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Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings. [ABSTRACT FROM AUTHOR]

Tumor proliferative capacity is a major biological correlate of breast tumor metastatic potential. In this paper, we developed a systems approach to investigate associations among gene expression patterns, representative protein-protein interactions, and the potential for clinical metastases, to uncover novel survival-related subnetwork signatures as a function of tumor proliferative potential. Based on the statistical associations between gene expression patterns and patient outcomes, we identified three groups of survival prognostic subnetwork signatures (SPNs) corresponding to three proliferation levels. We discovered 8 SPNs in the high proliferation group, 8 SPNs in the intermediate proliferation group, and 6 SPNs in the low proliferation group. We observed little overlap of SPNs between the three proliferation groups. The enrichment analysis revealed that most SPNs were enriched in distinct signaling pathways and biological processes. The SPNs were validated on other cohorts of patients, and delivered high accuracy in the classification of metastatic vs non-metastatic breast tumors. Our findings indicate that certain biological networks underlying breast cancer metastasis differ in a proliferation-dependent manner. These networks, in combination, may form the basis of highly accurate prognostic classification models and may have clinical utility in guiding therapeutic options for patients. [ABSTRACT FROM AUTHOR]