Fine tuned exploration of evolutionary relationships within the protein universe.

In the regime of domain classifications, the protein universe unveils a discrete set of folds connected by hierarchical relationships. Instead, at sub-domain-size resolution and because of physical constraints not necessarily requiring evolution to shape polypeptide chains, networks of protein motifs depict a continuous view that lies beyond the extent of hierarchical classification schemes. A number of studies, however, suggest that universal sub-sequences could be the descendants of peptides emerged in an ancient pre-biotic world. Should this be the case, evolutionary signals retained by structurally conserved motifs, along with hierarchical features of ancient domains, could sew relationships among folds that diverged beyond the point where homology is discernable. In view of the aforementioned, this paper provides a rationale where a network with hierarchical and continuous levels of the protein space, together with sequence profiles that probe the extent of sequence similarity and contacting residues that capture the transition from pre-biotic to domain world, has been used to explore relationships between ancient folds. Statistics of detected signals have been reported. As a result, an example of an emergent sub-network that makes sense from an evolutionary perspective, where conserved signals retrieved from the assessed protein space have been co-opted, has been discussed.

Probing the protein space for extending the detection of weak homology folds.

Redundancy of prediction methods has been used to explore the occurrence of weak homology protein motifs. A hybrid template-based algorithm has been implemented to predict different layers of protein structure by detecting domain building sub-structures, which share low sequence identity. Physicochemical determinants, secondary structure profiles, and multiple alignments have been analyzed to generate a broad set of structural sub-domains. Then, intensive computing procedures generated all the various tridimensional folds on the basis of secondary structure predictions, fragment assembly and detection of structural homologs. The proposed algorithm not only identifies common protein sub-structures, but also detects higher order architectures such as domain superfamilies/superfolds by linking backbone trajectories of supersecondary structures. Applying rigid transformation protocols, population of the detected domain building models with an average root mean square deviation from native structures of 2.3Å and an average template modeling score from native structures of 0.43 has been obtained. The fold detection algorithm here proposed yields more accurate results than previously proposed methods, predicting structural homology also for proteins sharing less than 20% sequence identity. Our tools are freely available at http://www.acbrc.org/tools.html.

In silico study of structural determinants modulating the redox potential of Rigidoporus lignosus and other fungal laccases.

Laccases are multicopper oxidases in which substrate oxidation takes place at the type-1 (T1) copper site. The redox potential (E (0)) significantly varies amongst members of the family and is a key parameter for substrate specificity. Despite sharing highly conserved features at the T1 copper site, laccases span a large range of E (0), suggesting that the influence of the metal secondary coordination sphere is important. In silico analysis of structural determinants modulating the E (0) of Rigidoporus lignosus and other fungal laccases indicated that different factors can be considered. First, the length of the T1 copper coordinating histidine bond is observed to be longer in high E (0) laccases than in low E (0) ones. The hydrophobic environment around the T1 copper site appeared as another important structural determinant in modulating the E (0), with a stronger hydrophobic environment correlating with higher E (0). The analysis of hydrogen bonding network (HBN) around the T1-binding pocket revealed that the amino acids building up the metal binding site strongly interact with neighbouring residues and contribute to the stabilization of the protein folds. Changes in these HBNs that modified the Cu1 preferred coordination geometry lead to an increase of E (0). The presence of axial ligands modulates the E (0) of T1 to different extent. Stacking interactions between aromatic residues located in the second coordination shell and the metal ion coordination histidine imidazole ring have also been identified as a factor that modulates the E (0). The electrostatic interactions between the T1 copper site and backbone carbonyl oxygen indicate that Cu1-CO=NH distance is longer in the high E (0) laccases. In short, the in silico study reported herein identifies several structural factors that may influence the E (0) of the examined laccases. Some of these are dependent on the nature of the coordination ligands at the T1 site, but others can be ascribed to the hydrophobic effects, HBNs, axial ligations, stacking and electrostatic interactions, not necessary directly interacting with the copper metal.

Automated molecular library generation of proteic fragments by virtual proteolysis for molecular modelling studies.

This paper presents a computer aided design method useful for simulation of a set of proteolytic cleavages upon target proteins obtained from the Brookhaven Data Bank. The method was developed by using algorithms that are able to interface themselves with other software environments, in order to assist computer analyses in the molecular modelling field, and allowing the generation of molecular libraries containing protein fragments produced by simulated proteolysis. These libraries include structures that differ for several amino acid deletions upon specified regions of the primary sequence. Target residues chosen for the simulation are compatible with enzymatic proteolysis methods used in conventional laboratory procedures. Furthermore, algorithms were able to identify a set of chemical-physical properties of the starting proteins, leading the simulation to find out the most suitable residues for proteolysis. The goal of these strategies is to generate fragments that are leaded to maintain the native-like condition of starting molecules, avoiding loss of conformational characteristics of the original tertiary structure. Proteins chosen for generating proteolytic libraries were represented by naphthalene 1,2 dioxygenase and Rigidoporus lignosus laccase.