Sequence Matching in Homology Modeling

Homology-based modeling, or more simply homology modeling, is becoming increasingly important. Models accurate to within 1-2 Å RMS deviation from the actual three-dimensional structure can be produced by homology modeling in favorable cases. With the number of known threedimensional structures projected to grow to more than ten thousand structures by the end of the decade, it will be possible to model a significant fraction of all sequences using these approaches. In homology modeling, a known three-dimensional structure is used as a template from which a three-dimensional model of a novel sequence is calculated. In the first stages of this procedure, the critical steps are the location of an appropriate homologous structure to use as the template, and the correct mapping of the novel sequence onto the known structure. The detection of a homologous structure involves the inference that the structure is indeed homologous based on a sequence or structure-based comparison. This inference is based on the assumption that if the sequence and structure are, by some measure, significantly similar, they are homologous. Unfortunately, for many extant methods, the influence of various parameters on the significance of the similarity is not well understood. In particular, the influence of variables such as the scoring system, and penalties for insertions and deletions in dynamic programming based approaches are still not theoretically understood and can be determined only by empirical approaches. This work examines the effects of scoring system and affine gap penalties on the ability of a local-similarity dynamic programming algorithm to discriminate between homologous and unrelated sequences.