The self assembly of proteins; probing patchy protein interactions

Literature Information

Publication Date 2015-01-13
DOI 10.1039/C4CP05892E
Impact Factor 3.676
Authors

Susan James, Michelle K. Quinn, Jennifer J. McManus


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Abstract

The ability to control the self-assembly of biological molecules to form defined structures, with a high degree of predictability is a central aim for soft matter science and synthetic biology. Several examples of this are known for synthetic systems, such as anisotropic colloids. However, for biomacromolecules, such as proteins, success has been more limited, since aeolotopic (or anisotropic) interactions between protein molecules are not easily predicted. We have created three double mutants of human γD-crystallin for which the phase diagrams for singly mutated proteins can be used to predict the behavior of the double mutants. These proteins provide a robust mechanism to examine the kinetic and thermodynamic properties of proteins in which competing interactions exist due to the anisotropic or patchy nature of the protein surface.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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