a b s t r a c tPresent-day proteins are believed to have evolved features to reduce the risk of aggregation. However, proteins can emerge de novo by translation of non-coding DNA segments. In this study we assess the aggregation, disorder and transmembrane propensity of protein sequences generated by translating random nucleotide sequences of varying GC-content. Potential de novo randomsequence proteins translated from regions with GC content between 40% and 60% do not show stronger aggregation propensity than existing ones and exhibit similar tendency to be disordered. We suggest that de novo emerging proteins do not mean an unavoidable aggregation threat to evolving organisms.

NMR spectroscopy is the leading technique to characterize protein internal dynamics at the atomic level and on multiple time scales. However, the structural interpretation of the observables obtained by various measurements is not always straightforward and in many cases dynamics-related parameters are only used to "decorate" static structural models without offering explicit description of conformational heterogeneity. To overcome such limitations, several computational techniques have been developed to generate ensemble-based representations of protein structure and dynamics with the use of NMR-derived data. An important common aspect of the methods is that NMR observables and derived parameters are interpreted as properties of the ensemble instead of individual conformers. The resulting ensembles reflect the experimentally determined internal mobility of proteins at a given time scale and can be used to understand the role of internal motions in biological processes at atomic detail. In this review we provide an overview of the calculation methods currently available and examples of biological insights obtained by the ensemble-based models of the proteins investigated.

BackgroundIn conjunction with the recognition of the functional role of internal dynamics of proteins at various timescales, there is an emerging use of dynamic structural ensembles instead of individual conformers. These ensembles are usually substantially more diverse than conventional NMR ensembles and eliminate the expectation that a single conformer should fulfill all NMR parameters originating from 1016 - 1017 molecules in the sample tube. Thus, the accuracy of dynamic conformational ensembles should be evaluated differently to that of single conformers.ResultsWe constructed the web application CoNSEnsX (Consistency of NMR-derived Structural Ensembles with eXperimental data) allowing fast, simple and convenient assessment of the correspondence of the ensemble as a whole with diverse independent NMR parameters available. We have chosen different ensembles of three proteins, human ubiquitin, a small protease inhibitor and a disordered subunit of cGMP phosphodiesterase 5/6 for detailed evaluation and demonstration of the capabilities of the CoNSEnsX approach.ConclusionsOur results present a new conceptual method for the evaluation of dynamic conformational ensembles resulting from NMR structure determination. The designed CoNSEnsX approach gives a complete evaluation of these ensembles and is freely available as a web service at http://consensx.chem.elte.hu.

The emerging role of internal dynamics in protein fold and function requires new avenues of structure analysis. We analyzed the dynamically restrained conformational ensemble of ubiquitin generated from residual dipolar coupling data, in terms of protruding and buried atoms as well as interatomic distances, using four proximity-based algorithms, CX, DPX, PRIDE and PRIDE-NMR (http://hydra.icgeb.trieste.it/protein/). We found that Ubiquitin, this relatively rigid molecule has a highly diverse dynamic ensemble. The environment of protruding atoms is highly variable across conformers, on the other hand, only a part of buried atoms tends to fluctuate. The variability of the ensemble cautions against the use of single conformers when explaining functional phenomena. We also give a detailed evaluation of PRIDE-NMR on a wide dataset and discuss its usage in the light of the features of available NMR distance restraint sets in public databases.

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.