Recent assessments of structure prediction have demonstrated that (i) although fold recognition methods can often identify remote similarities when standard sequence search methods fail, the score of the top-ranking fold is not always significant enough to allow a confident prediction; (ii) the use of structural information such as secondary structure increases recognition accuracy; (iii) modern sequence based methods incorporating evolutionary information from neighboring sequences can often identify very remote similarities; (iv) there is no one single method that is superior to other methods when evaluated over a wide range of targets, and (v) extensive human-expert intervention is usually required for the most difficult prediction targets. Here, I describe a new, hybrid fold recognition method that incorporates structural and evolutionary information into a single fully automated method. This work is a first attempt towards the automation of some of the processes that are often applied by human predictors. The method is tested with two fold-recognition benchmarks demonstrating a superior performance. The higher sensitivity and selectivity enable the applicability of this method at genomic scales.