Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Author(s):  
Ammu Prasanna Kumar ◽  
Chandra S Verma ◽  
Suryani Lukman

Abstract Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

2019 ◽  
Author(s):  
Roy González-Alemán ◽  
Nicolas Chevrollier ◽  
Manuel Simoes ◽  
Luis Montero-Cabrera ◽  
Fabrice Leclerc

AbstractComputational fragment-based approaches are widely used in drug design and drug discovery. One of the limitations of their application is the lack of performance of docking methods, mainly the scoring functions. With the emergence of new fragment-based approaches for single-stranded RNA ligands, we propose an analysis of an MCSS-based approach evaluated for its docking power on nucleotide-binding sites. Hybrid solvent models based on some partial explicit representation are shown to improve docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. The results suggest that we can apply the approach to the fragment-based design of sequence-selective oligonucleotides.


Author(s):  
Cristian Privat ◽  
Jose M. Granadino-Roldan ◽  
Jordi Bonet ◽  
Maria Santos Tomas ◽  
Juan Jesús Pérez ◽  
...  

Diverse computational methods to support Fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy to support FBDD campaigns, they exhibit some drawbacks such as protein denaturation...


2012 ◽  
Vol 4 (15) ◽  
pp. 1971-1979 ◽  
Author(s):  
Anna Vulpetti ◽  
Tuomo Kalliokoski ◽  
Francesca Milletti

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 651
Author(s):  
Koji Umezawa ◽  
Isao Kii

Drug discovery using small molecule inhibitors is reaching a stalemate due to low selectivity, adverse off-target effects and inevitable failures in clinical trials. Conventional chemical screening methods may miss potent small molecules because of their use of simple but outdated kits composed of recombinant enzyme proteins. Non-canonical inhibitors targeting a hidden pocket in a protein have received considerable research attention. Kii and colleagues identified an inhibitor targeting a transient pocket in the kinase DYRK1A during its folding process and termed it FINDY. FINDY exhibits a unique inhibitory profile; that is, FINDY does not inhibit the fully folded form of DYRK1A, indicating that the FINDY-binding pocket is hidden in the folded form. This intriguing pocket opens during the folding process and then closes upon completion of folding. In this review, we discuss previously established kinase inhibitors and their inhibitory mechanisms in comparison with FINDY. We also compare the inhibitory mechanisms with the growing concept of “cryptic inhibitor-binding sites.” These sites are buried on the inhibitor-unbound surface but become apparent when the inhibitor is bound. In addition, an alternative method based on cell-free protein synthesis of protein kinases may allow the discovery of small molecules that occupy these mysterious binding sites. Transitional folding intermediates would become alternative targets in drug discovery, enabling the efficient development of potent kinase inhibitors.


2020 ◽  
Vol 21 (20) ◽  
pp. 7702 ◽  
Author(s):  
Sofya I. Scherbinina ◽  
Philip V. Toukach

Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.


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