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Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity.

ABSTRACT: In this article we extend the Iterative Protein Redesign and Optimization (IPRO) framework for the design of protein libraries with targeted ligand specificity. Mutations that minimize the binding energy with the desired ligand are identified. At the same time explicit constraints are introduced that maintain the binding energy for all decoy ligands above a threshold necessary for successful binding. The proposed framework is demonstrated by computationally altering the effector binding specificity of the bacterial transcriptional regulatory protein AraC, belonging to the AraC/XylS family of transcriptional regulators for different unnatural ligands. The obtained results demonstrate the importance of systematically suppressing the binding energy for competing ligands. Pinpointing a small set of mutations within the binding pocket greatly improves the difference in binding energies between targeted and decoy ligands, even when they are very similar.

SUBMITTER: Fazelinia H

PROVIDER: S-EPMC1861794 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity.

Fazelinia Hossein H Cirino Patrick C PC Maranas Costas D CD

Biophysical journal 20070105 6

In this article we extend the Iterative Protein Redesign and Optimization (IPRO) framework for the design of protein libraries with targeted ligand specificity. Mutations that minimize the binding energy with the desired ligand are identified. At the same time explicit constraints are introduced that maintain the binding energy for all decoy ligands above a threshold necessary for successful binding. The proposed framework is demonstrated by computationally altering the effector binding specific ...[more]

PMID: 17208966

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Project description:The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.

Dataset Information

Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity.

Publications

Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity.

Similar Datasets

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