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.  


References

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

Molecular crowding effects on disordered proteins dynamics

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

Videos of our simulations of disordered proteins on Flickr and YouTube

       Inside cells the concentration of macromolecules can reach up to 400 g/L, creating a crowded environment (Fig 1). The space occupied by cellular molecules (proteins, nucleic acids, etc) reduces the amount of water available, causing molecules to behave differently than they would in more dilute environments. Most studies of proteins and other macromolecules are conducted in vitro with purified and relatively dilute samples. To accurately characterize macromolecules and the biochemical processes they are involved in, it is important to examine them in vivo, or under conditions that mimic the crowded cellular environment.

Fig 1. Diagram illustrating the crowded cellular environment. Microtubules, actin and other proteins (blue, red and green), ribosomes (yellow and purple), RNA (pink).

       In the crowded cellular environment, proteins are expected to behave differently than in vitro. The stability and the folding rate of a well-folded protein can be altered by the excluded volume effect produced by a high density of macromolecules. However, crowding effects on intrinsically disordered proteins (IDPs) are less explored. These proteins can be extremely dynamic and potentially sample a wide ensemble of conformations (Fig 2). The dynamic properties of IDPs are intimately related to the timescale of conformational exchange within the ensemble, which govern 

target recognition and how these proteins function.

Fig 2. Differential dynamics between disordered and well-folded proteins. Autocorrelation functions of backbone N-H bond vectors for individual amino acids residues illustrates that the highly disordered prothymosin alpha is considerably more dynamic compared to ubiquitin. The quickly decorrelating residues in ubiquitin correspond to the terminal ends, which are considerably more flexible than the core region. The data was extracted from MD simulations of each protein in the absence of crowding agents.

       In this manuscript, we focused on determining how molecular crowding affects the dynamics of IDPs using NMR spin-relaxation experiments. Measurements were taken for three disordered proteins, and the well-folded protein, ubiquitin, for comparison, in the absence and presence of crowding agents. Our data illustrates that IDPs remain at least partially disordered despite the presence of high concentration of other macromolecules (Fig 3).

Fig 3. ¹H-¹⁵N Heteronuclear Single Quantum Coherence (HSQC) spectra in the absence (black) and presence (red) of 160 g/L crowding agent Ficoll 70. The IDPs Prothymosin alpha, Thyroid cancer-1 and alpha synuclein as well as the well-folded protein, ubiquitin, were examined. Similar black and red spectra indicate that the protein structures are similar in dilute and crowded environments.


       Despite this, specific regions of Thyroid-cancer-1 and Prothymosin alpha, which encompass protein-protein interaction sites exhibited differential dynamics in the absence and presence of high concentration of crowding agents (Fig 4). This suggests that the crowded environment may have differential effects on the conformational propensity of distinct regions of an IDP, which may lead to selective stabilization of certain target-binding motifs.

Fig 4. Backbone N-H bond transverse relaxation rates for prothymosin alpha and thyroid cancer-1 in the absence (black) and presence (red and green) of crowding agents. Distinct regions of the proteins show differential changes in dynamics in response to crowding.

       Using an MD simulation of prothymosin alpha in the absence of crowding agents, we have proposed a model to correlate the observed changes in relaxation rates to the alteration in protein motions under crowding conditions (see the manuscript for details). Overall, the results show that the segmental motions of IDPs on the nanosecond timescale are retained under crowded conditions and that IDPs function as dynamic structural ensembles in cellular environments.

Our related work references

1. 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

2. Cino EA, Choy WY, Karttunen M (2012) Comparison of Secondary Structure Formation Using 10 Different Force Fields in Microsecond Molecular Dynamics Simulations. J Chem Theory Comput 8:2725-2740. link to manuscript

3. 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

4. Cino E, Fan J, Yang D, Choy WY (2012) (1)H, (15)N and (13)C backbone resonance assignments of the Kelch domain of mouse Keap1. Biomol NMR Assign. In press. link to manuscript

5. Khan H, Cino, EA, Brickenden A, Fan J, Yang D, Choy WY (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 6:1011-1027. link to manuscript

Comparing force fields for biomolecular simulations

Cino EA, Choy WY, Karttunen M (2012) Comparison of Secondary Structure Formation Using 10 Different Force Fields in Microsecond Molecular Dynamics Simulations. J Chem Theory Comput 8:2725-2740. link to manuscript

Videos of our simulations of disordered proteins on YouTube and Flickr

Fig 1. Structures of the NRF2 hairpin from folding simulations and representative free energy landscape of the hairpin folding. The free energy landscape was constructed from a 3 dimensional histogram consisting of radius of gyration, backbone rmsd to bound state structure (PDB id: 2FLU) and distance between 2 hydrophobic residues on opposite strands of the hairpin that make close contacts (as determined by solution NMR for the peptide in the free state).

       A primary choice in performing MD simulations is which force field to use. Currently, specific force fields are employed depending on the system being investigated. For example, a certain force field may give good agreement with experimental data for a specific type of protein, but not necessarily for another. Even though modifications to biomolecular force fields have lead to improved transferability, further progress relies on continued testing. Ideally, these efforts will lead to the development of fully transferable force fields.

       A good method to test force field performance is by simulating protein folding and comparing the results to experimentally determined protein structures. However, most proteins fold on timescales unattainable by modern computer simulations. As a result, it can be challenging to find good test systems. One approach has been to extract amino acid sequences encoding self-folding motifs out of well-folded proteins. While this may be a viable approach to decrease system sizes and obtain folding events, care must be taken to ensure that the motif does indeed fold properly in the absence of the rest of the protein. Another approach has been to design small, fast folding proteins. However, protein design is not an easy task.

       Perhaps a better, in terms of being doable, approach for force field testing of protein folding is to use amino acid sequences encoding preformed structural elements (PSEs). As discussed in my January 11th post, intrinsically disordered proteins (IDPs) often contain PSEs to facilitate their interactions with other proteins. The benefits of using PSEs for folding simulations is that they are typically locally occurring features that do not rely as heavily upon long-range contacts as structural elements in well-folded proteins. Moreover, they often contain features that are found in well-folded proteins, such as hydrophobic clusters and electrostatic interactions. In many ways, PSEs can be though of as mini or micro proteins. These may be ideal candidates for testing of force fields.

Fig 2. Example of a hairpin motif. Hairpins are composed of two antiparallel beta strands connected by a turn. They are common structural elements found in many proteins.

       For this post, the folding of a PSE from the protein NRF2 with 10 commonly used biomolecular force fields is compared. This PSE has been studied experimentally and is known to form what is known as a ‘hairpin’ structure (Fig. 2). Starting from an extended conformation, the amino acid sequence encoding this hairpin has been shown to fold into a structure consistent with experimental data in < 1 µs. However, when comparing the folding of this structural element with commonly used force fields, differences were observed (Fig. 1). Although many of the force fields reproduced experimentally determined free state contacts and yielded hairpin structures, some did not (Fig. 1). As mentioned in my January 11th post, the hairpin appears to be stabilized by hydrogen bonds and hydrophobic contacts.

       The results from this investigation emphasize the importance of force field selection. Additionally, the work illustrates that PSEs may be ideal candidates for force field testing. The results obtained from folding simulations of such elements should be useful for improving biomolecular force fields.

Our related work references

1. Cino EA, Choy WY, Karttunen M (2012) Comparison of Secondary Structure Formation Using 10 Different Force Fields in Microsecond Molecular Dynamics Simulations. J Chem Theory Comput 8:2725-2740. link to manuscript

2. 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

3. 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

4. Cino E, Fan J, Yang D, Choy WY (2012) (1)H, (15)N and (13)C backbone resonance assignments of the Kelch domain of mouse Keap1. Biomol NMR Assign. In press. link to manuscript

5. Khan H, Cino, EA, Brickenden A, Fan J, Yang D, Choy WY (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. In press. link to manuscript

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