Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 8, 2024

Abstract Engineering enzyme biocatalysts for higher efficiency is key to enabling sustainable, ‘green’ production processes the chemical and pharmaceutical industry. This challenge can be tackled from two angles: by directed evolution, based on labor-intensive experimental testing of variant libraries, or computational methods, where sequence-function data are used predict biocatalyst improvements. Here, we combine both approaches into a two-week workflow, ultra-high throughput screening library imine reductases (IREDs) in microfluidic devices provides not only selected ‘hits’, but also long-read sequence linked fitness scores >17 thousand variants. We demonstrate engineering an IRED chiral amine synthesis mapping functional information one go, ready interpretation extrapolation protein engineers with help machine learning (ML). calculate position-dependent mutability combinability mutations comprehensively illuminate complex interplay driven synergistic, often positively epistatic effects. Interpreted easy-to-use regression tree-based ML algorithms designed suit evaluation random whole-gene mutagenesis data, 3-fold improved ‘hits’ obtained extrapolated further give up 23-fold improvements catalytic rate after handful mutants. Our campaign paradigmatic future that will rely access large maps as profiles way responds mutation. These chart function exploiting synergy rapid combined extrapolation.

Язык: Английский

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The Development and Opportunities of Predictive Biotechnology

ChemBioChem, Год журнала: 2024, Номер 25(13)

Опубликована: Май 7, 2024

Recent advances in bioeconomy allow a holistic view of existing and new process chains enable novel production routines continuously advanced by academia industry. All this progress benefits from growing number prediction tools that have found their way into the field. For example, automated genome annotations, for building model structures proteins, structural protein methods such as AlphaFold2

Язык: Английский

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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

Molecules, Год журнала: 2024, Номер 29(19), С. 4626 - 4626

Опубликована: Сен. 29, 2024

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design proteins with unprecedented precision functionality. Computational methods now play a crucial role enhancing stability, activity, specificity for diverse applications biotechnology medicine. Techniques such as deep reinforcement transfer learning have dramatically improved structure prediction, optimization binding affinities, enzyme design. These innovations streamlined process allowing rapid generation targeted libraries, reducing experimental sampling, rational tailored properties. Furthermore, integration approaches high-throughput techniques facilitated development multifunctional novel therapeutics. However, challenges remain bridging gap between predictions validation addressing ethical concerns related to AI-driven This review provides comprehensive overview current state future directions engineering, emphasizing their transformative potential creating next-generation biologics advancing synthetic biology.

Язык: Английский

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gRNAde: Geometric Deep Learning for 3D RNA inverse design

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 1, 2024

Computational RNA design tasks are often posed as inverse problems, where sequences designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, geometric pipeline operating backbones to that explicitly account for dynamics. gRNAde uses multi-state Graph Neural Network autoregressive decoding generates candidate conditioned one or more backbone structures the identities of bases unknown. On single-state fixed re-design benchmark 14 from PDB identified by Das et al. (2010), obtains higher native sequence recovery rates (56% average) compared Rosetta (45% average), taking under second produce designs reported hours Rosetta. further demonstrate utility new structurally flexible RNAs, well zero-shot ranking mutational fitness landscapes in retrospective analysis recent ribozyme. Open source code: github.com/chaitjo/geometric-rna-design.

Язык: Английский

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Design of microbial catalysts for two-stage processes

Nature Reviews Bioengineering, Год журнала: 2024, Номер 2(12), С. 1039 - 1055

Опубликована: Авг. 22, 2024

Язык: Английский

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