The purpose of this work was to construct a consensus prediction algorithm of ‘aggregation-prone’ peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/‘aggregation-prone’ peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins).

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Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

The arthropod cuticle is a composite, bipartite system, made of chitin filaments embedded in a proteinaceous matrix. The physical properties of cuticle are determined by the structure and the interactions of its two major components, cuticular proteins (CPs) and chitin. The proteinaceous matrix consists mainly of structural cuticular proteins. The majority of the structural proteins that have been described to date belong to the CPR family, and they are identified by the conserved R&R region (Rebers and Riddiford Consensus). Two major subfamilies of the CPR family RR-1 and RR-2, have also been identified from conservation at sequence level and some correlation with the cuticle type. Recently, several novel families, also containing characteristic conserved regions, have been described. The package HMMER v3.0 [http://hmmer.janelia.org/] was used to build characteristic profile Hidden Markov Models based on the characteristic regions for 8 of these families, (CPF, CPAP3, CPAP1, CPCFC, CPLCA, CPLCG, CPLCW, Tweedle). In brief, these families can be described as having: CPF (a conserved region with 44 amino acids); CPAP1 and CPAP-3 (analogous to peritrophins, with 1 and 3 chitin-binding domains, respectively); CPCFC (2 or 3 C-x(5)-C repeats); and four of five low complexity (LC) families, each with characteristic domains. Using these models, as well as the models previously created for the two major subfamilies of the CPR family, RR-1 and RR-2 (Karouzou et al., 2007), we developed CutProtFam-Pred, an on-line tool (http://bioinformatics.biol.uoa.gr/CutProtFam-Pred) that allows one to query sequences from proteomes or translated transcriptomes, for the accurate detection and classification of putative structural cuticular proteins. The tool has been applied successfully to diverse arthropod proteomes including a crustacean (Daphnia pulex) and a chelicerate (Tetranychus urticae), but at this taxonomic distance only CPRs and CPAPs were recovered.

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