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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
- Doo Nam Kim
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Andrew D McNaughton
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
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McNaughton AD, Joshi RP, Knutson CR, Fnu A, Luebke KJ, Malerich JP, Madrid PB, Kumar N. Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties. J Chem Inf Model 2023; 63:1462-1471. [PMID: 36847578 DOI: 10.1021/acs.jcim.2c01662] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.
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Affiliation(s)
- Andrew D McNaughton
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Rajendra P Joshi
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Carter R Knutson
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Anubhav Fnu
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kevin J Luebke
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Jeremiah P Malerich
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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McNaughton AD, Bredeweg EL, Manzer J, Zucker J, Munoz Munoz N, Burnet MC, Nakayasu ES, Pomraning KR, Merkley ED, Dai Z, Chrisler WB, Baker SE, St. John PC, Kumar N. Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling. ACS Synth Biol 2021; 10:2968-2981. [PMID: 34636549 DOI: 10.1021/acssynbio.1c00267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.
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Affiliation(s)
- Andrew D. McNaughton
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Erin L. Bredeweg
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - James Manzer
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jeremy Zucker
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Nathalie Munoz Munoz
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Meagan C. Burnet
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ernesto S. Nakayasu
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kyle R. Pomraning
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Eric D. Merkley
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ziyu Dai
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - William B. Chrisler
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Scott E. Baker
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Peter C. St. John
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States
| | - Neeraj Kumar
- Earth and Biological Science Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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