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Marelli, B.

Publications and source records attributed to Marelli, B..

2 recordsLinked to original sources

Tardigrade secretory proteins protect biological structures from desiccation

Tardigrades, microscopic animals that survive a broad range of environmental stresses, express a unique set of proteins termed tardigrade-specific intrinsically disordered proteins (TDPs). TDPs are often expressed at high levels in tardigrades upon desiccation, and appear to mediate stress adaptation. Here, we focused on the proteins belonging to the secretory family of tardigrade proteins termed secreted-abundant heat soluble ("SAHS") proteins, and investigated their ability to protect diverse biological structures. Recombinantly expressed SAHS proteins prevented desiccated liposomes from fusion, and enhanced desiccation tolerance of E. coli and Rhizobium tropici upon extracellular application. Molecular dynamics simulation and comparative structural analysis suggest a model by which SAHS proteins may undergo a structural transition upon desiccation, in which removal of water and solutes from a large internal cavity in SAHS proteins destabilizes the beta-sheet structure. These results highlight the potential application of SAHS proteins as stabilizing molecules for preservation of cells.

molecular biology↗

Artificial intelligence method to design and fold alpha-helix structural proteins from the primary amino acid sequence

The development of rational techniques to discover new proteins for use in variety of applications ranging from agriculture to biotechnology remains an outstanding materials design problem. The key barrier is to design a sequence to fold into a predictable structure to achieve a certain material function. Focused on alpha-helical proteins, we report a Multi-scale Neighborhood-based Neural Network (MNNN) model to learn how a specific amino acid sequence folds into a protein structure. The algorithm predicts the protein structure without using a template or co-evolutional information at a maximum error of 2.1 [A]. We find that the prediction accuracy is higher than other models and the prediction consumes less than six orders of magnitude time than ab initio folding methods. We demonstrate that MNNN can predict the structure of an unknown protein that agrees with experiments, and our model hence shows a great advantage in the rational design of de novo proteins.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC=\"FIGDIR/small/660639v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (66K):\norg.highwire.dtl.DTLVardef@8771fcorg.highwire.dtl.DTLVardef@4c7255org.highwire.dtl.DTLVardef@e63f1eorg.highwire.dtl.DTLVardef@39e4ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗