Suboptimal axonal regeneration contributes to the consequences of nervous system trauma and neurodegenerative disease, but the intrinsic mechanisms that regulate axon growth remain unclear. We screened 50,400 small molecules for their ability to promote axon outgrowth on inhibitory substrata. The most potent hits were the statins, which stimulated growth of all mouse- and human-patient-derived neurons tested, both in vitro and in vivo, as did combined inhibition of the protein prenylation enzymes farnesyltransferase (PFT) and geranylgeranyl transferase I (PGGT-1). Compensatory sprouting of motor axons may delay clinical onset of amyotrophic lateral sclerosis (ALS). Accordingly, elevated levels of PGGT1B, which would be predicted to reduce sprouting, were found in motor neurons of early- versus late-onset ALS patients postmortem. The mevalonate-prenylation pathway therefore constitutes an endogenous brake on axonal growth, and its inhibition provides a potential therapeutic approach to accelerate neuronal regeneration in humans.

Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein–protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the first community-wide resource to provide structure-based PPI predictions that go beyond homology modeling. Also, most web-resources for predicting PPIs currently rely on functional genomic data (e.g. GO annotation, gene expression, cellular localization, etc.). Our structure-based approach is independent of such methods and only requires the sequence information of the proteins being queried. The web service allows multiple querying options, aimed at maximizing flexibility. For the most commonly studied organisms (fly, human and yeast), predictions have been pre-computed and can be retrieved almost instantaneously. For proteins from other species, users have the option of getting a quick-but-approximate result (using orthology over pre-computed results) or having a full-blown computation performed. The web service is freely available at http://struct2net.csail.mit.edu.

Gyoneva et al use RNA-seq to show that Cx3cr1-deficient microglia in young mice display a gene expression profile similar to microglia in aged mice, suggesting premature microglial aging.

Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the "twilight zone" (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses on only the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.