The free energy landscape of the oncogene protein E7 of human papillomavirus type 16 reveals a complex interplay between ordered and disordered regions.

When present, structural disorder makes it very challenging to characterise the conformational properties of proteins. This is particularly the case of proteins, such as the oncogene protein E7 of human papillomavirus type 16, which contain both ordered and disordered domains, and that can populate monomeric and oligomeric states under physiological conditions. Nuclear magnetic resonance (NMR) spectroscopy is emerging as a powerful method to study these complex systems, most notably in combination with molecular dynamics simulations. Here we use NMR chemical shifts and residual dipolar couplings as structural restraints in replica-averaged molecular dynamics simulations to determine the free energy landscape of E7. This landscape reveals a complex interplay between a folded but highly dynamical C-terminal domain and a disordered N-terminal domain that forms transient secondary and tertiary structures, as well as an equilibrium between a high-populated (98%) dimeric state and a low-populated (2%) monomeric state. These results provide compelling evidence of the complex conformational heterogeneity associated with the behaviour and interactions of this disordered protein associated with disease.University of Florence (Italy) “Science without borders” of the Brazilian Ministry of Science and Technology (CNPq

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

BACKGROUND: Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure. In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past. RESULTS: We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å. After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å. Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server. CONCLUSIONS: The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints

Protein Dielectric Constants Determined from NMR Chemical Shift Perturbations

Understanding the connection between protein structure and function requires a quantitative under-standing of electrostatic effects. Structure-based electrostatics calculations are essential for this purpose, but their use has been limited by a long-standing discussion on which value to use for the dielectric constants (εeff and εp) required in Coulombic models and Poisson-Boltzmann models. The currently used values for εeff and εp are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for εeff and εp by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in fourteen proteins to get a broad and general characterization of electric fields. Coulomb’s law reproduces the measured CSPs optimally with a protein dielectric constant (εeff) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (εp) rangedsfrom 2-5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders, and how different it is from the εp of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of εp = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pKa values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable εp common to most folded proteins.Science Foundation Irelan