Distance Matrix-Based Approach to Protein Structure Prediction

By Andrzej Kloczkowski, Robert L. Jernigan, Zhi-jun Wu, Guang Song, Lei Yang, Andrzej Kolinski, Piotr Pokarowski. 2009

48 citations recorded by the source corpus.

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [rij2] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance rij is greater or less than a cutoff value rcutoff. We have performed spectral decomposition of the distance matrices $$ {\mathbf{D}} = \sum {\lambda_{k} {\mathbf{v}}_{k} {\mathbf{v}}_{k}^{T} } $$, in terms of eigenvalues $$ \lambda_{k} $$ and the corresponding eigenvectors $$ {\mathbf{v}}_{k} $$ and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r2—the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r2 from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 Å, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 Å. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Distance-based protein folding powered by deep learning (2018, methodology): “Kloczkowski et al 23 described a spectral decomposition method for inter-residue distance prediction.”
Heatmapper: web-enabled heat mapping for all (2016, background): “In structural biology pairwise distance maps have long been used to compare structures, to identify secondary structure elements and even to predict protein structure (14–16).”
3D Human Pose Estimation from a Single Image via Distance Matrix Regression (2016, background): “in modal analysis to estimate shape basis [2], to represent protein structures [20], for sensor network localization [7] and for the resolution of kinematic constraints [29].”