Comprehensive classification of the plant non-specific lipid transfer protein superfamily towards its sequence-structure-function analysis.

BACKGROUND: Non-specific Lipid Transfer Proteins (nsLTPs) are widely distributed in the plant kingdom and constitute a superfamily of related proteins. Several hundreds of different nsLTP sequences-and counting-have been characterized so far, but their biological functions remain unclear. It has been clear for years that they present a certain interest for agronomic and nutritional issues. Deciphering their functions means collecting and analyzing a variety of data from gene sequence to protein structure, from cellular localization to the physiological role. As a huge and growing number of new protein sequences are available nowadays, extracting meaningful knowledge from sequence-structure-function relationships calls for the development of new tools and approaches. As nsLTPs show high evolutionary divergence, but a conserved common right handed superhelix structural fold, and as they are involved in a large number of key roles in plant development and defense, they are a stimulating case study for validating such an approach. METHODS: In this study, we comprehensively investigated 797 nsLTP protein sequences, including a phylogenetic analysis on canonical protein sequences, three-dimensional structure modeling and functional annotation using several well-established bioinformatics programs. Additionally, two integrative methodologies using original tools were developed. The first was a new method for the detection of (i) conserved amino acid residues involved in structure stabilization and (ii) residues potentially involved in ligand interaction. The second was a structure-function classification based on the evolutionary trace display method using a new tree visualization interface. We also present a new tool for visualizing phylogenetic trees. RESULTS: Following this new protocol, an updated classification of the nsLTP superfamily was established and a new functional hypothesis for key residues is suggested. Lastly, this work allows a better representation of the diversity of plant nsLTPs in terms of sequence, structure and function.

Structure and activity analysis of two spider toxins that alter sodium channel inactivation kinetics.

In this work, Phoneutria nigriventer toxins PnTx2-5 and PnTx2-6 were shown to markedly delay the fast inactivation kinetics of neuronal-type sodium channels. Furthermore, our data show that they have significant differences in their interaction with the channel. PnTx2-6 has an affinity 6 times higher than that of PnTx2-5, and its effects are not reversible within 10-15 min of washing. PnTx2-6 partially (59%) competes with the scorpion alpha-toxin AaHII, but not with the scorpion beta-toxin CssIV, thus suggesting a mode of action similar to that of site 3 toxins. However, PnTx2-6 is not removed by strong depolarizing pulses, as in the known site 3 toxins. We have also established the correct PnTx2-5 amino acid sequence and confirmed the sequence of PnTx2-6, in both cases establishing that the cysteines are in their oxidized form. A structural model of each toxin is proposed. They show structures with poor alpha-helix content. The model is supported by experimental and theoretical tests. A likely binding region on PnTx2-5 and PnTx2-6 is proposed on the basis of their different affinities and sequence differences.

PEPOP: computational design of immunogenic peptides.

BACKGROUND: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides. RESULTS: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated. CONCLUSION: PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at http://diagtools.sysdiag.cnrs.fr/PEPOP/.