Aug 2026· TIBS -Trends in Biochemical Sciences. Regular ed· 0 citations· 68 references
Medicine
TL;DR
How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.
Abstract
Research on the structural and functional potential of minimal amino acid alphabets is shifting from reductive 'pruning' of extant proteins to bottom-up exploration of combinatorial sequence space. This review highlights the experimental and computational toolkits driving this transition. We discuss how solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering. Integrating these approaches with generative machine learning and de novo protein design allows researchers to move beyond observing what evolution produced toward exploring what chemistry permits. Together, these advances are redirecting the field from simplifying extant proteins to uncovering the physicochemical principles governing protein foldability, while expanding the design space beyond the constraints imposed by biological evolution.
Raygun is introduced, a generative artificial intelligence framework that enables miniaturization, modification and augmentation of proteins, using a probabilistic encoding of protein sequences constructed from language model embeddings, enabling the kind of coordinated, large-scale sequence modifications that characte...
Kapil Devkota, Daichi Shonai, Joey Mao et al.· Nature· 1 citation
An approach to create novel, functional proteins through the integration of deep mutational scanning, structural analysis, and evolutionary mining within prompts for a generative protein language model (PLM) is described and the utility of this approach is demonstrated with the generation of novel compact RNA-guided nu...
Nicholas W. Hughes, Sourab Kulkarni, Grant Goldman et al.· bioRxiv· 0 citations
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
Guodong Min, Huan Peng· Methods in molecular biology· 0 citations
An integrated framework combining generative artificial intelligence, computational prediction, and experimental validation was established and 13 short-sequence elements with low molecular weight and minimal redundancy formed condensates in E. coli, with enhanced protein recruitment compared to native elements.
Di Zhang, Bin-Yun Zhang, Cheng Zheng et al.· Synthetic and Systems Biotec...· 1 citation
A protein design strategy is used that couples a structure-guided inverse-folding model with evolution-informed residue constraints to generate active, divergent variants of TnpB, a minimal CRISPR-Cas12-like nuclease, termed SynTnpBs, establishing a strategy for creating non-natural RNA-guided nucleases and conformatio...
Petr Skopintsev, Isabel Esain-Garcia, Evan C. DeTurk et al.· Science· 2 citations
This review examines current computational strategies for exploring constrained protein fitness landscapes, including sequence-derived evolutionary descriptors, structural fitness assessment, energetic evaluation, and integrated multi-parameter scoring.
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.
