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Abstract
BackgroundMagnesium research is increasing in molecular medicine due to the relevance of this ion in several important biological processes and associated molecular pathogeneses. It is still difficult to predict from the protein covalent structure whether a human chain is or not involved in magnesium binding. This is mainly due to little information on the structural characteristics of magnesium binding sites in proteins and protein complexes. Magnesium binding features, differently from those of other divalent cations such as calcium and zinc, are elusive. Here we address a question that is relevant in protein annotation: how many human proteins can bind Mg2+? Our analysis is performed taking advantage of the recently implemented Bologna Annotation Resource (BAR-PLUS), a non hierarchical clustering method that relies on the pair wise sequence comparison of about 14 millions proteins from over 300.000 species and their grouping into clusters where annotation can safely be inherited after statistical validation.ResultsAfter cluster assignment of the latest version of the human proteome, the total number of human proteins for which we can assign putative Mg binding sites is 3,751. Among these proteins, 2,688 inherit annotation directly from human templates and 1,063 inherit annotation from templates of other organisms. Protein structures are highly conserved inside a given cluster. Transfer of structural properties is possible after alignment of a given sequence with the protein structures that characterise a given cluster as obtained with a Hidden Markov Model (HMM) based procedure. Interestingly a set of 370 human sequences inherit Mg2+ binding sites from templates sharing less than 30% sequence identity with the template.ConclusionWe describe and deliver the "human magnesome", a set of proteins of the human proteome that inherit putative binding of magnesium ions. With our BAR-hMG, 251 clusters including 1,341 magnesium binding protein structures corresponding to 387 sequences are sufficient to annotate some 13,689 residues in 3,751 human sequences as "magnesium binding". Protein structures act therefore as three dimensional seeds for structural and functional annotation of human sequences. The data base collects specifically all the human proteins that can be annotated according to our procedure as "magnesium binding", the corresponding structures and BAR+ clusters from where they derive the annotation (http://bar.biocomp.unibo.it/mg).
Highlights
Magnesium research is increasing in molecular medicine due to the relevance of this ion in several important biological processes and associated molecular pathogeneses
Finding Magnesium binding sites with BAR+ When a human sequence has a counterpart in BAR+ with sequence identity ≥ 40% over at least 90% of the alignment length, it falls into the same cluster of the similar chain
In this work we address the problem of annotating magnesium binding sites in proteins starting from their sequence
Summary
Magnesium research is increasing in molecular medicine due to the relevance of this ion in several important biological processes and associated molecular pathogeneses. It is still difficult to predict from the protein covalent structure whether a human chain is or not involved in magnesium binding. This is mainly due to little information on the structural characteristics of magnesium binding sites in proteins and protein complexes. Observations on the structural geometry of Mg2+ binding sites in proteins known with atomic resolution may be derived from PROCOGNATE, a cognate ligand domain mapping for enzymes [5] and from the Protein Data Bank [PDB, http://www.rcsb.org]. Typical magnesium binding sites on proteins show three or fewer direct binding contacts with carbonyl oxygen atoms of the backbone and/or protein side chains, with a tendency to bind water molecules given the octahedral coordination geometry of the divalent cation [3,6]. Three dimensional Mg2+ binding pockets derived from 70 Mg2+ binding proteins solved at atomic resolution were recognised in protein structures by implementing a structural alphabet [11]
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