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Overlapping genes are commonplace in viruses and play an important role in their function and evolution. Coevolution in OVerlapped sequences by Tree analysis (COVTree) is a web server providing the online analysis of coevolving amino-acid pairs in overlapping genes, where residues might be located inside or outside the overlapping region.
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We propose PhyloSofS, the first automated tool to reconstruct plausible evolutionary scenarios explaining a set of observed transcripts, and to generate 3D molecular models of the protein isoforms. We apply it to the JNK family (60 transcripts, 7 trees) to identify AS events of ancient origin and relate their functional outcome with changes in the protein dynamics. We also show that PhyloSofS can help identify new potential therapeutic targets.
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A.Carbone and F.Oteri work on the Spike protein of SARS2 in collaboration with the group of F.L.Cosset at CIRI in Lyon, expert in non-replicative retroviral pseudoparticles. Based on coevolution analysis of patient sequences and sequences from bats and other species, they aim at identifying key residues in the Spike protein involved in the entry mechanism of the virus in human cells. In the past, the two groups successfully combined their computational and experimental methods to unravel critical features of the original HCV fusion mechanism.
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GEMME is a fast, scalable and simple method to predict mutational landscapes from natural sequences. It demonstrates how deleterious effects of a protein mutation are identified by looking at the closest known sequence accepting the mutation in the evolutionary tree of sequences and at its epistatic changes. The article just appeared in Molecular Biology and Evolution.
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We propose the concept of "interacting region" and the dynJET2 method toward deciphering the complexity underlying protein surface usage and deformability. Interacting regions account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. dynJET2 predicts interacting patches by crossing evolutionary, physico‐chemical and geometrical properties of the protein surface with information coming from complete cross‐docking (CC‐D) simulations.
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Cosa possono dirci le proteine su come interagiscono e lavorano insieme? Al LCQB sviluppiamo approcci computazionali per predire interazioni proteiche, per modellare il comportamento sociale delle proteine e per dedurre l'effetto delle mutazioni sulle reti di interazione proteica.
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Que peuvent nous dire les protéines sur la manière dont elles interagissent et fonctionnent ensemble? Au LCQB, nous développons des approches computationnelles pour prédire les interactions protéiques, modéliser le comportement social des protéines et en déduire l’effet des mutations sur les réseaux d’interaction protéique.
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