Do Natural Proteins Differ from Random Sequences Polypeptides? Natural vs. Random Proteins Classification Using an Evolutionary Neural Network

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random

Exploring the Universe of Protein Structures beyond the Protein Data Bank

It is currently believed that the atlas of existing protein structures is faithfully represented in the Protein Data Bank. However, whether this atlas covers the full universe of all possible protein structures is still a highly debated issue. By using a sophisticated numerical approach, we performed an exhaustive exploration of the conformational space of a 60 amino acid polypeptide chain described with an accurate all-atom interaction potential. We generated a database of around 30,000 compact folds with at least of secondary structure corresponding to local minima of the potential energy. This ensemble plausibly represents the universe of protein folds of similar length; indeed, all the known folds are represented in the set with good accuracy. However, we discover that the known folds form a rather small subset, which cannot be reproduced by choosing random structures in the database. Rather, natural and possible folds differ by the contact order, on average significantly smaller in the former. This suggests the presence of an evolutionary bias, possibly related to kinetic accessibility, towards structures with shorter loops between contacting residues. Beside their conceptual relevance, the new structures open a range of practical applications such as the development of accurate structure prediction strategies, the optimization of force fields, and the identification and design of novel folds

HIV-1 Nef Induces Proinflammatory State in Macrophages through Its Acidic Cluster Domain: Involvement of TNF Alpha Receptor Associated Factor 2

Background: HIV-1 Nef is a virulence factor that plays multiple roles during HIV replication. Recently, it has been described that Nef intersects the CD40 signalling in macrophages, leading to modification in the pattern of secreted factors that appear able to recruit, activate and render T lymphocytes susceptible to HIV infection. The engagement of CD40 by CD40L induces the activation of different signalling cascades that require the recruitment of specific tumor necrosis factor receptor-associated factors (i.e. TRAFs). We hypothesized that TRAFs might be involved in the rapid activation of NF-kappa B, MAPKs and IRF-3 that were previously described in Nef-treated macrophages to induce the synthesis and secretion of proinflammatory cytokines, chemokines and IFN beta to activate STAT1, -2 and -3. Methodology/Principal Findings: Searching for possible TRAF binding sites on Nef, we found a TRAF2 consensus binding site in the AQEEEE sequence encompassing the conserved four-glutamate acidic cluster. Here we show that all the signalling effects we observed in Nef treated macrophages depend on the integrity of the acidic cluster. In addition, Nef was able to interact in vitro with TRAF2, but not TRAF6, and this interaction involved the acidic cluster. Finally silencing experiments in THP-1 monocytic cells indicate that both TRAF2 and, surprisingly, TRAF6 are required for the Nef-induced tyrosine phosphorylation of STAT1 and STAT2. Conclusions: Results reported here revealed TRAF2 as a new possible cellular interactor of Nef and highlighted that in monocytes/macrophages this viral protein is able to manipulate both the TRAF/NF-kappa B and TRAF/IRF-3 signalling axes, thereby inducing the synthesis of proinflammatory cytokines and chemokines as well as IFN beta