We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic models of local protein structure. These models form a probabilistic alternative to the widely used fragment and rotamer libraries. Combined with an easily extendible software architecture, this makes PHAISTOS well suited for Bayesian inference of protein structure from sequence and/or experimental data. Currently, two force-fields are available within the framework:PROFASI and OPLS-AA/L, the latter including the generalized Born surface area solvent model. A flexible command-line and configuration-file interface allows users quickly to set up simulations with the desired configuration. PHAISTOS is released under the GNU General Public License v3.0. Source code and documentation are freely available from http://phaistos.sourceforge.net. The software is implemented in C11 and has been tested on Linux and OSX platforms.

The inherent flexibility of intrinsically disordered proteins (IDPs) and multi-domain proteins with intrinsically disordered regions (IDRs) presents challenges to structural analysis. These macromolecules need to be represented by an ensemble of conformations, rather than a single structure. Small-angle X-ray scattering (SAXS) experiments capture ensemble-averaged data for the set of conformations. We present a Bayesian approach to ensemble inference from SAXS data, called Bayesian ensemble SAXS (BE-SAXS). We address two issues with existing methods: the use of a finite ensemble of structures to represent the underlying distribution, and the selection of that ensemble as a subset of an initial pool of structures. This is achieved through the formulation of a Bayesian posterior of the conformational space. BE-SAXS modifies a structural prior distribution in accordance with the experimental data. It uses multi-step expectation maximization, with alternating rounds of Markov-chain Monte Carlo simulation and empirical Bayes optimization. We demonstrate the method by employing it to obtain a conformational ensemble of the antitoxin PaaA2 and comparing the results to a published ensemble.

1. Fungus-growing ants are obligate mutualists. Their nutrient-rich fungus garden provides a valuable food store that sustains the ant hosts, but can also attract social parasites.2. The 'guest ant' Megalomyrmex adamsae Longino parasitises the fungus-growing Trachymyrmex zeteki Weber queen just after nest founding. The parasitic queen infiltrates the incipient nest, builds a cavity in the fungal garden, and lays eggs that develop into workers and reproductive males and females.3. This study compared young parasitised and non-parasitised laboratory colonies by measuring garden growth and biomass, and the number of host workers and reproductives. Host queen survival and parasite colony growth were also monitored.4. Parasitised Trachymyrmex colonies had reduced host worker and alate numbers, as well as lower garden biomass, compared with non-parasitised control colonies, confirming that M. adamsae is a xenobiotic social parasite. Host queen survival was not significantly different between parasitised and control colonies.5. This is the first study that experimentally infects host colonies with a xenobiotic social parasite to measure fitness cost to the host. The natural history of M. adamsae and the fungus-growing ant mutualism are evaluated in the context of three general predictions of (Bronstein, Ecology Letters, 4, 277-287, 2001a) regarding the cost of mutualism exploiters.

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