Exploring Machine Learning Algorithms and Numerical Representations Strategies to Develop Sequence-Based Predictive Models for Protein Networks

Medina-Ortiz, David; Salinas, Pedro; Cabas-Moras, Gabriel; Durán-Verdugo, Fabio; Olivera-Nappa, Álvaro; Uribe-Paredes, Roberto

doi:10.1007/978-3-031-36805-9_16

Cited by 1 publication

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“…ML methods have found invaluable applications in diverse biotechnology areas, including drug discovery (Rickerby et al, 2020), assessing various aspects of protein fitness such as thermostability (Csicsery-Ronay et al, 2022), stereoselectivity (Moon et al, 2021;Li et al, 2021), fluorescence properties (Somermeyer et al, 2022), predicting affinity in protein complex interactions (Medina-Ortiz et al, 2023;Liu et al, 2021), functional classification based on Enzyme Commission numbers (Shi et al, 2022;Fernández et al, 2023), recognition of biological activities in peptide sequences (Quiroz et al, 2021), photoreceptor adduct lifetime (Hemmer et al, 2023), and assessing DNA-binding proteins (Qu et al, 2019). The versatility of ML methods has resulted in their integration with traditional experimental techniques, such as DE and RD (Yang et al, 2019;Wittmann et al, 2021).…”

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confidence: 99%

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Medina-Ortiz,

Khalifeh,

Anvari-Kazemabad

et al. 2024

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Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable model’s effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.

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