We discuss limitations imposed by ill-posedness of the conversion of primary data to distance distributions and propose overlap of distance distributions as a fit criterion that can tackle this problem. For testing consistency and size sufficiency of the restraint set, we propose jack-knife resampling. At current desktop computers, our approach is expected to be viable for domains up to 150 residues and for between 10 and 50 distance distribution restraints.

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