Modeling Disordered Regions in Proteins Using Rosetta

Abstract

Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.

Isoform-specific interactions of the von Hippel-Lindau tumor suppressor protein

“…A graphical representation of the alignment is reported in Figure S1 . The structure of the pVHL N-terminus, residues 1 to 53, was predicted using the Rosetta ab initio prediction software 28 using a previously described protocol optimized for intrinsically disordered proteins 29 . A total of 25,000 decoys were generated and clustered, with the ten most frequent models visually inspected with Chimera 30 .…”

Evolution of a protein folding nucleus

“…Despite its essential role in protein folding the FN is a poorly understood element of protein primary structure. Simulations of protein folding utilizing simplified lattice models identify the FN as the critical starting point, and seek strategies to identify it; however, accurate prediction of the location of the FN for even small proteins is computationally demanding . Experimental identification of the FN can be accomplished by “ ϕ ‐value” analysis in which thermodynamic stability and folding kinetics of mutant proteins are used to identify the contribution of a specific primary structure position to the folding transition state.…”

The E3 ubiquitin-protein ligase MDM2 is a novel interactor of the von Hippel-Lindau tumor suppressor

“…Linear motifs were predicted with ELM 62 and intrinsic disorder with MobiDB 3.0 63 . Ab initio protein structures prediction was performed with Rosetta 3.8 64 using specific protocol to model intrinsically disordered regions 65 . For each fragment 5,000 decoys were generated and clustered using Rosetta clustering module.…”