Emergence of Protein Fold Families through Rational Design

By Feng Ding, N. V. Dokholyan. 2006

197 citations recorded by the source corpus.

Diverse proteins with similar structures are grouped into families of homologs and analogs, if their sequence similarity is higher or lower, respectively, than 20%–30%. It was suggested that protein homologs and analogs originate from a common ancestor and diverge in their distinct evolutionary time scales, emerging as a consequence of the physical properties of the protein sequence space. Although a number of studies have determined key signatures of protein family organization, the sequence-structure factors that differentiate the two evolution-related protein families remain unknown. Here, we stipulate that subtle structural changes, which appear due to accumulating mutations in the homologous families, lead to distinct packing of the protein core and, thus, novel compositions of core residues. The latter process leads to the formation of distinct families of homologs. We propose that such differentiation results in the formation of analogous families. To test our postulate, we developed a molecular modeling and design toolkit, Medusa, to computationally design protein sequences that correspond to the same fold family. We find that analogous proteins emerge when a backbone structure deviates only 1–2 Å root-mean-square deviation from the original structure. For close homologs, core residues are highly conserved. However, when the overall sequence similarity drops to ~25%–30%, the composition of core residues starts to diverge, thereby forming novel families of protein homologs. This direct observation of the formation of protein homologs within a specific fold family supports our hypothesis. The conservation of amino acids in designed sequences recapitulates that of the naturally occurring sequences, thereby validating our computational design methodology.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “For this chapter, we used the test set of 38 large proteins from Ding and Dokholyan (2006).”
Automated Minimization of Steric Clashes in Protein Structures (2011, methodology): “In the web server, any given input structure is first examined for missing atoms— missing side-chain atoms are reconstructed using Medusa,17,18 which uses Dunbrack rotamer libraries.”
A Generic Program for Multistate Protein Design (2011, methodology): “The missing rotamer problem could be avoided entirely if, instead of performing discrete rotamer optimization, we performed continuous rotamer optimization [62,63], but this would surely come at the price of longer running times since rotamer-pair energies could not be saved and reused.”
Rapid flexible docking using a stochastic rotamer library of ligands (2010, methodology): “27,32,33 In principle, we can model ligand conformational ﬂexibility in the same way as amino acids using the ligand rotamers of STROLL.”