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Gu X, Myung Y, Rodrigues CHM, Ascher DB. EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models. Protein Sci 2024; 33:e5096. [PMID: 38979954 PMCID: PMC11232051 DOI: 10.1002/pro.5096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/06/2024] [Accepted: 06/15/2024] [Indexed: 07/10/2024]
Abstract
Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.
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Affiliation(s)
- Xiaotong Gu
- The Australian Centre for Ecogenomics, School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Yoochan Myung
- The Australian Centre for Ecogenomics, School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Carlos H. M. Rodrigues
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - David B. Ascher
- The Australian Centre for Ecogenomics, School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
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Diaz DJ, Gong C, Ouyang-Zhang J, Loy JM, Wells J, Yang D, Ellington AD, Dimakis AG, Klivans AR. Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations. Nat Commun 2024; 15:6170. [PMID: 39043654 PMCID: PMC11266546 DOI: 10.1038/s41467-024-49780-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 06/14/2024] [Indexed: 07/25/2024] Open
Abstract
Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.
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Affiliation(s)
- Daniel J Diaz
- UT Austin, Department of Computer Science, Austin, TX, 78712, USA.
- Intelligent Proteins, LLC, Austin, TX, 78712, USA.
- UT Austin, Department of Chemistry, Austin, TX, 78712, USA.
| | - Chengyue Gong
- UT Austin, Department of Computer Science, Austin, TX, 78712, USA
| | | | - James M Loy
- Intelligent Proteins, LLC, Austin, TX, 78712, USA
- UT Austin, Department of Molecular Biosciences, Austin, TX, 78712, USA
| | - Jordan Wells
- UT Austin, McKetta Department of Chemical Engineering, Austin, TX, 78712, USA
| | - David Yang
- UT Austin, Department of Molecular Biosciences, Austin, TX, 78712, USA
| | | | - Alexandros G Dimakis
- UT Austin, Chandra Family Department of Electrical and Computer Engineering, Austin, TX, 78712, USA
| | - Adam R Klivans
- UT Austin, Department of Computer Science, Austin, TX, 78712, USA
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3
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Chu SKS, Narang K, Siegel JB. Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset. PLoS Comput Biol 2024; 20:e1012248. [PMID: 39038042 DOI: 10.1371/journal.pcbi.1012248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 06/13/2024] [Indexed: 07/24/2024] Open
Abstract
Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our model's limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.
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Affiliation(s)
- Simon K S Chu
- Biophysics Graduate Program, University of California Davis, Davis, California, United States of America
| | - Kush Narang
- College of Biological Sciences, University of California Davis, Davis, California, United States of America
| | - Justin B Siegel
- Genome Center, University of California Davis, Davis, California, United States of America
- Department of Chemistry, University of California Davis, Davis, California, United States of America
- Department of Biochemistry and Molecular Medicine, University of California Davis, Davis, California, United States of America
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Wang JM, Cui RK, Qian ZK, Yang ZZ, Li Y. Mining channel-regulated peptides from animal venom by integrating sequence semantics and structural information. Comput Biol Chem 2024; 109:108027. [PMID: 38340414 DOI: 10.1016/j.compbiolchem.2024.108027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/24/2024] [Accepted: 02/04/2024] [Indexed: 02/12/2024]
Abstract
Channel-regulated peptides (CRPs) derived from animal venom hold great promise as potential drug candidates for numerous diseases associated with channel proteins. However, discovering and identifying CRPs using traditional bio-experimental methods is a time-consuming and laborious process. While there were a few computational studies on CRPs, they were limited to specific channel proteins, relied heavily on complex feature engineering, and lacked the incorporation of multi-source information. To address these problems, we proposed a novel deep learning model, called DeepCRPs, based on graph neural networks for systematically mining CRPs from animal venom. By combining the sequence semantic and structural information, the classification performance of four CRPs was significantly enhanced, reaching an accuracy of 0.92. This performance surpassed baseline models with accuracies ranging from 0.77 to 0.89. Furthermore, we employed advanced interpretable techniques to explore sequence and structural determinants relevant to the classification of CRPs, yielding potentially valuable bio-function interpretations. Comprehensive experimental results demonstrated the precision and interpretive capability of DeepCRPs, making it an accurate and bio-explainable suit for the identification and categorization of CRPs. Our research will contribute to the discovery and development of toxin peptides targeting channel proteins. The source data and code are freely available at https://github.com/liyigerry/DeepCRPs.
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Affiliation(s)
- Jian-Ming Wang
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Rong-Kai Cui
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Zheng-Kun Qian
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Zi-Zhong Yang
- Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R&D, College of Pharmacy, Dali University, Dali, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, China.
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Wang T, Jin X, Lu X, Min X, Ge S, Li S. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness. Front Genet 2024; 14:1347667. [PMID: 38274106 PMCID: PMC10808456 DOI: 10.3389/fgene.2023.1347667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.
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Affiliation(s)
- Tianshu Wang
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Xiaocheng Jin
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoli Lu
- Information and Networking Center, Xiamen University, Xiamen, China
| | - Xiaoping Min
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Shengxiang Ge
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Shaowei Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
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Ju H, Kim K, Kim BI, Woo SK. Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics. Int J Mol Sci 2024; 25:698. [PMID: 38255770 PMCID: PMC10815846 DOI: 10.3390/ijms25020698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.
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Affiliation(s)
- Hyemin Ju
- Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea;
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Kangsan Kim
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Byung Il Kim
- Department of Nuclear Medicine, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
| | - Sang-Keun Woo
- Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Republic of Korea;
- Division of RI-Convergence Research, Korea Institute of Radiological and Medical Sciences, Seoul 07812, Republic of Korea;
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Umerenkov D, Nikolaev F, Shashkova TI, Strashnov PV, Sindeeva M, Shevtsov A, Ivanisenko NV, Kardymon OL. PROSTATA: a framework for protein stability assessment using transformers. Bioinformatics 2023; 39:btad671. [PMID: 37935419 PMCID: PMC10651431 DOI: 10.1093/bioinformatics/btad671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/09/2023] Open
Abstract
MOTIVATION Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning. RESULTS In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment. AVAILABILITY AND IMPLEMENTATION PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.
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Affiliation(s)
| | | | | | - Pavel V Strashnov
- Bioinformatics Group, AIRI, Moscow 121170, Russia
- Department of Computer Design and Technology, Bauman Moscow State Technical University, Moscow 105005, Russia
| | | | - Andrey Shevtsov
- Bioinformatics Group, AIRI, Moscow 121170, Russia
- Regulatory Transcriptomics and Epigenomics Group, Institute of Bioengineering, Research Center of Biotechnology RAS, Moscow 117036, Russia
| | - Nikita V Ivanisenko
- Bioinformatics Group, AIRI, Moscow 121170, Russia
- Laboratory of Computational Proteomics, Institute of Cytology and Genetics SB RAS, Novosibirsk 630090, Russia
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