Lysosomal storage diseases (LSDs) often manifest with severe systemic and central nervous system (CNS) symptoms. The existing treatment options are limited and have no or only modest efficacy against neurological manifestations of disease. We demonstrate that recombinant human heat shock protein 70 (HSP70) improves the binding of several sphingolipid-degrading enzymes to their essential cofactor bis(monoacyl)glycerophosphate in vitro. HSP70 treatment reversed lysosomal pathology in primary fibroblasts from 14 patients with eight different LSDs. HSP70 penetrated effectively into murine tissues including the CNS and inhibited glycosphingolipid accumulation in murine models of Fabry disease (Gla(-/-)), Sandhoff disease (Hexb(-/-)), and Niemann-Pick disease type C (Npc1(-/-)) and attenuated a wide spectrum of disease-associated neurological symptoms in Hexb(-/-) and Npc1(-/-) mice. Oral administration of arimoclomol, a small-molecule coinducer of HSPs that is currently in clinical trials for Niemann-Pick disease type C (NPC), recapitulated the effects of recombinant human HSP70, suggesting that heat shock protein-based therapies merit clinical evaluation for treating LSDs.

IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/

A method is described for the isolation of rat lung epithelial Type II cells using trypsin digestion of tissue to release cells for subsequent separation by Percoll gradient centrifugation. Both the concentration of trypsin and the age (body weight) of the rat affect the yield from primary digestion and the final number of Type II cells obtained. A lung weighing 1 g from a 200 g rat yields approximately 30 X 10(6) washed Type II cells (approximately 25% of the total estimated lung population). These cells have a plating efficiency of 40-50% after 48 h of culture. The cells have a high alkaline to acid phosphatase ratio (usually greater than 4.0) compared with that of alveolar macrophages (0.1) and accumulate putrescine by an active transport mechanism with an apparent KM between 8 and 14 microM. Together with studies of [3H]thymidine uptake into DNA, which is maximal between 48 and 72 h of culture, these quantitative measurements form a good basis for investigating the interactions between a number of chemical agents and Type II cells in vitro.

Our aim in CASP12 was to improve our Template-Based Modeling (TBM) methods through better model selection, accuracy self-estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z-score > 0.0/-2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z-score > -2.0/0.0) compared to all other groups.

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.