SUMMARYFor medical biologists, sequencing has become a commonplace technique to support diagnosis. Rapid changes in this field have led to the generation of large amounts of data, which are not always correctly listed in databases. This is particularly true for data concerning class A β-lactamases, a group of key antibiotic resistance enzymes produced by bacteria. Many genomes have been reported to contain putative β-lactamase genes, which can be compared with representative types. We analyzed several hundred amino acid sequences of class A β-lactamase enzymes for phylogenic relationships, the presence of specific residues, and cluster patterns. A clear distinction was first made between dd-peptidases and class A enzymes based on a small number of residues (S70, K73, P107, 130SDN132, G144, E166, 234K/R, 235T/S, and 236G [Ambler numbering]). Other residues clearly separated two main branches, which we named subclasses A1 and A2. Various clusters were identified on the major branch (subclass A1) on the basis of signature residues associated with catalytic properties (e.g., limited-spectrum β-lactamases, extended-spectrum β-lactamases, and carbapenemases). For subclass A2 enzymes (e.g., CfxA, CIA-1, CME-1, PER-1, and VEB-1), 43 conserved residues were characterized, and several significant insertions were detected. This diversity in the amino acid sequences of β-lactamases must be taken into account to ensure that new enzymes are accurately identified. However, with the exception of PER types, this diversity is poorly represented in existing X-ray crystallographic data.

Histone modifications are fundamental to chromatin structure and transcriptional regulation, and are recognized by a limited number of protein folds. Among these folds are PHD fingers, which are present in most chromatin modification complexes. To date, about 15 PHD finger domains have been structurally characterized, whereas hundreds of different sequences have been identified. Consequently, an important open problem is to predict structural features of a PHD finger knowing only its sequence. Here, we classify PHD fingers into different groups based on the analysis of residue–residue co-evolution in their sequences. We measure the degree to which fixing the amino acid type at one position modifies the frequencies of amino acids at other positions. We then detect those position/amino acid combinations, or ‘conditions’, which have the strongest impact on other sequence positions. Clustering these strong conditions yields four families, providing informative labels for PHD finger sequences. Existing experimental results, as well as docking calculations performed here, reveal that these families indeed show discrepancies at the functional level. Our method should facilitate the functional characterization of new PHD fingers, as well as other protein families, solely based on sequence information.

Background: Identifying the active site of an enzyme is a crucial step in functional studies. While protein sequences and structures can be experimentally characterized, determining which residues build up an active site is not a straightforward process. In the present study a new method for the detection of protein active sites is introduced. This method uses local network descriptors derived from protein three-dimensional structures to determine whether a residue is part of an active site. It thus does not involve any sequence alignment or structure similarity to other proteins. A scoring function is elaborated over a set of more than 220 proteins having different structures and functions, in order to detect protein catalytic sites with a high precision, i.e. with a minimal rate of false positives.

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