The results show that globular proteins may have formed early in evolution and show that it is possible to design proteins with interesting properties for biotechnology and synthetic biology.
Abstract
Proteins are built from 20 canonical amino acids. It is interesting to explore whether proteins can be formed from significantly reduced amino acid alphabets. Our bioinformatics survey of UniProt (more than 250 M sequences) revealed that proteins composed of reduced amino acid alphabets (< 10) are extremely rare among existing proteins. Next, we used computational protein design to design proteins composed of all 1,013 possible alphabets of 2-10 early amino acids (Ala, Asp, Glu, Gly, Ile, Leu, Pro, Ser, Thr, and Val). The length of all proteins was 100 amino acid residues. Small amino acid alphabets preferred simple helices or helix bundles. Larger amino acid alphabets allowed for the design of more complex structures. A protein composed of 8 amino acid types (Ala, Asp, Gly, Leu, Val, Ser, Thr, and Pro) was successfully experimentally verified. It adopts the β-sheet-rich fibronectin type III domain architecture. Attempts to experimentally verify designs composed of 6 and 4 amino acid types were unsuccessful. We show by a computational experiment with an experimental validation that inverse folding models, namely ProteinMPNNsol, can stabilize a designed protein within the same eight-amino-acid alphabet. Our results show that globular proteins may have formed early in evolution. Furthermore, we show that it is possible to design proteins with interesting properties for biotechnology and synthetic biology.
Crystallographic analysis revealed that both this peptide and its Dab-substituted derivative adopted the double-Z β-barrel (DZBB) fold, a likely evolutionary intermediate between extant β-barrel folds, which might have supported the foldability of primitive proteins before the incorporation of lysine and arginine into...
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Protein design protocols could improve solubility through targeting lysine enrichment in regions such as helical termini, in addition to the more conventional consideration of helix capping interactions.
De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3–7. As a result, de novo designed proteins are becoming larger and more complex8–10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved...
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It is concluded that the concept of exome-matched proteins is interesting theoretically, promising for practical applications in animal and human nutrition and stimulating for further transdisciplinary research.
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HighMorph is presented, an interaction-guided framework that combines protein–protein interaction information with artificial intelligence for rational cyclic peptide design and provides insights for developing therapeutics targeting challenging protein interfaces.
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A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.