De novo protein design by citizen scientists

Abstract

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have been very successful for data collection, annotation, and processing, but for the most part have harnessed human pattern recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure with an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top scoring solutions, and subsequent game improvement, Foldit players can now, starting from an extended polypeptide chain, generate a diversity of protein structures and sequences which encode them in silico. 146 Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed in E. coli with good solubility and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge contributing to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges, such as the protein design problem.

Protein structure prediction assisted with sparse NMR data in CASP13

“…Two real NMR data sets (N1008 and n1008) were provided for the data‐guided prediction program of CASP13. Both data sets were for the CASP COMMONS target T1008 (foldit3), proposed by Brian Koepnick and David Baker as part of their project assessing de novo protein design by citizen scientists in the online protein‐folding game Foldit . Foldit players were provided a set of general principles for protein design in the form of Foldit rules, and the resulting designs were assessed by Rosetta stability calculations.…”

“…Foldit players were provided a set of general principles for protein design in the form of Foldit rules, and the resulting designs were assessed by Rosetta stability calculations. One hundred and fifty‐six designs were encoded in synthetic genes, which were expressed, screened for stability, and (in four cases) experimental structures were determined . One of the protein designs, the 80‐residue foldit3 protein, was produced for CASP13 with uniform 15 N, 13 C‐enrichment, and its structure was determined by conventional triple‐resonance NMR in the context of this project.…”

“…NMR studies were performed using a uniformly 15 N, 13 C‐enriched sample of a de novo designed protein, named foldit3, CASP13 target 1008. The synthetic codon optimized gene (Genscript, Inc), designed to exclude ACA nucleotide sequences, , was cloned into plasmid pET15TEV_NESG .…”

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

“…The Montelione group cloned and expressed these, with the goal of determining which are suitable for NMR structure determination, and in parallel the CASP community was invited to submit models. So far one experimental structure has been obtained . A new round of modeling is now beginning, using the new free modeling methods from CASP13.…”

“…So far one experimental structure has been obtained. 38 A new round of modeling is now beginning, using the new free modeling methods from CASP13.…”