De novo protein design—From new structures to programmable functions

Kortemme, Tanja

doi:10.1016/j.cell.2023.12.028

Cited by 62 publications

(5 citation statements)

References 106 publications

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“…Materials like silk, elastin or collagen therefore have inspired the design of synthetic peptide-based materials that have long been studied for biomedical applications [1][2][3][4] . Increasingly, self-assembling protein nanomaterials are created from natural or designed peptides and protein domains by leveraging a fast-growing knowledgebase on such building blocks as advances in de novo protein design [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

“…Materials like silk, elastin or collagen therefore have inspired the design of synthetic peptide-based materials that have long been studied for biomedical applications 1–4 . Increasingly, self-assembling protein nanomaterials are created from natural or designed peptides and protein domains by leveraging a fast-growing knowledgebase on such building blocks as advances in de novo protein design 5–9 . Significantly fewer examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization and exhibit robust assembly properties for macroscale material formation.…”

Section: Introductionmentioning

confidence: 99%

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Design of a genetically programmable and customizable protein scaffolding system for the hierarchical assembly of robust, functional macroscale materials

Zhang,

Kang,

Gaascht

et al. 2024

Preprint

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Inspired by the properties of natural protein-based biomaterials, protein nanomaterials are increasingly designed with natural or engineered peptides, or with protein building blocks. Very few examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization, and exhibit robust assembly properties for macroscale material formation. Here, we designed a protein-scaffolding system that self-assembles into robust, macroscale materials suitable for cell-free applications. By controlling the co-expression in E. coli of self-assembling scaffold building blocks with and without modifications for covalent attachment of cross-linking cargo proteins, hybrid scaffolds with spatially organized conjugation sites are overproduced that can be readily isolated. Cargo proteins, including enzymes, are rapidly cross-linked onto scaffolds for the formation of functional materials. We show that these materials can be used for the cell-free operation of a co-immobilized two-enzyme reaction and that the protein material can be recovered and reused. We believe that this work will provide a versatile platform for the design and scalable production of functional materials with customizable properties and the robustness required for biotechnological applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a genetically programmable and customizable protein scaffolding system for the hierarchical assembly of robust, functional macroscale materials

Zhang,

Kang,

Gaascht

et al. 2024

Preprint

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“…Core attributes of AI in preclinical drug development are molecular screening and target identification, thanks to the use of traditional machine learning and neural networks. More precisely, and regarding drug design, AI may generate in silico-designed molecules and analogs with specific properties [3]. We previously described the timeline for the implication of AI in anticancer drug development [4].…”

Section: Introductionmentioning

confidence: 99%

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development

Chamorey,

Gal,

Mograbi

et al. 2024

Pharmaceuticals

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The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.

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“…The field of computational protein design has achieved major breakthroughs in recent years [ 1 ] in terms of its ability to design proteins and protein assemblies with diverse folds and functions (see, e.g., [ 2 – 9 ]), which has already found application in the design of therapeutically relevant biomolecules such as vaccines [ 10 ] and antibodies [ 11 , 12 ]. Such breakthroughs are built upon improvements in atomistic modeling techniques, such as the Rosetta software suite [ 13 ], and recent advances in machine learning-based structure prediction models [ 14 – 18 ], sequence design (or inverse folding) models [ 19 – 25 ], protein language models [ 26 – 34 ], and denoising diffusion probabilistic models [ 35 – 44 ].…”

Section: Introductionmentioning

confidence: 99%

An integrative approach to protein sequence design through multiobjective optimization

Hong,

Kortemme

2024

PLoS Comput Biol

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With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

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De novo protein design—From new structures to programmable functions

Cited by 62 publications

References 106 publications

Design of a genetically programmable and customizable protein scaffolding system for the hierarchical assembly of robust, functional macroscale materials

Design of a genetically programmable and customizable protein scaffolding system for the hierarchical assembly of robust, functional macroscale materials

Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development

An integrative approach to protein sequence design through multiobjective optimization

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