Toward a Minimal Amino Acid Alphabet for Protein Design

Prebiotic cationic amino acids support formation of an ancient protein fold

Charged amino acid propensities and solubility: lysine is elevated at the termini of helices in E. coli proteins

An emerging result in the relationship between amino acid sequence and protein solubility is a preference, on average, for lysine over arginine in more soluble proteins. The termini of helices are known to be prone to partial unfolding, often employing N- and C-cap amino acids to maintain stability. Hypothesising that lysine/arginine differences in relation to solubility may be evident at helical termini, their propensities and predicted charge interactions in helices were examined. There is enrichment of lysine over arginine at helical termini in AlphaFold models of Escherichia coli proteins, more so (on average) for the most soluble proteins. Similar effects are seen for the sum of charged amino acids at helical termini. Regions other than helical termini also show correlation of lysine composition, and overall charged amino acid composition, with solubility. These results suggest that 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.

Expanding all-α-helical protein space through rational computational design

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 structural and functional complexity by modular combination of recurring protein domains13. Approximately 25% of these natural domains are mostly α-helical structures14. Here we show how these can be expanded using rational computational design. Following the domain classification scheme CATH15, we build complex all-α de novo proteins hierarchically using sequence-to-structure relationships for helix-helix interactions, systematic rules to connect helices, computational tools to design loops, and in silico evaluation. The pipeline starts with a target architecture of free-standing helices. These are connected into a topology by considering local arrangements of helical bundles using understood sequence-to-structure relationships for helix packing. Single-chain sequences are completed using template- and AI-based methods. Finally, AlphaFold models are assessed to give small numbers of designs for experimental validation. We test 31 designs for 14 different architectures and 25 topologies. 75% of these express as stable, monomeric, water-soluble proteins; and >30% yield X-ray crystal structures matching the designs to atomic accuracy and with new-to-nature structures. Finally, several of the scaffolds are functionalised through one-shot designs to deliver ion, small-molecule and protein binders.

Exome-matched protein as the ideal dietary amino acid pattern: A narrative review.

With few exceptions, all organisms on Earth use a common amino acid alphabet for synthesizing protein that consists of 20 canonical amino acids. Heterotrophic organisms have to obtain these amino acids from their food, especially the indispensable amino acids. The amount of and ratio between the indispensable amino acids is therefore a major determinant of protein quality. There exist several measures of protein quality for human nutrition and all of them rely on the empirical estimation of amino acid requirements. With the availability of whole genome sequencing data, however, a new method of deductively deriving amino acid requirements became available which uses the information encoded on the exome (the totality of protein-coding exons). By translating an organism's exome into the corresponding proteome in silico, the average encoded amino acid pattern can be computed and used ex hypothesi for defining an ideal amino acid pattern. Here I review the theoretical concepts behind exome-matched proteins and summarize the preclinical data that provide preliminary evidence for the hypothesis that such proteins indeed constitute an ideal protein source to maximize growth, reproduction and health. While clinical trials are yet to confirm this hypothesis in humans, there is a broad hypothetical range of applications of exome-matched proteins for human consumption. This review concludes 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.

HighMorph: De Novo Cyclic Peptide Sequence Design via Protein–Protein Interaction Recapitulation

Cyclic peptides have emerged as a compelling class of bioactive scaffolds, but de novo design of target-binding cyclic peptides from protein structures remains challenging. Here, we present HighMorph, an interaction-guided framework that combines protein–protein interaction information with artificial intelligence for rational cyclic peptide design. HighMorph integrates Monte Carlo tree search with a Transformer-based policy-value network to efficiently explore cyclic peptide sequence space, while incorporating explicit atomic-level hydrogen bond constraints extracted from reference protein–protein complexes to guide sequence optimization. The framework is systematically validated on two clinically relevant targets, programmed death-ligand 1 (PD-L1) and kallikrein-related peptidase 4 (KLK4). Notably, 33.3% and 40% of the generated candidates are active against PD-L1 and KLK4, respectively, with active cyclic peptides exhibiting micromolar binding affinities (approximately 10–6 M). These results validate our approach for cyclic peptide design. Additionally, interaction analysis provides insights for developing therapeutics targeting challenging protein interfaces.

HighPlay2: Structure-guided design of cyclic peptide candidates containing non-canonical amino acids.

HighPlay2 is presented as a feasible framework for the early-stage design and screening of cyclic peptide candidates containing ncAAs, while further affinity maturation and experimental structural validation remain necessary.