Sampling bottlenecks in de novo protein structure prediction

By David E. Kim, A. Cortajarena, B. Blum, Gregg Lois, Philip Bradley, Eilon Sherman, David Baker, Corey S. O'Hern, Lynne Regan, Gilad Haran. 2009

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Unfolded proteins may contain native or non-native residual structure, which has important implications for the thermodynamics and kinetics of folding as well as for misfolding and aggregation diseases. However, it has been universally accepted that residual structure should not affect the global size scaling of the denatured chain, which obeys the statistics of random coil polymers. Here we use a single-molecule optical technique, fluorescence correlation spectroscopy, to probe the denatured state of set of repeat proteins containing an increasing number of identical domains, from two to twenty. The availability of this set allows us to obtain the scaling law for the unfolded state of these proteins, which turns out to be unusually compact, strongly deviating from random-coil statistics. The origin of this unexpected behavior is traced to the presence of extensive non-native polyproline II helical structure, which we localize to specific segments of the polypeptide chain. We show that the experimentally observed effects of PPII on the size scaling of the denatured state can be well-described by simple polymer models. Our findings suggest an hitherto unforeseen potential of non-native structure to induce significant compaction of denatured proteins, affecting significantly folding pathways and kinetics.

Subfields: Cell Biology, Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “molecular dynamics refinement, have been successful at predicting the 3D structures of smaller proteins (,80 residues) [22,24,25,26]”
Protein structure prediction from sequence variation (2012, background): “Clearly, and unfortunately, the de novo structure prediction problem does not scale 13 , the conformational search space increases exponentially as the size of the protein increases, presenting a fundamental computational challenge, even for fragment-based methods 14 .”