Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Yang, Aerin; Jude, Kevin; Lai, Benjamin; Minot, Mason; Kocyła, Anna; Glassman, Caleb R.; Nishimiya, Daisuke; Kim, Yoon Seok; Reddy, Sai T.; Khan, Aly A.; Garcia, K Christopher

doi:10.1126/science.adh1720

Cited by 18 publications

(3 citation statements)

References 65 publications

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“…Proteins purified from M9 media, with or without ZnCl 2 , were also used for FASTpp experiments (Figure S11). Because resistance to proteolysis has been used in several recent protein stability studies (Leuenberger et al, 2017; Rocklin et al, 2017; Tsuboyama et al, 2023; Yang et al, 2023), we used FASTpp to further examine the stability of the PHD scaffold, especially given the consistency in the results of proteolysis versus other methods (Figure 4; Figure S9).…”

Section: Methodsmentioning

confidence: 99%

Assessing the functional roles of coevolving PHD finger residues

Basu,

Subedi,

Tonelli

et al. 2024

Protein Science

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Although in silico folding based on coevolving residue constraints in the deep‐learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well‐known histone reader with distinct subtypes containing subtype‐specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model‐based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families.

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Section: Methodsmentioning

confidence: 99%

Assessing the functional roles of coevolving PHD finger residues

Basu,

Subedi,

Tonelli

et al. 2024

Protein Science

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“…Protein language models are large pre-trained models utilized for sequence representation [39][40][41] and can be fine-tuned for various downstream deterministic tasks, including protein structure prediction 3,4 , de novo sequence generation [42][43][44] , protein function annotation [45][46][47][48] , and protein-protein interactions 49 . In this study, we employed the protein language models ESM2 and ESMFold 3 to construct Generative Protein Design by Language Model (GPDL), including two distinct protein structure design models, GPDL-Inpainting and GPDL-Hallucination.…”

Section: Introductionmentioning

confidence: 99%

Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method

Zhang,

Liu,

Zheng

et al. 2023

Preprint

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Proteins are essential macromolecules that play crucial roles in nearly every type of biological function. Most of the protein functions are determined by their position topologies, indicating that new functional proteins might be generated by designing their tertiary structures initially. Over the past two decades, numerous energy-based and machine learning algorithms have been proposed forde novoprotein design. However, optimizing these models to achieve a balanced performance among three critical aspects, namely sample fidelity, diversity, and generation speed, remains a challenging task on account of the constrained residue connections and hydrophobic interactions. Here we used the protein language model (pLM) as supervision to develop two complementary deep learning algorithms, Generative Protein Design by Language Model-Inpainting (GPDL-Inpainting) and GPDL-Hallucination, that can sufficiently represent and generate the protein structures and sequences. We compared the models with the state-of-the-art protein design models (RFdiffusion, RF Inpainting, and RF Halluciantion) and demonstrated that our methods can generate more designable structures with more diversity and higher computation efficiency. We also explored the possibility of the combination of hallucination and inpainting which might further improve the model efficiency and diversity. The source code of GPDL is available athttps://github.com/sirius777coder/GPDL.

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“…Machine learning (ML) has been introduced to predict unknown events by learning a dataset [17]. This approach has been widely applied in drug development [18,19], protein structure and function prediction [20,21], and epidemic surveillance [22,23] and has exhibited better outcomes than DOE or RSM [24]. Lately, combining active learning with ML has successfully optimized the culture media for mammalian cells [25,26].…”

Section: Introductionmentioning

confidence: 99%

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

Zhang,

Aida,

Ying

2023

Applied Microbiology

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Medium optimization and development for selective bacterial cultures are essential for isolating and functionalizing individual bacteria in microbial communities; nevertheless, it remains challenging due to the unknown mechanisms between bacterial growth and medium components. The present study first tried combining machine learning (ML) with active learning to fine-tune the medium components for the selective culture of two divergent bacteria, i.e., Lactobacillus plantarum and Escherichia coli. ML models considering multiple growth parameters of the two bacterial strains were constructed to predict the fine-tuned medium combinations for higher specificity of bacterial growth. The growth parameters were designed as the exponential growth rate (r) and maximal growth yield (K), which were calculated according to the growth curves. The eleven chemical components in the commercially available medium MRS were subjected to medium optimization and specialization. High-throughput growth assays of both strains grown separately were performed to obtain thousands of growth curves in more than one hundred medium combinations, and the resultant datasets linking the growth parameters to the medium combinations were used for the ML training. Repeated rounds of active learning (i.e., ML model construction, medium prediction, and experimental verification) successfully improved the specific growth of a single strain out of the two. Both r and K showed maximized differentiation between the two strains. A further analysis of all the data accumulated in active learning identified the decision-making medium components for growth specificity and the differentiated, determinative manner of growth decisions of the two strains. In summary, this study demonstrated the efficiency and practicality of active learning in medium optimization for selective cultures and offered novel insights into the contribution of the chemical components to specific bacterial growth.

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Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Cited by 18 publications

References 65 publications

Assessing the functional roles of coevolving PHD finger residues

Assessing the functional roles of coevolving PHD finger residues

Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method

Employing Active Learning in Medium Optimization for Selective Bacterial Growth

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