Protein engineering, a rapidly evolving field of biotechnology, has the potential to revolutionize various sectors, including antibody design, drug discovery, food safety and ecology. Traditional methods such as directed evolution and rational design have been instrumental. However, the vast mutational space makes these approaches expensive, time-consuming and limited in scope. Leveraging large protein databases and advanced ML models, especially those inspired by NLP, has significantly accelerated the protein engineering process. Advances in topological data analysis (TDA) and AI-based protein structure prediction tools like AlphaFold2 have further enhanced the capabilities of structure-based ML-assisted protein engineering strategies.
Machine learning-assisted protein engineering (MLPE) leverages data-driven techniques to improve the efficiency and effectiveness of protein engineering. ML models can rapidly generate and test numerous protein variants by analyzing and predicting the impacts of mutations, thereby optimizing the protein-to-fitness landscape, even with limited experimental data. MLPE involves a comprehensive approach integrating data collection, feature extraction, model training and iterative validation, supported by high-throughput sequencing and screening technologies.
Advanced mathematical tools such as TDA and NLP based models play a crucial role in data representation, which is essential for accurate model training and prediction. Despite substantial progress, challenges such as data preprocessing, feature extraction, and iterative optimization persist. The review addresses these issues and discusses potential future directions in the field, aimed at further improving MLPE methodologies and results.
Sequence-based deep protein language models:
Recent advances in NLP have inspired computational methods to analyze protein sequences, treating them in the same way as human languages. Sequence-based protein language models, leveraging local evolutionary data from homologs and global data from large protein databases like UniProt, have been developed to predict the structural and functional properties of proteins. Techniques range from local models using Hidden Markov Models (HMM) and Variational Autoencoders (VAE) to global models using large NLP architectures like Transformers. Hybrid approaches, such as fine-tuning global models with local data, further improve forecast accuracy, exemplified by models like eUniRep and Transcription.
Structure-based topological data analysis (TDA) models:
Structure-based models using TDA address the limitations of sequence-based models by incorporating stereochemical information. TDA, rooted in algebraic topology, characterizes complex geometric data and discovers topological structures. Persistent homology, a key TDA method, analyzes multi-scale data, while persistent cohomology and element-specific persistent homology (ESPH) improve this by including heterogeneous data. Persistent topological Laplacians further capture the complexity of the data. GNNs and topological deep learning combine connectivity and shape information, advancing protein structure analysis and function prediction with applications in drug discovery and protein engineering.
AI-assisted protein engineering: challenges and solutions:
Protein engineering is a complex optimization problem that aims to identify the optimal amino acid sequence that maximizes specific properties such as activity, stability, and selectivity. This problem is compounded by the vastness of sequence space and the epistatic nature of the fitness landscape, where interactions between amino acids are highly interdependent and nonlinear. Traditional methods such as directed evolution often remain trapped in local optima and need help navigating the high-dimensional fitness landscape. Furthermore, experimental approaches are limited by the large number of possible mutations and the limited testing throughput, which makes exhaustive exploration of the entire sequence space impossible.
Recent advances in machine learning have significantly improved the protein engineering process by enabling efficient exploration and optimization within this vast search space. Machine learning models, leveraging limited experimental data, can predict protein fitness with high accuracy using techniques such as zero-shot and few-shot learning. Zero-shot models, like VAEs and Transformers, can assess the likelihood that a new protein sequence is functional by recognizing natural protein patterns. On the other hand, supervised regression models, including deep and ensemble learning methods, use labeled data to predict fitness landscapes and guide the search for optimal sequences. Active learning strategies refine this process by balancing exploration and exploitation, using uncertainty quantification models such as Gaussian processes to navigate the fitness landscape more efficiently. This iterative approach, integrating machine learning predictions and experimental validation, is crucial for achieving optimal solutions in protein engineering.
Conclusion:
The review highlights advances in deep protein language models and topological data analysis methods for protein modeling, emphasizing accelerated progress in protein engineering using MLPE methods. Structure-based models often outperform sequence-based ones due to more comprehensive data on protein properties despite the limited availability of structural data. State-of-the-art methods like AlphaFold2 and RosettaFold expand structural databases with high accuracy. Future directions include the development of alignment-free prediction methods, sophisticated TDA techniques, and large-scale deep learning models to utilize large datasets from advanced biotechnologies such as next-generation sequencing.
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Sana Hassan, Consulting Intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-world solutions.
