Search PubMedSearch

Biomedical subjects

D Shasha

Publications and source records attributed to D Shasha.

2 recordsLinked to original sources

Complementary classification approaches for protein sequences.

We have studied five methods of protein classification and have applied them to the 768 groups of related proteins in the PROSITE catalog. Four of these methods are based on searching a database of blocks, and the other uses the frequently occurring motifs found in the protein families combined with a fingerprint technique. Our experimental results show that the block-based methods perform well when taking into account the probability of amino acids occurring in a block. Furthermore, the five methods give information that is complementary to each other. Thus, using the five methods together, one can obtain high confidence classifications (if the results agree) or suggest alternative hypotheses (if the results disagree). We also list those proteins whose current families documented in the PROSITE catalog differ from those suggested by our results. There are remarkably few of them, which is a testimony to the quality of PROSITE.

Amino Acid Sequence

Discovering active motifs in sets of related protein sequences and using them for classification.

We describe a method for discovering active motifs in a set of related protein sequences. The method is an automatic two step process: (1) find candidate motifs in a small sample of the sequences; (2) test whether these motifs are approximately present in all the sequences. To reduce the running time, we develop two optimization heuristics based on statistical estimation and pattern matching techniques. Experimental results obtained by running these algorithms on generated data and functionally related proteins demonstrate the good performance of the presented method compared with visual method of O'Farrell and Leopold. By combining the discovered motifs with an existing fingerprint technique, we develop a protein classifier. When we apply the classifier to the 698 groups of related proteins in the PROSITE catalog, it gives information that is complementary to the BLOCKS protein classifier of Henikoff and Henikoff. Thus, using our classifier in conjunction with theirs, one can obtain high confidence classifications (if BLOCKS and our classifier agree) or suggest a new hypothesis (if the two disagree).

Algorithms