In this paper, we describe a novel, shape-modeling approach to recovering 3D protein structures from volumetric images. The input to our method is a sequence of alpha-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of alpha-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A* search with several choices of heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.

Abstract—Previous distributed anomaly detection efforts have operated on summary statistics gathered from each node. This has the advantage that the audit trail is limited in size since event sets can be succinctly represented. While this minimizes the bandwidth consumed and helps scale the detection to a large number of nodes, it limits the infrastructure’s ability to identify the source of anomalies. We describe three optimizations that together allow fine-grained tracking of the sources of anomalous activity in a Grid, thereby facilitating precise responses. We demonstrate the scheme’s scalability in terms of storage and network bandwidth overhead with an implementation on nodes running BOINC. The results generalize to other types of Grids as well. Keywords-anomalies, correlation, filtration, lineage, monitoring, provenance, temporal, vaccination

In this work, we analyse the potential for using structural knowledge to improve the detection of the DNA-binding helix–turn–helix (HTH) motif from sequence. Starting from a set of DNA-binding protein structures that include a functional HTH motif and have no apparent sequence similarity to each other, two different libraries of hidden Markov models (HMMs) were built. One library included sequence modelsofwholeDNA-bindingdomains,whichincorporate the HTH motif, the second library included shorter models of ‘partial ’ domains, representing only the fraction of the domain that corresponds to the functionally relevant HTH motif itself. The libraries were scanned against a dataset of protein sequences, some containing the HTH motifs, others not. HMM predictions were compared with the results obtained from a previously published structure-based method and subsequently combined with it. The combined method proved more effective than either of the single-featured approaches, showing that information carried by motif sequences and motif structures are to some extent complementary and can successfully be used together for the detection of DNAbinding HTHs in proteins of unknown function.

ProTarget is a Web-based tool for the automatic prediction of fold novelty. It offers the structural genomics community a method for target selection by providing an online analysis of any new or preexisting sequence for its relationship to any previously solved three-dimensional structure. ProTarget takes as input an amino acid sequence. Regions of this sequence that exhibit high similarity to an existing PDB (Protein Data Bank) sequence are removed, leaving one or more subsequences. Each of these subsequences is then analyzed against a clustering of the protein space to determine the likelihood of its representing a new structural superfamily. This likelihood is derived from the distance in the clustering between the (sub)sequence and sequences that have known structures. The output of ProTarget is a graphical visualization of the protein of interest together with the likelihood that a protein sequence represents a novel structural superfamily. ProTarget is updated regularly and currently covers over 160 000 protein sequences from the SwissProt and PDB databases. ProTarget is available at

THEMATICS (Theoretical Microscopic Titration Curves) is a simple, reliable computational predictor of the active sites of enzymes from structure. Our method, based on well-established Finite Difference Poisson-Boltzmann techniques, identifies the ionisable residues with anomalous predicted titration behaviour. A cluster of two or more such perturbed residues is a very reliable predictor of the active site. The power of the method is that it only requires the three-dimensional structure as input. The protein does not have to bear any resemblance in sequence or structure to any previously characterized protein. The disadvantage of the method is that it does require the structure. We now present evidence that THEMATICS can also locate the active site in structures built by comparative modelling from similar structures. Results are given for three sets of orthologous proteins (Triosephosphate isomerase, 6-Hydroxymethyl-7,8dihydropterin pyrophosphokinase, and Aspartate aminotransferase) and for one set of human homologues of Aldose reductase with different functions. In all of the cases studied, THEMATICS correctly locates the active site in the model structures. This suggests that the method can be applicable to proteins for which an experimentally determined structure is unavailable.

5.1 Analyzing conformational flexibility using molecular dynamics trajectories

Abstract not found

The level of sequence similarity that implies similarity in protein structure is well established. Recently, many groups proposed thresholds for similarity in sequence implying similarity in enzymatic function. All previous results suggest the strong conservation of enzymatic function above levels of 50 % pairwise sequence identity. Here, I argue that all groups substantially overestimated the conservation of enzyme function because their data sets were either too biased, or too small. An unbiased analysis suggested that less than 30 % of the pair fragments above 50 % sequence identity have entirely identical EC numbers. Another surprising finding was that even BLAST E-values below 10250 did not suffice to automatically transfer enzyme function without errors. As expected, most misclassifications originated from similarities in relatively short regions and/or from transferring annotations for different domains. Both problems cannot be corrected easily by adjusting the thresholds for automatic transfer of genome annotations. A score relating sequence identity

Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have not been determined, but that represent an as yet unknown structural superfamily or fold. Once such a structure is solved, it can be used as a template for modelling homologous proteins. This will aid in unveiling the structural diversity of the protein space. Currently, no reliable method for accurate 3D structural prediction is available when a sequence or a structure homologue is not available. Here we present a systematic methodology for selecting target proteins whose structure is likely to adopt a new, as yet unknown superfamily or fold. Our method takes advantage of a global classification of the sequence space as presented by ProtoNet-3D, which is a hierarchical agglomerative clustering of the proteins of interest (the proteins in Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet structurally solved, along with the probability of each of the proteins belonging to a new superfamily or fold. The sorted list has been self-validated against real structural data that was not available when the predictions were made. The practical application of using our computational–statistical method to determine novel superfamilies for structural genomics projects is also discussed. Copyright © 2003 John Wiley & Sons, Ltd.

provides structural genomics with new insights into protein family space