Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

Interactions between proteins and their ligands, such as small molecules, other proteins, and DNA, depend on specific interatomic interactions that can be classified on the basis of atom type and distance and angle constraints. Visualisation of these interactions provides insights into the nature of molecular recognition events and has practical uses in guiding drug design and understanding the structural and functional impacts of mutations. We present Arpeggio, a web server for calculating interactions within and between proteins and protein, DNA, or small-molecule ligands, including van der Waals', ionic, carbonyl, metal, hydrophobic, and halogen bond contacts, and hydrogen bonds and specific atom–aromatic ring (cation–π, donor–π, halogen–π, and carbon–π) and aromatic ring–aromatic ring (π–π) interactions, within user-submitted macromolecule structures. PyMOL session files can be downloaded, allowing high-quality publication images of the interactions to be generated. Arpeggio is implemented in Python and available as a user-friendly web interface at http://structure.bioc.cam.ac.uk/arpeggio/ and as a downloadable package at https://bitbucket.org/harryjubb/arpeggio.

e We report here a series of five chemically diverse scaffolds that have in vitro activities on replicating and hypoxic nonreplicating bacilli by targeting the respiratory bc 1 complex in Mycobacterium tuberculosis in a strain-dependent manner. Deletion of the cytochrome bd oxidase generated a hypersusceptible mutant in which resistance was acquired by a mutation in qcrB. These results highlight the promiscuity of the bc 1 complex and the risk of targeting energy metabolism with new drugs.

Mutations in the renal tumour suppressor protein, folliculin, lead to proliferative skin lesions, lung complications and renal cell carcinoma. Folliculin has been reported to interact with AMP-activated kinase, a key component of the mammalian target of rapamycin pathway. Most cancer-causing mutations lead to a carboxy-terminal truncation of folliculin, pointing to a functional importance of this domain in tumour suppression. We present here the crystal structure of folliculin carboxy-terminal domain and demonstrate that it is distantly related to differentially expressed in normal cells and neoplasia (DENN) domain proteins, a family of Rab guanine nucleotide exchange factors (GEFs). Using biochemical analysis, we show that folliculin has GEF activity, indicating that folliculin is probably a distantly related member of this class of Rab GEFs.

Many essential biological processes including cell regulation and signalling are mediated through the assembly of protein complexes. Changes to protein-protein interaction (PPI) interfaces can affect the formation of multiprotein complexes, and consequently lead to disruptions in interconnected networks of PPIs within and between cells, further leading to phenotypic changes as functional interactions are created or disrupted. Mutations altering PPIs have been linked to the development of genetic diseases including cancer and rare Mendelian diseases, and to the development of drug resistance. The importance of these protein mutations has led to the development of many resources for understanding and predicting their effects. We propose that a better understanding of how these mutations affect the structure, function, and formation of multiprotein complexes provides novel opportunities for tackling them, including the development of small-molecule drugs targeted specifically to mutated PPIs.