DPN-Generated Combinatorial Libraries

2012 ◽  
Author(s):  
Chad Mirkin
2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>


2021 ◽  
Author(s):  
Dennis Larsen ◽  
Sophie R. Beeren

Template-induced kinetic trapping of specific cyclodextrins in enzyme-mediated dynamic combinatorial libraries of linear and cyclic α-glucans enables the one-step synthesis of cyclodextrins from maltose in water.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Simon J. Moore ◽  
Yonek B. Hleba ◽  
Sarah Bischoff ◽  
David Bell ◽  
Karen M. Polizzi ◽  
...  

Abstract Background  A key focus of synthetic biology is to develop microbial or cell-free based biobased routes to value-added chemicals such as fragrances. Originally, we developed the EcoFlex system, a Golden Gate toolkit, to study genes/pathways flexibly using Escherichia coli heterologous expression. In this current work, we sought to use EcoFlex to optimise a synthetic raspberry ketone biosynthetic pathway. Raspberry ketone is a high-value (~ £20,000 kg−1) fine chemical farmed from raspberry (Rubeus rubrum) fruit. Results  By applying a synthetic biology led design-build-test-learn cycle approach, we refactor the raspberry ketone pathway from a low level of productivity (0.2 mg/L), to achieve a 65-fold (12.9 mg/L) improvement in production. We perform this optimisation at the prototype level (using microtiter plate cultures) with E. coli DH10β, as a routine cloning host. The use of E. coli DH10β facilitates the Golden Gate cloning process for the screening of combinatorial libraries. In addition, we also newly establish a novel colour-based phenotypic screen to identify productive clones quickly from solid/liquid culture. Conclusions  Our findings provide a stable raspberry ketone pathway that relies upon a natural feedstock (L-tyrosine) and uses only constitutive promoters to control gene expression. In conclusion we demonstrate the capability of EcoFlex for fine-tuning a model fine chemical pathway and provide a range of newly characterised promoter tools gene expression in E. coli.


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