scholarly journals EXTRACTION, QUANTIFICATION AND VISUALIZATION OF PROTEIN POCKETS

Author(s):  
Xiaoyu Zhang ◽  
Chandrajit Bajaj
Keyword(s):  
Nanoscale ◽  
2021 ◽  
Author(s):  
Matias Luis Picchio ◽  
Julian Bergueiro Álvarez ◽  
Stefanie Wedepohl ◽  
Roque J Minari ◽  
Cecilia Ines Alvarez Igarzabal ◽  
...  

After several decades of development in the field of near-infrared (NIR) dyes for photothermal therapy (PTT), indocyanine green (ICG) still remains the only FDA-approved NIR contrast agent. However, upon NIR...


2016 ◽  
Vol 38 (15) ◽  
pp. 1252-1259 ◽  
Author(s):  
Sam Tonddast-Navaei ◽  
Bharath Srinivasan ◽  
Jeffrey Skolnick

2018 ◽  
Author(s):  
Sebastian Daberdaku

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.


1988 ◽  
Vol 9 (1) ◽  
pp. 10-11 ◽  
Author(s):  
Jim Elliott
Keyword(s):  

2010 ◽  
Vol 50 (4) ◽  
pp. 589-603 ◽  
Author(s):  
Ryan G. Coleman ◽  
Kim A. Sharp
Keyword(s):  

2010 ◽  
Vol 9 (12) ◽  
pp. 6498-6510 ◽  
Author(s):  
Felix Reisen ◽  
Martin Weisel ◽  
Jan M. Kriegl ◽  
Gisbert Schneider

2020 ◽  
Author(s):  
Michael L. Drummond ◽  
Andrew Henry ◽  
Huifang Li ◽  
Christopher I. Williams

ABSTRACTExtending upon our previous publication (Drummond and Williams, J. Chem. Inf. Model. 2019, 59, 1634), in this work two additional computational methods are presented to model PROTAC-mediated ternary complex structures, which are then used to predict the efficacy of any accompanying protein degradation. Method 4B, an extension to one of our previous approaches, incorporates a clustering procedure uniquely suited for considering ternary complexes. Method 4B yields the highest proportion to date of crystal-like poses in modeled ternary complex ensembles, nearing 100% in two cases and always giving a hit rate of at least 10%. Techniques to further improve this performance for particularly troublesome cases are suggested and validated. This demonstrated ability to reliably reproduce known crystallographic ternary complex structures is further established through modeling of a newly released crystal structure. Moreover, for the far more common scenario where the structure of the ternary complex intermediate is unknown, the methods detailed in this work nonetheless consistently yield results that reliably follow experimental protein degradation trends, as established through seven retrospective case studies. These various case studies cover challenging yet common modeling situations, such as when the precise orientation of the PROTAC binding moiety in one (or both) of the protein pockets has not been experimentally established. Successful results are presented for one PROTAC targeting many proteins, for different possible PROTACs targeting the same protein, and even for degradation effected by an E3 ligase that has not been structurally characterized in a ternary complex. Overall, the computational modeling approaches detailed in this work should greatly facilitate PROTAC screening and design efforts, so that the many advantages of a PROTAC-based degradation approach can be effectively utilized both rapidly and at reduced cost.


2019 ◽  
Author(s):  
Sebastian Daberdaku

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This research presents PoCavEDT, an automated purely geometric technique for the identification of binding pockets and occluded cavities in proteins based on the 3D Euclidean Distance Transform. Candidate protein pocket regions are identified between two Solvent-Excluded surfaces generated with the Euclidean Distance Transform using different probe spheres, which depend on the size of the binding ligand. The application of simple, yet effective geometrical heuristics ensures that the proposed method obtains very good ligand binding site prediction results. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities. Its performance was compared to the results achieved with several purely geometric pocket and cavity prediction methods, namely SURFNET, PASS, CAST, LIGSITE, LIGSITECS, PocketPicker and POCASA. Success rates PoCavEDT were comparable to those of POCASA and outperformed the other software.


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