Abstract Motivation: Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. Results: We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies. Availability: An interactive tool implementing this method, named ProTarget, is available at http://www.protarget.cs.huji.ac.il. It can be used interactively to retrieve a list of candidate proteins for Structural genomics projects. Supplementary material is available at http://www.protarget.cs.huji.ac.il/supplement Contact: [email protected]

Abstract Motivation: A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation. Results: We propose a novel technique that automatically clusters protein sequences. We consider all proteins in SWISSPROT, and carry out an all-against-all BLAST similarity test among them. With this similarity measure in hand we proceed to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. Here we compare the clusters that result from alternative merging rules, and validate the results against InterPro. Our preliminary results show that clusters that are consistent with several rather than a single merging rule tend to comply with InterPro annotation. This is an affirmation of the view that the protein space consists of families that differ markedly in their evolutionary conservation. Availability: The outcome of these investigations can be viewed in an interactive Web site at http://www.protonet.cs.huji.ac.il Supplementary information: Biological examples for comparing the performance of the different algorithms used for classification are presented in http://www.protonet.cs.huji.ac.il/examples.html Contact: [email protected] Keywords: protein families; protein classification; sequence alignment; clustering.

In a non-e~pansive hashing scheme, similar inputs are stored in memory locations which are close.We develop a non-expansive hashing scheme wherein any set of size O (R1-C) from a large universe may be stored in a memory of size R (any e > 0, and R > Ro(c)), and where ret rieval takes O(1) operations.We explain how to use non-expansive hashing schemes for efficient storage and retrieval of noisy data.A dynamic version of this hashing scheme is presented as well.