Improvement of statistical potentials and threading score functions using information maximization

Abstract

We show that statistical potentials and threading score functions, derived from finite data sets, are informatic functions, and that their performance depends on the manner in which data are classified and compressed. The choice of sequence and structural parameters affects estimates of the conditional probabilities P(C|S), the quantification of the effect of sequence S on conformation C, and determines the amount of information extracted from the data set, as measured by information gain. The mathematical link between information gain and mean conformational energy, established in this work using the local backbone potential as model, demonstrates that manipulation of descriptive parameters also alters the "energy" values assigned to native conformation and to decoy structures in the test pool, and consequently, the performance of such statistical potential functions in fold recognition exercises. We show that sequence and structural partitions that maximize information gain also minimize the mean energy of the ensemble of native conformations. Moreover, we establish an informatic basis for the placement of the native score within an energy spectrum given by the decoy pool in a threading exercise. We discover that, among all informatic quantities, information gain is the best predictor of threading success, even better than the standard Z-score. Consequently, the choices of sequence and structural descriptors, extent of compression, and levels of discretization that maximize information gain must also produce the best potential functions. Strategies to optimize these parameters with respect to information extraction are therefore relevant to building better statistical potentials. Last, we demonstrate that the backbone torsion potential, defined by the trimer sequence, can be an effective tool in greatly reducing the set of possible conformations from a vast decoy pool.

Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)

“…In case of glycine, a representative of the Cβ position gets constructed using the backbone as anchor. The same sequence separation as in the all-atom interaction is applied. Solvation Term: Statistics are created by counting surrounding atoms around all chemically distinguishable heavy atoms not belonging to the assessed residue itself. Torsion Term: The central ϕ/ψ angles of three consecutive amino acids are assessed based on the identity of the involved amino acids using a grouping scheme described by Solis and Rachovsky (Solis and Rackovsky, 2006). …”

“…Torsion Term: The central ϕ/ψ angles of three consecutive amino acids are assessed based on the identity of the involved amino acids using a grouping scheme described by Solis and Rachovsky (Solis and Rackovsky, 2006). …”

Deriving High-Resolution Protein Backbone Structure Propensities from All Crystal Data Using the Information Maximization Device

“…Our previous work [23], [24], [27] employed information theory to help resolve the protein sequence-structure relationship. Information theory is a natural framework with which to analyze this relationship, due to a number of reasons that I shall elucidate throughout this paper.…”

A Knowledge-Based Potential with an Accurate Description of Local Interactions Improves Discrimination between Native and Near-Native Protein Conformations

“…This supports our hypothesis that the current reference systems for potential derivation are not conducive for the computation of pseudo-energies for close nonbonded atom-atom interacting pairs. The similar accuracy difference between the NL-NL and NB-NB 1-5 potentials could be explained on the basis of information gain [37]. For example, the NL-NL potential takes into account a much smaller number of interacting terms when calculating the total pseudo-energy of a given protein structure than those considered by the NB-NB 1-5 potential.…”