Sequence alignments are fundamental to a wide range of applications, including database searching, functional residue identification and structure prediction techniques. These applications predict or propagate structural/functional/evolutionary information based on a presumed homology between the aligned sequences. If the initial hypothesis of homology is wrong, no subsequent application, however sophisticated, can be expected to yield accurate results. Here we present a novel method, LEON, to predict homology between proteins based on a multiple alignment of complete sequences (MACS). In MACS, weak signals from distantly related proteins can be considered in the overall context of the family. Intermediate sequences and the combination of individual weak matches are used to increase the significance of low-scoring regions. Residue composition is also taken into account by incorporation of several existing methods for the detection of compositionally biased sequence segments. The accuracy and reliability of the predictions is demonstrated in large-scale comparisons with structural and sequence family databases, where the specificity was shown to be >99% and the sensitivity was estimated to be approximately 76%. LEON can thus be used to reliably identify the complex relationships between large multidomain proteins and should be useful for automatic high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc.