Tuesday, 8 April 2014

Conformational biases of linear motifs and their roles in target binding

Cino EA, Choy WY, Karttunen M (2013) Conformational Biases of Linear Motifs. J Phys Chem B 117:15943-15957. link to manuscript

Videos of our simulations of disordered proteins on Flickr and YouTube

 Fig 1. p53 TAD region conformational ensemble contains a population of structures that resemble its MDM2 bound state [1].

     Intrinsically disordered proteins (IDPs) comprise ~30% of the proteins in our bodies and have key roles in protein-protein interaction networks. Studies have shown that the structural properties of IDPs are crucial to the protein-protein interactions they participate in [1-7]. Despite their name, IDPs do not adopt completely random conformations – many IDPs have conformational propensities. These segments are called Linear Motifs (LMs), and typically consist of continuous 5-25 amino acid stretches. Interestingly, LMs are often crucial protein-protein interaction sites (Fig 1). Several recent studies have identified a number of LMs that prefer to form specific structures that resemble their complexed state while participating in protein-protein interactions (Fig 1). Based on this knowledge, efforts [4, 8] have been made to develop therapeutic LMs (peptides) with enhanced propensities to form particular structures – the idea being to either disrupt the natural protein-protein interaction, or to enhance downstream events that result due to the interaction. In either case, the goal is to elicit a therapeutic effect.

     In our recent paper, which is discussed in this post, the conformational propensities of Linear Motifs from several proteins (Exoenzyme S, Amphiphysin 1, β-Arrestin 2, p21, p66 and Fen1, the LxxLL motif containing protein, RIP140 and a synthetic peptide, Tcf4, and p53) were investigated using microsecond timescale equilibrium Molecular Dynamics (MD) simulations in explicit solvent. The free state conformational landscapes of the LMs were analyzed using several metrics and compared to their known conformations in complex with interacting proteins (ie. Exoenzyme S:14-3-3, Tcf4:β-Catenin and p53:MDM2).


    In this post, the Exoenzyme S LM, which interacts with 14-3-3, is briefly discussed. The Exoenzyme S protein contains an11 amino acid region that forms a short amphipathic helix, with the hydrophobic face pointing toward the 14-3-3 binding groove (Video 1). Microsecond timescale MD simulations of the Exoenzyme S LM (in the absence of 14-3-3) show that it had propensity for adopting structures similar to the one found in complex with 14-3-3 (Video 1). The LM was found to transition between high and low rmsds to the 14-3-3 bound state on the nanosecond timescale. In 8% of the frames the main chain rmsd was <0.15 nm, suggesting that although the uncomplexed state equilibrium favors the unfolded conformation, a population of bound state-like structures exists. To assess which features of the LM govern formation of bound-state like structures, correlations between several measurements (solvent accessible surfaces, secondary structures, compactness, and principal components) and rmsds were analyzed. One variable that was generally well correlated with bound state rmsds was the backbone dihedral angle principal components (dPCA PC1 vs. mainchain rmsd for Exoenzyme S r2=0.64) (Video 1). For further details of the Exoenzyme S and other LMs, take a look at the manuscript.

     The results from this work show that LMs can have distinct conformational propensities, which often resemble the structure formed after binding to a target protein. As a result, the free state structure and dynamics of LMs may hold important clues regarding binding mechanisms, affinities and specificities. The findings should be helpful in advancing our understanding of the mechanisms whereby disordered amino acid sequences bind targets, modeling disordered proteins/regions, and computational prediction of binding affinities.


1. Cino EA, Choy WY, Karttunen M (2013) Conformational Biases of Linear Motifs. J Phys Chem B 117:15943-15957 link to manuscript 

2. Das, R. K.; Mao, A. H.; Pappu, R. V (2012) Unmasking Functional Motifs Within Disordered Regions of Proteins. Sci Signal 5: pe17

3. Fuxreiter, M.; Tompa, P.; Simon, I. (2007) Local Structural Disorder Imparts Plasticity on Linear Motifs. Bioinformatics 23: 950-956

4. Cino, E.A., Killoran, R.C., Karttunen, M., and Choy, W.Y. (2013) Binding of intrinsically disordered proteins to a protein hub. Sci Reps 3: 2305 link to manuscript

5. Khan, H., Cino, E.A., Brickenden A., Fan, J., Yang, D. and Choy, W.Y. (2013) Fuzzy Complex Formation between the Intrinsically Disordered Prothymosin α and the Kelch Domain of Keap1 Involved in the Oxidative Stress Response. J Mol Biol 425(6): 1011-1027 link to manuscript  

6. Cino EA, Wong-Ekkabut J, Karttunen M, Choy WY (2011) Microsecond molecular dynamics simulations of intrinsically disordered proteins involved in the oxidative stress response. PLoS One 6: e27371 link to manuscript  

7. Cino EA, Karttunen M, Choy WY (2012) Effects of molecular crowding on the dynamics of intrinsically disordered proteins. PLoS One 7: e49876 link to manuscript  

8. Bernal, et al (2007) Reactivation of the p53 Tumor Suppressor Pathway by a Stapled p53 Peptide. J Am Chem Soc 129(9): 2456-2457