Dataset Information

Chromodomain Ligand Optimization via Target-Class Directed Combinatorial Repurposing.

ABSTRACT: Efforts to develop strategies for small-molecule chemical probe discovery against the readers of the methyl-lysine (Kme) post-translational modification have been met with limited success. Targeted disruption of these protein-protein interactions via peptidomimetic inhibitor optimization is a promising alternative to small-molecule hit discovery; however, recognition of identical peptide motifs by multiple Kme reader proteins presents a unique challenge in the development of selective Kme reader chemical probes. These selectivity challenges are exemplified by the Polycomb repressive complex 1 (PRC1) chemical probe, UNC3866, which demonstrates submicromolar off-target affinity toward the non-PRC1 chromodomains CDYL2 and CDYL. Moreover, since peptidomimetics are challenging subjects for structure-activity relationship (SAR) studies, traditional optimization of UNC3866 would prove costly and time-consuming. Herein, we report a broadly applicable strategy for the affinity-based, target-class screening of chromodomains via the repurposing of UNC3866 in an efficient, combinatorial peptide library. A first-generation library yielded UNC4991, a UNC3866 analogue that exhibits a distinct selectivity profile while maintaining submicromolar affinity toward the CDYL chromodomains. Additionally, in vitro pull-down experiments from HeLa nuclear lysates further demonstrate the selectivity and utility of this compound for future elucidation of CDYL protein function.

SUBMITTER: Barnash KD

PROVIDER: S-EPMC5026585 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Chromodomain Ligand Optimization via Target-Class Directed Combinatorial Repurposing.

Barnash Kimberly D KD Lamb Kelsey N KN Stuckey Jacob I JI Norris Jacqueline L JL Cholensky Stephanie H SH Kireev Dmitri B DB Frye Stephen V SV James Lindsey I LI

ACS chemical biology 20160714 9

Efforts to develop strategies for small-molecule chemical probe discovery against the readers of the methyl-lysine (Kme) post-translational modification have been met with limited success. Targeted disruption of these protein-protein interactions via peptidomimetic inhibitor optimization is a promising alternative to small-molecule hit discovery; however, recognition of identical peptide motifs by multiple Kme reader proteins presents a unique challenge in the development of selective Kme reader ...[more]

PMID: 27356154

Similar Datasets

Project description:Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (e.g., stability, activity, immunogenicity). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one- and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually isomorphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries--a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 10? variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.

Project description:Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs". Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

Dataset Information

Chromodomain Ligand Optimization via Target-Class Directed Combinatorial Repurposing.

Publications

Chromodomain Ligand Optimization via Target-Class Directed Combinatorial Repurposing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets