Literature DB >> 23541220

A rapid, ensemble and free energy based method for engineering protein stabilities.

Athi N Naganathan1.   

Abstract

Engineering the conformational stabilities of proteins through mutations has immense potential in biotechnological applications. It is, however, an inherently challenging problem given the weak noncovalent nature of the stabilizing interactions. In this regard, we present here a robust and fast strategy to engineer protein stabilities through mutations involving charged residues using a structure-based statistical mechanical model that accounts for the ensemble nature of folding. We validate the method by predicting the absolute changes in stability for 138 experimental mutations from 16 different proteins and enzymes with a correlation of 0.65 and importantly with a success rate of 81%. Multiple point mutants are predicted with a higher success rate (90%) that is validated further by comparing meosphile-thermophile protein pairs. In parallel, we devise a methodology to rapidly engineer mutations in silico which we benchmark against experimental mutations of ubiquitin (correlation of 0.95) and check for its feasibility on a larger therapeutic protein DNase I. We expect the method to be of importance as a first and rapid step to screen for protein mutants with specific stability in the biotechnology industry, in the construction of stability maps at the residue level (i.e., hot spots), and as a robust tool to probe for mutations that enhance the stability of protein-based drugs.

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Year:  2013        PMID: 23541220     DOI: 10.1021/jp401588x

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  7 in total

1.  Selection and analyses of variants of a designed protein suggest importance of hydrophobicity of partially buried sidechains for protein stability at high temperatures.

Authors:  Mingjie Han; Sanhui Liao; Xiong Peng; Xiaoqun Zhou; Quan Chen; Haiyan Liu
Journal:  Protein Sci       Date:  2019-05-23       Impact factor: 6.725

2.  pStab: prediction of stable mutants, unfolding curves, stability maps and protein electrostatic frustration.

Authors:  Soundhararajan Gopi; Devanshu Devanshu; Praveen Krishna; Athi N Naganathan
Journal:  Bioinformatics       Date:  2018-03-01       Impact factor: 6.937

3.  Engineering folding dynamics from two-state to downhill: application to λ-repressor.

Authors:  James W Carter; Christopher M Baker; Robert B Best; David De Sancho
Journal:  J Phys Chem B       Date:  2013-10-22       Impact factor: 2.991

4.  Controlling Structure and Dimensions of a Disordered Protein via Mutations.

Authors:  Sneha Munshi; Divya Rajendran; Samyuktha Ramesh; Sandhyaa Subramanian; Kabita Bhattacharjee; Meagha Ramana Kumar; Athi N Naganathan
Journal:  Biochemistry       Date:  2019-09-26       Impact factor: 3.162

5.  Tunable order-disorder continuum in protein-DNA interactions.

Authors:  Sneha Munshi; Soundhararajan Gopi; Gitanjali Asampille; Sandhyaa Subramanian; Luis A Campos; Hanudatta S Atreya; Athi N Naganathan
Journal:  Nucleic Acids Res       Date:  2018-09-28       Impact factor: 16.971

6.  Thermodynamics and folding landscapes of large proteins from a statistical mechanical model.

Authors:  Soundhararajan Gopi; Akashnathan Aranganathan; Athi N Naganathan
Journal:  Curr Res Struct Biol       Date:  2019-10-23

7.  A disorder-induced domino-like destabilization mechanism governs the folding and functional dynamics of the repeat protein IκBα.

Authors:  Srinivasan Sivanandan; Athi N Naganathan
Journal:  PLoS Comput Biol       Date:  2013-12-19       Impact factor: 4.475

  7 in total

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