Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences

Yang, Zeliang; Milas, Katarina A.; White, Andrew D.

doi:10.1101/2022.08.05.502972

Cited by 11 publications

(10 citation statements)

References 70 publications

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“…Supervised neural models are trained on experimental data to predict a regression output [44][45][46][47]. Of particular note is the structural score given by AlphaFold2, which can be used as an optimization objective to find sequences likely to fold into a desired structure [46,48]. If the model is sufficiently good, model outputs can be used as a synthetic replacement for the experimental target.…”

Section: Synthetic Landscapesmentioning

confidence: 99%

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Thomas

Agarwala

Belanger

et al. 2022

Preprint

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Advancements in DNA synthesis and sequencing technologies have enabled a novel paradigm of protein design where machine learning (ML) models trained on experimental data are used to guide exploration of a protein fitness landscape. ML-guided directed evolution (MLDE) builds on the success of traditional directed evolution and unlocks strategies which make more efficient use of experimental data. Building an MLDE pipeline involves many design choices across the design-build-test-learn loop ranging from data collection strategies to modeling, each of which has a large impact on the success of designed sequences. The cost of collecting experimental data makes benchmarking every component of these pipelines on real data prohibitively difficult, necessitating the development of synthetic landscapes where MLDE strategies can be tested. In this work, we develop a framework called SLIP (Synthetic Landscape Inference for Proteins) for constructing biologically-motivated synthetic landscapes with tunable difficulty based on Potts models. This framework can be extended to any protein family for which there is a sequence alignment. We show that without tuning, Potts models are easy to optimize. In contrast, our tuning framework provides landscapes sufficiently challenging to benchmark MLDE pipelines. SLIP is open-source and is available at https://github.com/google-research/slip.

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Section: Synthetic Landscapesmentioning

confidence: 99%

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Thomas

Agarwala

Belanger

et al. 2022

Preprint

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“…Accordingly, we found that our cRBM model better predicted binding affinity changes upon mutation that ProteinMPNN [58], a recently published structure-based autoregressive graph neural network for sequence design. Synergy between evolutionary-based and structure-based protein design approaches is well-established [68][69][70][71] and therefore, although structure-based computational design methods are rapidly improving [58,72,73], we expect that evolutionary information will still prove valuable in the future.…”

Section: Discussionmentioning

confidence: 99%

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions

Tubiana

Adriana-Lifshits²,

Nissan³

et al. 2023

PLoS Comput Biol

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Design of peptide binders is an attractive strategy for targeting “undruggable” protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

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“…Bayesian optimization is highly effective in striking this balance while being able to also incorporate prior knowledge about the problem, constraints to the search space, and the ability to optimize multiple objectives simultaneously. This has successfully been used in chemical synthesis engineering to reduce the number of experiments required to find the optimal conditions for a reaction and can similarly be applied to protein engineering. ,,,, …”

Section: Machine-learning Methods For Protein Engineeringmentioning

confidence: 99%

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

Markus,

Andreas

et al. 2023

ACS Catal.

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Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology’s limitations, unanswered questions, and unmet challenges.

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Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences

Cited by 11 publications

References 70 publications

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions

Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

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