ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design

Praljak, Nikša; Lian, Xinran; Ranganathan, Rama; Ferguson, Andrew L.

doi:10.1021/acssynbio.3c00261

Cited by 5 publications

(4 citation statements)

References 57 publications

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“…The performance of our zero-shot library contributes to a growing body of evidence showing that samples from models fit on natural sequences can be used to generate libraries that are not only enriched for functional variants 82,97,98 but also contain variants with improved fitness 42,59,[99][100][101][102][103][104][105] , even though the zero-shot sampling process did not take the target phenotype of the engineering campaign into account. We encourage further exploration of these techniques for initial library design 63,106 , particularly in lower-throughput settings where improving hit rates can increase the chance of finding at least one satisfactory variant (Figure 5b).…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, our ML campaign outperformed two directed evolution approaches that used the same platform: one that was run independently and using standard in-vitro techniques for hit selection and diversification and one that was designed in-silico and pooled with the ML-designed libraries for screening. Finally, the performance of our zero-shot library contributes to a growing body of evidence showing that samples from models fit on natural sequences can be used to generate libraries that are not only enriched for functional variants [76][77][78] but also contain variants with improved fitness 41,59,[79][80][81][82][83][84][85] .…”

Section: Discussionmentioning

confidence: 99%

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Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Thomas,

Belanger,

et al. 2024

Preprint

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Designing enzymes to function in novel chemical environments is a central goal of synthetic biology with broad applications. Guiding protein design with machine learning (ML) has the potential to accelerate the discovery of high-performance enzymes by precisely navigating a rugged fitness landscape. In this work, we describe an ML-guided campaign to engineer the nuclease NucB, an enzyme with applications in the treatment of chronic wounds due to its ability to degrade biofilms. In a multi-round enzyme evolution campaign, we combined ultra-high-throughput functional screening with ML and compared to parallelin-vitrodirected evolution (DE) andin-silicohit recombination (HR) strategies that used the same microfluidic screening platform. The ML-guided campaign discovered hundreds of highly-active variants with up to 19-fold nuclease activity improvement, while the best variant found by DE had 12-fold improvement. Further, the ML-designed hits were up to 15 mutations away from the NucB wildtype, far outperforming the HR approach in both hit rate and diversity. We also show that models trained on evolutionary data alone, without access to any experimental data, can design functional variants at a significantly higher rate than a traditional approach to initial library generation. To drive future progress in ML-guided design, we curate a dataset of 55K diverse variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date. Data and code is available at:https://github.com/google-deepmind/nuclease_design.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Thomas,

Belanger,

et al. 2024

Preprint

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“…In pure sequence generation, protein language models (PLMs) can be conditioned by a known enzyme family to generate novel sequences with that function, without direct consideration of structure (Figure D). − Models with transformer architectures have generated enzymes such as lysozymes, malate dehydrogenases, and chorismate mutases: for the best models, up to 80% of wet-lab validated sequences expressed and functioned. , Some of these generated sequences have low sequence identity (<40%) to known proteins and may be quite different from those explored by evolution, thus potentially unlocking combinations of properties not found in nature. Variational autoencoders (VAEs) have been used to generate phenylalanine hydroxylases and luciferases, with wet-lab validation achieving 30–80% success rates. ,, Generative adversarial networks (GANs) were also applied to the generation of malate dehydrogenases, with 24% success rate . Alternatively, a diffusion model such as EvoDiff could achieve better coverage of protein functional and structural space during generation .…”

Section: Discovery Of Functional Enzymes With Machine Learningmentioning

confidence: 99%

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Yang,

Li,

Arnold

2024

ACS Cent. Sci.

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Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiencyor even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

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“…Thanks to the use of deep learning, protein engineering has seen tremendous advances, allowing researchers to design reliable sequence and structure prediction techniques. State-of-the-art fast structure prediction techniques such as AlphaFold2 have played a crucial role in this progress, granting greater access to protein structures and accelerating research involving the design of new proteins and peptides [7][8][9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

Vishva Saravanan,

Choudhuri,

Ghosh

2024

Preprint

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The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA: a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used forde novodesign of peptide binders given target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. The ability of our model to generate peptides conditioned on the target receptor’s binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

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ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design

Cited by 5 publications

References 57 publications

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

A Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

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