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Xiong D, U K, Sun J, Cribbs AP. PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction. Interdiscip Sci 2024:10.1007/s12539-024-00639-6. [PMID: 39155325 DOI: 10.1007/s12539-024-00639-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 08/20/2024]
Abstract
X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, 14853, USA.
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, 14853, USA.
| | - Kaicheng U
- Department of Computational Biology, Cornell University, Ithaca, 14853, USA
| | - Jianfeng Sun
- Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK.
| | - Adam P Cribbs
- Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
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Matinyan S, Filipcik P, Abrahams JP. Deep learning applications in protein crystallography. Acta Crystallogr A Found Adv 2024; 80:1-17. [PMID: 38189437 PMCID: PMC10833361 DOI: 10.1107/s2053273323009300] [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: 07/29/2023] [Accepted: 10/24/2023] [Indexed: 01/09/2024] Open
Abstract
Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field.
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Affiliation(s)
| | | | - Jan Pieter Abrahams
- Biozentrum, Basel University, Basel, Switzerland
- Paul Scherrer Institute, Villigen, Switzerland
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Le NQK, Li W, Cao Y. Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. Brief Bioinform 2023; 24:bbad319. [PMID: 37649385 DOI: 10.1093/bib/bbad319] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/09/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially determined and screened by modeling. As a result, this study created a new pipeline aimed at using protein sequence to predict protein crystallization propensity in the protein material production stage, purification stage and production of crystal stage. The newly created pipeline proposed a new feature selection method, which involves combining Chi-square (${\chi }^{2}$) and recursive feature elimination together with the 12 selected features, followed by a linear discriminant analysisfor dimensionality reduction and finally, a support vector machine algorithm with hyperparameter tuning and 10-fold cross-validation is used to train the model and test the results. This new pipeline has been tested on three different datasets, and the accuracy rates are higher than the existing pipelines. In conclusion, our model provides a new solution to predict multistage protein crystallization propensity which is a big challenge in computational biology.
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Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing Street, 110, Taipei, Taiwan
| | - Wanru Li
- NUS-ISS, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Yanshuang Cao
- NUS-ISS, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
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Zhao H, Ni P, Zhao Q, Liang X, Ai D, Erhardt S, Wang J, Li Y, Wang J. Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework. Commun Biol 2023; 6:870. [PMID: 37620651 PMCID: PMC10449791 DOI: 10.1038/s42003-023-05243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the seriousness of clinical outcomes of adverse reactions to drugs. GCAP has two tasks: one is to predict whether adverse reactions to drugs cause serious clinical outcomes, and the other is to infer the corresponding classes of serious clinical outcomes. Experimental results demonstrate that our method is a powerful and robust framework with high extendibility. GCAP can serve as a useful tool to successfully address the challenge of predicting the seriousness of clinical outcomes stemming from adverse reactions to drugs.
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Affiliation(s)
- Haochen Zhao
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
- Xiangjiang Laboratory, Changsha, 410205, China
| | - Peng Ni
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Qichang Zhao
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Xiao Liang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Di Ai
- Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Shannon Erhardt
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Jun Wang
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA, 23529-0001, USA
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
- Xiangjiang Laboratory, Changsha, 410205, China.
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