Abstract
Knowledge-based approaches are being increasingly used in predicting protein structure and motifs. Machine learning techniques such as neural networks and decision-trees have become invaluable tools for these approaches. This paper describes the use of machine learning in predicting sequence-based motifs in antibody fragments. Given the limited number of three dimensional structures and the plethora of sequences, this technique is useful for homology modeling of three dimensional structures of antibody fragments.
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