Dataset Information

An enumerative algorithm for de novo design of proteins with diverse pocket structures.

ABSTRACT: To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

SUBMITTER: Basanta B

PROVIDER: S-EPMC7486743 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An enumerative algorithm for de novo design of proteins with diverse pocket structures.

Basanta Benjamin B Bick Matthew J MJ Bera Asim K AK Norn Christoffer C Chow Cameron M CM Carter Lauren P LP Goreshnik Inna I Dimaio Frank F Baker David D

Proceedings of the National Academy of Sciences of the United States of America 20200824 36

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined ...[more]

PMID: 32839327

Similar Datasets

Project description:De novo protein design represents an attractive approach for testing and extending our understanding of metalloprotein structure and function. Here, we describe our work on the design of DF (Due Ferri or two-iron in Italian), a minimalist model for the active sites of much larger and more complex natural diiron and dimanganese proteins. In nature, diiron and dimanganese proteins protypically bind their ions in 4-Glu, 2-His environments, and they catalyze diverse reactions, ranging from hydrolysis, to O2-dependent chemistry, to decarbonylation of aldehydes. In the design of DF, the position of each atom-including the backbone, the first-shell ligands, the second-shell hydrogen-bonded groups, and the well-packed hydrophobic core-was bespoke using precise mathematical equations and chemical principles. The first member of the DF family was designed to be of minimal size and complexity and yet to display the quintessential elements required for binding the dimetal cofactor. After thoroughly characterizing its structural, dynamic, spectroscopic, and functional properties, we added additional complexity in a rational stepwise manner to achieve increasingly sophisticated catalytic functions, ultimately demonstrating substrate-gated four-electron reduction of O2 to water. We also briefly describe the extension of these studies to the design of proteins that bind nonbiological metal cofactors (a synthetic porphyrin and a tetranuclear cluster), and a Zn2+/proton antiporting membrane protein. Together these studies demonstrate a successful and generally applicable strategy for de novo metalloprotein design, which might indeed mimic the process by which primordial metalloproteins evolved. We began the design process with a highly symmetrical backbone and binding site, by using point-group symmetry to assemble the secondary structures that position the amino acid side chains required for binding. The resulting models provided a rough starting point and initial parameters for the subsequent precise design of the final protein using modern methods of computational protein design. Unless the desired site is itself symmetrical, this process requires reduction of the symmetry or lifting it altogether. Nevertheless, the initial symmetrical structure can be helpful to restrain the search space during assembly of the backbone. Finally, the methods described here should be generally applicable to the design of highly stable and robust catalysts and sensors. There is considerable potential in combining the efficiency and knowledge base associated with homogeneous metal catalysis with the programmability, biocompatibility, and versatility of proteins. While the work reported here focuses on testing and learning the principles of natural metalloproteins by designing and studying proteins one at a time, there is also considerable potential for using designed proteins that incorporate both biological and nonbiological metal ion cofactors for the evolution of novel catalysts.

Dataset Information

An enumerative algorithm for de novo design of proteins with diverse pocket structures.

Publications

An enumerative algorithm for de novo design of proteins with diverse pocket structures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets