Persistent spectral theory-guided protein engineering

Qiu, Yuchi; Wei, Guo-Wei

doi:10.1038/s43588-022-00394-y

Cited by 32 publications

(23 citation statements)

References 52 publications

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“…Alphafold2 is currently the most popular tool for protein folding, which combines knowledge of protein structure with deep learning . While Alphafold2 has achieved significant success in protein folding prediction, its predictive accuracy is lower compared to experimental techniques such as X-ray crystallography . Additionally, running Alphafold2 requires substantial computational resources.…”

Section: Discussionmentioning

confidence: 99%

“…581 While Alphafold2 has achieved significant success in protein folding prediction, its predictive accuracy is lower compared to experimental techniques such as X-ray crystallography. 582 Additionally, running Alphafold2 requires substantial computational resources. Other advanced methods such as DeepSVM fold have also been proposed, which achieved a prediction accuracy of 67.3% and outperformed other methods.…”

Section: Protein Folding Predictionmentioning

confidence: 99%

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Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

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Section: Discussionmentioning

confidence: 99%

Section: Protein Folding Predictionmentioning

confidence: 99%

Machine Learning Methods for Small Data Challenges in Molecular Science

et al. 2023

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“…It is generally agreed that for many proteins, sequence determines structure, and structure strongly influences function. Thus, there have been efforts to enrich protein representations by incorporating structural information using voxels, contact maps, or graph neural networks. − However, these have not led to significant performance improvements, likely because variant structures vary in subtle yet impactful ways which are challenging to model and extremely difficult to observe experimentally, despite an explosion in protein structure prediction tools. Many available protein structures may be quite noisy or inaccurate.…”

Section: Navigating Protein Fitness Landscapes Using 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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“…The resource-intensive nature hinders model revisions from a few labeled data, and the latter learning requirement conditions effective model training on substantial volumes of high-quality protein data. Alternatively, topology- or geometry-based models encode protein structures for effective local environment representation. ,,− These models are less data-intensive and can potentially capture crucial characteristics for topology-aware protein properties, such as binding affinity and thermostability. To this end, ProtLGN employed roto-translation equivariant graph convolutions that efficiently analyze the microenvironment of AAs at the structure level, considering the physicochemical properties of its spatially closed AA neighbors.…”

Section: Introductionmentioning

confidence: 99%

Protein Engineering with Lightweight Graph Denoising Neural Networks

Zhou,

Zheng,

et al. 2024

J. Chem. Inf. Model.

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Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen−antibody affinity, thermostability, and DNA cleavage activity. More than 40% of PROTLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

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Persistent spectral theory-guided protein engineering

Cited by 32 publications

References 52 publications

Machine Learning Methods for Small Data Challenges in Molecular Science

Machine Learning Methods for Small Data Challenges in Molecular Science

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Protein Engineering with Lightweight Graph Denoising Neural Networks

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