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Structural hot spots for the solubility of globular proteins

TLDR
It is shown that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility.
Abstract
Natural selection shapes protein solubility to physiological requirements and recombinant applications that require higher protein concentrations are often problematic. This raises the question whether the solubility of natural protein sequences can be improved. We here show an anti-correlation between the number of aggregation prone regions (APRs) in a protein sequence and its solubility, suggesting that mutational suppression of APRs provides a simple strategy to increase protein solubility. We show that mutations at specific positions within a protein structure can act as APR suppressors without affecting protein stability. These hot spots for protein solubility are both structure and sequence dependent but can be computationally predicted. We demonstrate this by reducing the aggregation of human α-galactosidase and protective antigen of Bacillus anthracis through mutation. Our results indicate that many proteins possess hot spots allowing to adapt protein solubility independently of structure and function. Mutations in aggregation prone regions of recombinant proteins often improve their solubility, although they might cause negative effects on their structure and function. Here, the authors identify proteins hot spots that can be exploited to optimize solubility without compromising stability.

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Principles of Protein Stability and Their Application in Computational Design

TL;DR: The computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability, such as thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability as mentioned in this paper.
Journal ArticleDOI

Prediction and Reduction of the Aggregation of Monoclonal Antibodies

TL;DR: This work rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues, showing that reducing protein aggregation is beneficial throughout the development process.
Journal ArticleDOI

Computational Design of Stable and Soluble Biocatalysts

TL;DR: This work designs optimized protein sequences that maximize stability, solubility, and activity over a wide range of temperatures and pH values in buffers of different composition and in the presence of organic cosolvents.
Journal ArticleDOI

Prediction and interpretation of deleterious coding variants in terms of protein structural stability.

TL;DR: A stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing.
Journal ArticleDOI

Water follows polar and nonpolar protein surface domains.

TL;DR: The results demonstrate that the water−amino acid degree of correlation follows the same trend as the amino acid contribution in proteins solubility, namely, the negatively charged amino acids are the most beneficial for protein solubilty, then the positively charge amino acids, and finally the charge-neutral amino acids.
References
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Journal ArticleDOI

Features and development of Coot.

TL;DR: Coot is a molecular-graphics program designed to assist in the building of protein and other macromolecular models and the current state of development and available features are presented.
Journal ArticleDOI

MolProbity: all-atom structure validation for macromolecular crystallography

TL;DR: MolProbity structure validation will diagnose most local errors in macromolecular crystal structures and help to guide their correction.
Journal ArticleDOI

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

TL;DR: Current understanding of the major factors regulating protein expression is summarized to demonstrate a substantial role for regulatory processes occurring after mRNA is made in controlling steady-state protein abundances.
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