Expanding the space of protein geometries by computational design of de novo fold families

By Xingjie Pan, Michael J. Thompson, Yang Zhang, Lin Liu, James S. Fraser, M. Kelly, Tanja Kortemme. 2020

63 citations recorded by the source corpus.

Exploring the design landscape Protein design typically selects a protein topology and then identifies the geometries (secondary-structure lengths and orientations) that give the most stable structures. A challenge for this approach is that functional sites in natural proteins often adopt nonideal geometries. Pan et al. addressed this issue by exploring the diversity of geometries that can be sampled by a given topology. They developed a computational method called LUCS that systematically samples geometric variation in loop-helix-loop elements and applied it to two different topologies. This method generated families of well-folded proteins that include structures with non-native geometries. The ability to tune protein geometry may enable the custom design of new functions. Science, this issue p. 1132 A computational method to systematically sample loop-helix-loop geometries expands the structure space of designer proteins. Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Sampling of structure and sequence space of small protein folds (2021, methodology): “Chemical shifts, structural restraints, and atomic coordinates have been deposited in the BMRB (entry 30844) and the PDB (PDB ID 7LDF).”
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022, unknown): “…where a particular fold or architecture is desired (such as TIM barrels or cavity-containing NTF2s for small molecule binder and enzyme design 25,26 ), we further fine-tuned RF diffusion to condition on (partial) input secondary structure and/or fold information, enabling rapid and accurate…”
Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, methodology): “For the second dataset (“Monomer dataset”), we have curated a selection of 518 de novo proteins from several studies and spanning a wide range of folds (α, β, and mixed α/β) that were designed to be stable monomers without function (Kuhlman et al., 2003; Koga et al., 2012; Brunette et al., 2015; Doy”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “The recently developed loop-helix-loop unit combinatorial sampling method systematically samples loop-helix-loop geometries in arbitrary protein folds by near exhaustive testing of combinations of short loops (32) (Fig.”