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. 2016 Aug 10;138(31):9730-42.
doi: 10.1021/jacs.6b06543. Epub 2016 Jul 19.

Finding Our Way in the Dark Proteome

Asmit Bhowmick, David H Brookes, Shane R Yost, H Jane Dyson  1 , Julie D Forman-Kay  2   3 , Daniel Gunter, Martin Head-Gordon, Gregory L Hura, Vijay S Pande  4 , David E Wemmer, Peter E Wright  3 , Teresa Head-Gordon
Affiliations

Finding Our Way in the Dark Proteome

Asmit Bhowmick et al. J Am Chem Soc. .

Abstract

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However, approximately one-third of the human proteome is comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain "unseen" by traditional structural biology methods. This Perspective considers the challenges raised by the "Dark Proteome", in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a "computational beamline" that will allow key functional features to be identified in IDP structural ensembles.

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Figures

Figure 1
Figure 1. Heatmap comparing pairs of profiles from multiple experimental techniques including SAXS, DLS and CD
Each cell is a pair wise comparison between a condition or construct. Similarity between SAXS curves is measured by the metric Vr, and displayed as a gradient color where red indicates similarity (low Vr score) and white indicates dissimilarity. The black square diagonals are self-comparisons.
Figure 2
Figure 2
log p(X, ξ|D, I) evaluated for X equal to the following qualitatively different ensembles for the Aβ42 monomer: random coil (RC), statistical secondary structure (Pred-SS), de novo MD, and ENSEMBLE optimized ensembles (MD-ENS1, MD-ENS2, MD-ENS4, and Pred-SS-ENS) using (a) chemical shift data only, (b) J-coupling data only, and (c) J-coupling and chemical shift data together. Adapted with permission from [], copyright 2016 American Chemical Society.
Figure 3
Figure 3. Convergence time for Fs (capped 21 alanine) peptide transition matrix generated with various hybrid sampling schemes
Time is measured in the number of eigenvalue-based trajectories needed to converge to an absolute error of 2.00 after 1000 initial trajectories are run from a chosen sampling method. Absolute error is defined as the sum of absolute deviations in transition matrix elements. Convergence times for each method were, averaged over 10 simulations, 1) 3913 for pure eigenvalue-based sampling, 2) 2669 for connectivity-based hybrid sampling, 3) 1107 for even sampling hybrid sampling, and 4) 286 for min count-based hybrid sampling. Reprinted with permission from []; copyright 2011 American Chemical Society.
Figure 4
Figure 4. J-coupling constants (a) 3JCγN and (b) 3JCγCO for the DHFR binary product complex E:THF and (c) 3JCγN and (d) 3JCγCO for the DHFR binary product complex E:FOL
The red symbols are the experimental data from . The blue symbols are calculated from the MC-SCE ensemble using backbones from molecular dynamics and the Karplus parameterization from . Reprinted with permission from []; copyright 2015 Elsevier.
Figure 5
Figure 5
Conceptualization of a Computational Beamline for the IDP community.
See this image and copyright information in PMC

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