Considerations of Protein Subpockets in Fragment‐Based Drug Design

Bartolowits, Matthew; Davisson, V. Jo

doi:10.1111/cbdd.12631

Cited by 14 publications

(18 citation statements)

References 156 publications

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“…In addition to increasing the throughput of iterative screens, multifragment-binding approaches can enhance second and/or third fragment effects in environments where induced-fit or conformational selection are operative. 23 This can be true when searching for novel fragment pairs demonstrated by the de novo discovery of NLys-NPip-NBal. Previous studies were able to identify the small-molecule T2AA as an inhibitor in cellular systems.…”

Section: Discussionmentioning

confidence: 99%

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

et al. 2019

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Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein–protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA–protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on N-alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA–protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein–protein interactions, demonstrating the method’s utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

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Section: Discussionmentioning

confidence: 99%

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

et al. 2019

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“…Cavity segmentation into subcavities is crucial in the era of structurebased drug discovery, with medicinal chemists attempting to improve potency and selectivity of their hits by filling protein subpockets. 29,[52][53][54][55] Similar proteins may have binding pockets with different subcavities and dissimilar proteins may have conserved subcavities. Many drug targets exhibit well-defined subpockets geometrically, such as proteases, kinases and GPCRs, which are used extensively in order to develop selective compouds.…”

Section: Enclosure Of Grid Pointsmentioning

confidence: 99%

“…Many drug targets exhibit well-defined subpockets geometrically, such as proteases, kinases and GPCRs, which are used extensively in order to develop selective compouds. 53 In addition, two independent studies concluded that drug-like ligands typically occupy about a third of their binding pockets, filling only some of the subpockets. 56,57 Efforts have been made to try to characterize the chemical fragment preference of certain residues, 58,59 and link the fragment chemical space to binding pocket microenvironments.…”

Section: Enclosure Of Grid Pointsmentioning

confidence: 99%

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Marchand¹,

Pirard²,

Ertl³

et al. 2020

Preprint

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<div>Motivation: The detection of small molecules binding sites in proteins is central to structure based</div><div>drug design. Many tools were developed in the last 40 years, but only few of them are available</div><div>today, open-source, and suitable for the analysis of large databases or for the integration in</div><div>automatic workflows. In addition, no software can characterize subpockets solely with the</div><div>information of the protein structure, a pivotal concept in fragment-based drug design.</div><div>Results: CAVIAR is a new open source tool for protein cavity identification and rationalization.</div><div>Protein pockets are automatically detected based on the protein structure. It comprises a subcavity</div><div>segmentation algorithm that decomposes binding sites into subpockets without requiring the</div><div>presence of a ligand. The defined subpockets mimick the empirical definitions of subpockets in</div><div>medicinal chemistry projects. A tool like CAVIAR may be valuable to support chemical biology,</div><div>medicinal chemistry and ligand identification efforts. Our analysis of the entire PDB and the</div><div>PDBBind confirms that liganded cavities tend to be bigger, more hydrophobic and more complex</div><div>than apo cavities. Moreover, in line with the paradigm of fragment-based drug design, the binding</div><div>affinity scales relatively well with the number of subcavities filled by the ligand. Compounds</div><div>binding to more than three of the subcavities identified by CAVIAR are mostly in the nanomolar</div><div>or better range of affinities to their target.</div><div>Availability and implementation: Installation notes, user manual and support for CAVIAR are</div><div>available at https://jr-marchand.github.io/caviar/. The CAVIAR GUI and CAVIAR command line</div><div>tool are available on GitHub at https://github.com/jr-marchand/caviar and the package is hosted</div><div>on Anaconda cloud at https://anaconda.org/jr-marchand/caviar under a MIT license. The GitHub</div><div>repository also hosts the validation datasets.</div>

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“…Instead of the weak binding of the compound, fragments are highly efficient ligands and display a high binding affinity per heavy atom. [9,22,23] Furthermore, it is also possible to supplement an existing structure with known affinity by the biochemical target, such as a substrate, or a drug already present in the therapeutic to increase the affinity. Then, the fragments could be modified or linked to yield potent inhibitors.…”

Section: Successful Applicationsmentioning

confidence: 99%

“…Then, the fragments could be modified or linked to yield potent inhibitors. [9,22,23] Furthermore, it is also possible to supplement an existing structure with known affinity by the biochemical target, such as a substrate, or a drug already present in the therapeutic to increase the affinity. [24] From the analysis of the affinity between a series of fragments and the target, it is possible to extrapolate the affinity of compounds formed using these fragments.…”

Section: Successful Applicationsmentioning

confidence: 99%

Fragment‐based drug discovery as alternative strategy to the drug development for neglected diseases

et al. 2017

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Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

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Considerations of Protein Subpockets in Fragment‐Based Drug Design

Cited by 14 publications

References 156 publications

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Automatic Cavity Identification and Decomposition into Subpockets with CAVIAR

Fragment‐based drug discovery as alternative strategy to the drug development for neglected diseases

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