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Xie J, Chen Y, Luo S, Yang W, Lin Y, Wang L, Ding X, Tong M, Yu R. Tracing unknown tumor origins with a biological-pathway-based transformer model. CELL REPORTS METHODS 2024; 4:100797. [PMID: 38889685 PMCID: PMC11228371 DOI: 10.1016/j.crmeth.2024.100797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.
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Affiliation(s)
- Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Ying Chen
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
| | - Shijie Luo
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Wenxian Yang
- Aginome Scientific, Xiamen, Fujian 361005, China
| | - Yuxiang Lin
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Liansheng Wang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
| | - Xin Ding
- Department of Pathology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361004, China.
| | - Mengsha Tong
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.
| | - Rongshan Yu
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China; Aginome Scientific, Xiamen, Fujian 361005, China.
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2
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Janson G, Feig M. Transferable deep generative modeling of intrinsically disordered protein conformations. PLoS Comput Biol 2024; 20:e1012144. [PMID: 38781245 PMCID: PMC11152266 DOI: 10.1371/journal.pcbi.1012144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/05/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.
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Affiliation(s)
- Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
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Roche R, Moussad B, Shuvo MH, Tarafder S, Bhattacharya D. EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. Nucleic Acids Res 2024; 52:e27. [PMID: 38281252 PMCID: PMC10954458 DOI: 10.1093/nar/gkae039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here, we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
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Janson G, Feig M. Transferable deep generative modeling of intrinsically disordered protein conformations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579522. [PMID: 38370653 PMCID: PMC10871340 DOI: 10.1101/2024.02.08.579522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.
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Affiliation(s)
- Giacomo Janson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA
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Roche R, Moussad B, Shuvo MH, Tarafder S, Bhattacharya D. EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557719. [PMID: 37745556 PMCID: PMC10515942 DOI: 10.1101/2023.09.14.557719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
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