We report the development of two computational methods to assist density map interpretation at intermediate resolutions: sheettracer for building pseudo-C(alpha) models of beta-sheets, and a deconvolution method for enhancing features attributed to major secondary structural elements. Sheettracer is tightly coupled with sheetminer, which was developed to locate sheet densities in intermediate-resolution density maps. The results from sheetminer are used as inputs to sheettracer, which employs a multi-step ad hoc morphological analysis of sheet densities to trace individual strands of beta-sheets. The methods were tested on simulated density maps from 12 protein crystal structures that represent a reasonably complete sampling of sheet morphology. The sheet-tracing results were quantitatively assessed in terms of sensitivity, specificity and rms deviations. Furthermore, sheettracer and the deconvolution method were rigorously tested on experimental maps of the lambda2 protein of reovirus at resolutions of 7.6A and 11.8A. Our results clearly demonstrate the capability of sheettracer in building pseudo-C(alpha) models of beta-sheets in intermediate-resolution density maps and the power of the deconvolution method in enhancing the performance of sheettracer. These computational methods, along with other related ones, should facilitate recognition and analysis of folding motifs from experimental data at intermediate resolutions.