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HAMP domains connect extracellular sensory with intracellular signaling domains in over 7500 proteins, including histidine kinases, adenylyl cyclases, chemotaxis receptors, and phosphatases. The solution structure of an archaeal HAMP domain shows a homodimeric, four-helical, parallel coiled coil with unusual interhelical packing, related to the canonical packing by rotation of the helices. This suggests a model for the mechanism of signal transduction, in which HAMP alternates between the observed conformation and a canonical coiled coil. We explored this mechanism in vitro and in vivo using HAMP domain fusions with a mycobacterial adenylyl cyclase and an E. coli chemotaxis receptor. Structural and functional studies show that the equilibrium between the two forms is dependent on the side-chain size of residue 291, which is alanine in the wild-type protein.

Motivation: Recent breakthroughs in protein residue–residue contact prediction have made reliable de novo prediction of protein structures possible. The key was to apply statistical methods that can distinguish direct couplings between pairs of columns in a multiple sequence alignment from merely correlated pairs, i.e. to separate direct from indirect effects. Two classes of such methods exist, either relying on regularized inversion of the covariance matrix or on pseudo-likelihood maximization (PLM). Although PLM-based methods offer clearly higher precision, available tools are not sufficiently optimized and are written in interpreted languages that introduce additional overheads. This impedes the runtime and large-scale contact prediction for larger protein families, multi-domain proteins and protein–protein interactions.Results: Here we introduce CCMpred, our performance-optimized PLM implementation in C and CUDA C. Using graphics cards in the price range of current six-core processors, CCMpred can predict contacts for typical alignments 35–113 times faster and with the same precision as the most accurate published methods. For users without a CUDA-capable graphics card, CCMpred can also run in a CPU mode that is still 4–14 times faster. Thanks to our speed-ups (http://dictionary.cambridge.org/dictionary/british/speed-up) contacts for typical protein families can be predicted in 15–60 s on a consumer-grade GPU and 1–6 min on a six-core CPU.Availability and implementation: CCMpred is free and open-source software under the GNU Affero General Public License v3 (or later) available at https://bitbucket.org/soedinglab/ccmpredContact: johannes.soeding@mpibpc.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.

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In this study we compare commonly used coiled-coil prediction methods against a database derived from proteins of known structure. We Wnd that the two older programs COILS and PairCoil/MultiCoil are signiWcantly outperformed by two recent developments: Marcoil, a program built on hidden Markov models, and PCOILS, a new COILS version that uses proWles as inputs; and to a lesser extent by a PairCoil update, PairCoil2. Overall Marcoil provides a slightly better performance over the reference database than PCOILS and is considerably faster, but it is sensitive to highly charged false positives, whereas the weighting option of PCOILS allows the identiWcation of such sequences.