MOTIVATION: Monte Carlo methods are the most effective means of exploring the energy landscapes of protein folding. The rugged topography of folding energy landscapes causes sampling inefficiencies however, particularly at low, physiological temperatures. RESULTS: A hybrid Monte Carlo method, termed density guided importance sampling (DGIS), is presented that overcomes these sampling inefficiencies. The method is shown to be highly accurate and efficient in determining Boltzmann weighted structural metrics of a discrete off-lattice protein model. In comparison to the Metropolis Monte Carlo method, and the hybrid Monte Carlo methods, jump-walking, smart-walking and replica-exchange, the DGIS method is shown to be more efficient, requiring no parameter optimization. The method guides the simulation towards under-sampled regions of the energy spectrum and recognizes when equilibrium has been reached, avoiding arbitrary and excessively long simulation times. AVAILABILITY: Fortran code available from authors upon request. CONTACT: [email protected].

Abstract Motivation: Rearrangements of protein domains and motifs such as swaps and circular permutations (CPs) can produce erroneous results in searching sequence databases when using traditional methods based on linear sequence alignments. Circular permutations are also of biological relevance because they can help to better understand both protein evolution and functionality. Results: We have developed an algorithm, RASPODOM, which is based on the classical recursive alignment scheme. Sequences are represented as strings of domains taken from precompiled resources of domain (motif) databases such as ProDom. The algorithm works several orders of magnitude faster than a reimplementation of the existing CP detection algorithm working on strings of amino acids, produces virtually no false positives and allows the discrimination of true CPs from ‘intermediate’ CPs (iCPs). Several true CPs which have not been reported in literature so far could be identified from Swiss-Prot/TrEMBL within minutes. Availability: Source codes, additional scripts, data and a web-based interface can be found on: http://www.uni-muenster.de/Biologie.Botanik/ebb/projects/raspodom/ Contact: [email protected]