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