Jason Yang

I’m a 4th-year chemical engineering PhD candidate at Caltech, coadvised by Professor Frances Arnold and Professor Yisong Yue, where I am supported by the NSF GRFP and the Google PhD Fellowship. I am primarily interested in developing machine learning-assisted workflows for protein engineering, particularly for discovering and optimizing enzymes. I interned this past summer at Profluent, and I am also a co-organizer for the ML for protein engineering seminar series.

I completed my BS in chemical engineering and applied math at Yale University. During my undergrad, I worked on membranes for water treatment with Professor Menachem Elimelech, and molecular simulation and machine learning of polymers with Professor Ying Li. In the past, I have also interned at Genentech and NREL.

Outside of research, I enjoy electronic music, exploring new places and foods, and learning about history and philosophy. I also enjoy teaching, mentoring, and collaboration. Please reach out!

selected publications

Conditional Enzyme Generation Using Protein Language Models with Adapters

Jason Yang, Aadyot Bhatnagar, Jeffrey A. Ruffolo, and 1 more author

arXiv, Oct 2024

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The conditional generation of proteins with desired functions and/or properties is a key goal for generative models. Existing methods based on prompting of language models can generate proteins conditioned on a target functionality, such as a desired enzyme family. However, these methods are limited to simple, tokenized conditioning and have not been shown to generalize to unseen functions. In this study, we propose ProCALM (Protein Conditionally Adapted Language Model), an approach for the conditional generation of proteins using adapters to protein language models. Our specific implementation of ProCALM involves finetuning ProGen2 to incorporate conditioning representations of enzyme function and taxonomy. ProCALM matches existing methods at conditionally generating sequences from target enzyme families. Impressively, it can also generate within the joint distribution of enzymatic function and taxonomy, and it can generalize to rare and unseen enzyme families and taxonomies. Overall, ProCALM is a flexible and computationally efficient approach, and we expect that it can be extended to a wide range of generative language models.
Active Learning-Assisted Directed Evolution

Jason Yang, Ravi G Lal, James C Bowden, and 6 more authors

bioRxiv, Jul 2024

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Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learningassisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a nonnative cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.
CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes

Jason Yang, Ariane Mora, Shengchao Liu, and 4 more authors

arXiv, Jun 2024

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Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task. CARE is available at https://github.com/ jsunn-y/CARE/.
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Jason Yang, Francesca-Zhoufan Li, and Frances H. Arnold

ACS Central Science, Feb 2024

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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.