Exploring the electronic structure of knotted proteins: the case of two ornithine transcarbamylase family.

CONTEXT: Geometrical knots are rare structural arrangements in proteins in which the polypeptide chain ties itself into a knot, which is very intriguing due to the uncertainty of their impact on the protein properties. Presently, classical molecular dynamics is the most employed technique in the few studies found on this topic, so any information on how the presence of knots affects the reactivity and electronic properties of proteins is even scarcer. Using the electronic structure methods and quantum chemical descriptors analysis, we found that the same amino-acid residues in the knot core have statistically larger values for the unknotted protein, for both hard-hard and soft-soft interaction descriptors. In addition, we present a computationally feasible protocol, where we show it is possible to separate the contribution of the geometrical knot to the reactivity and other electronic structure properties. METHODS: In order to investigate these systems, we used PRIMoRDiA, a new software developed by our research group, to explore the electronic structure of biological macromolecules. We evaluated several local quantum chemical descriptors to unveil relevant patterns potentially originating from the presence of the geometrical knot in two proteins, belonging to the ornithine transcarbamylase family. We compared several sampled structures from these two enzymes that are highly similar in both tertiary structure and function, but one of them has a knot whereas the other does not. The sampling was carried out through molecular dynamics simulations using ff14SB force field along 50 ns, and the semiempirical convergence was performed with PM7 Hamiltonian.

PFstats: A Network-Based Open Tool for Protein Family Analysis.

PFstats is a software developed for the extraction of useful information from protein multiple sequence alignments. By analyzing positional conservation and residue coevolution networks, the software allows the identification of structurally and functionally important residue groups and the discovery of probable functional subclasses. Furthermore, it contains tools for the identification of the possible biological significance of these findings. PFstats contains methods for maximizing the significance of alignments through filtering and weighting, residue conservation and coevolution analysis, automatic UniprotKb queries for residue-position annotation and many possible data visualization methods.