Structural basis of O6-alkylguanine recognition by a bacterial alkyltransferase-like DNA repair protein.

Alkyltransferase-like proteins (ATLs) are a novel class of DNA repair proteins related to O(6)-alkylguanine-DNA alkyltransferases (AGTs) that tightly bind alkylated DNA and shunt the damaged DNA into the nucleotide excision repair pathway. Here, we present the first structure of a bacterial ATL, from Vibrio parahaemolyticus (vpAtl). We demonstrate that vpAtl adopts an AGT-like fold and that the protein is capable of tightly binding to O(6)-methylguanine-containing DNA and disrupting its repair by human AGT, a hallmark of ATLs. Mutation of highly conserved residues Tyr(23) and Arg(37) demonstrate their critical roles in a conserved mechanism of ATL binding to alkylated DNA. NMR relaxation data reveal a role for conformational plasticity in the guanine-lesion recognition cavity. Our results provide further evidence for the conserved role of ATLs in this primordial mechanism of DNA repair.

Structural genomics is the largest contributor of novel structural leverage.

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today's UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849-851, 2007) has resulted from systematic targeting of large families. PSI's per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another approximately15 years to cover most sequences in the current UniProt database.

Physical protein-protein interactions predicted from microarrays.

MOTIVATION: Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task. RESULTS: We validated our support vector machine-based method on several independent datasets. At the same levels of accuracy, our method recovered more experimentally observed physical interactions than a conventional correlation-based approach. Pairs predicted by our method to very likely interact were close in the overall network of interaction, suggesting our method as an aid for functional annotation. We applied the method to predict interactions in yeast (Saccharomyces cerevisiae). A Gene Ontology function annotation analysis and literature search revealed several probable and novel predictions worthy of future experimental validation. We therefore hope our new method will improve the annotation of interactions as one component of multi-source integrated systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Create and assess protein networks through molecular characteristics of individual proteins.

MOTIVATION: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. RESULTS: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. AVAILABILITY: http://www.rostlab.org/services/pinat.