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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Jahn LR, Marquet C, Heinzinger M, Rost B. Protein embeddings predict binding residues in disordered regions. Sci Rep 2024; 14:13566. [PMID: 38866950 PMCID: PMC11169622 DOI: 10.1038/s41598-024-64211-4] [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: 03/06/2024] [Accepted: 06/06/2024] [Indexed: 06/14/2024] Open
Abstract
The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding residues often err for intrinsically disordered proteins or regions (IDPs/IDPRs), often also referred to as molecular recognition features (MoRFs). Here, we presented a novel machine learning (ML) model trained to specifically predict binding regions in IDPRs. The proposed model, IDBindT5, leveraged embeddings from the protein language model (pLM) ProtT5 to reach a balanced accuracy of 57.2 ± 3.6% (95% confidence interval). Assessed on the same data set, this did not differ at the 95% CI from the state-of-the-art (SOTA) methods ANCHOR2 and DeepDISOBind that rely on expert-crafted features and evolutionary information from multiple sequence alignments (MSAs). Assessed on other data, methods such as SPOT-MoRF reached higher MCCs. IDBindT5's SOTA predictions are much faster than other methods, easily enabling full-proteome analyses. Our findings emphasize the potential of pLMs as a promising approach for exploring and predicting features of disordered proteins. The model and a comprehensive manual are publicly available at https://github.com/jahnl/binding_in_disorder .
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Affiliation(s)
- Laura R Jahn
- School of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics and Computational Biology, TUM (Technical University of Munich), 85748, Garching/Munich, Germany
| | - Céline Marquet
- School of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics and Computational Biology, TUM (Technical University of Munich), 85748, Garching/Munich, Germany.
| | - Michael Heinzinger
- School of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics and Computational Biology, TUM (Technical University of Munich), 85748, Garching/Munich, Germany
| | - Burkhard Rost
- School of Computation, Information, and Technology (CIT), Department of Informatics, Bioinformatics and Computational Biology, TUM (Technical University of Munich), 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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3
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Peñaherrera D, Koes DR. Structure-Infused Protein Language Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.13.571525. [PMID: 38712044 PMCID: PMC11071282 DOI: 10.1101/2023.12.13.571525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Embeddings from protein language models (PLM's) capture intricate patterns for protein sequences, enabling more accurate and efficient prediction of protein properties. Incorporating protein structure information as direct input into PLMs results in an improvement on the predictive ability of protein embeddings on downstream tasks. In this work we demonstrate that indirectly infusing structure information into PLMs also leads to performance gains on structure related tasks. The key difference between this framework and others is that at inference time the model does not require access to structure to produce its embeddings.
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4
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Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane CM. Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. J Cheminform 2024; 16:32. [PMID: 38486231 PMCID: PMC10941399 DOI: 10.1186/s13321-024-00821-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/17/2024] Open
Abstract
Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.
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Affiliation(s)
- Anna Carbery
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
| | - Martin Buttenschoen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Rachael Skyner
- OMass Therapeutics, Building 4000, Chancellor Court, John Smith Drive, ARC Oxford, OX4 2GX, UK
| | - Frank von Delft
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Centre for Medicines Discovery, University of Oxford, Oxford, OX3 7DQ, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, United Kingdom
- Department of Biochemistry, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
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5
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Hao X, Fan L. ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs. Eur J Pharm Sci 2024; 194:106705. [PMID: 38246432 DOI: 10.1016/j.ejps.2024.106705] [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: 11/09/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024]
Abstract
Viscosity is a key characteristic of therapeutic antibodies for subcutaneous administration which requires low volume and high concentration formulations. It would be highly beneficial to accurately predict the viscosity of newly developed therapeutic antibodies in the early stages of development. In this work, a ProtT5-XL-UniRef50 (ProtT5) and Random Forests (RF)-based prediction method was proposed for accurately predicting the viscosity of monoclonal antibodies, with only corresponding sequences needed. Starting from the given heavy and light chain V-region sequences, corresponding features were first extracted from the ProtT5 pretrained model. Kernel principal analysis (Kernel-PCA) was then used for reducing the extracted 2048-D (1024-D for each sequence) feature vector to a reasonable level for efficient training of the RF-regressor. Then, the RF model was constructed on 40 commercially available therapeutic antibodies and tested with 3-folds cross-validation. Test results show that the model could reproduce the viscosity value at a high level (Pearson correlation coefficient (PCC) = 0.928). Performance on classifying high (>30 cP) and low (<30 cP) viscosity is much more satisfactory, the Accuracy (ACC) and the area under precision-recall curve (AUC) of the classification model from validation tests are 0.975 and 1.000, respectively. Compared to 5 existing state-of-the-art viscosity prediction methods, the proposed method performs best which would facilitate high concentration antibody viscosity high-throughput screening.
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Affiliation(s)
- Xiaohu Hao
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China
| | - Long Fan
- Production and R&D Center I of LSS (Life Science Service), GenScript Biotech Corporation, No. 28, Yongxi Rd., Nanjing, 211110, Jiangsu, China; Production and R&D Center I of LSS (Life Science Service), GenScript (Shanghai) Biotech Corporation, No. 186, Hedan Rd., Shanghai, 200100, China.
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6
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Palacios A, Acharya P, Peidl A, Beck M, Blanco E, Mishra A, Bawa-Khalfe T, Pakhrin S. SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model. NAR Genom Bioinform 2024; 6:lqae011. [PMID: 38327870 PMCID: PMC10849187 DOI: 10.1093/nargab/lqae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/17/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64%, 73.36%, 0.48% and 74.00%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.
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Affiliation(s)
- Andrew Vargas Palacios
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Pujan Acharya
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
| | - Anthony Stephen Peidl
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Moriah Rene Beck
- Department of Chemistry and Biochemistry, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA
| | - Eduardo Blanco
- Department of Computer Science, University of Arizona, 1040 4th St., Tucson, AZ 85721, USA
| | - Avdesh Mishra
- Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
| | - Tasneem Bawa-Khalfe
- Department of Biology and Biochemistry, Center for Nuclear Receptors & Cell Signaling, University of Houston, Houston, TX 77204, USA
| | - Subash Chandra Pakhrin
- Department of Computer Science and Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, TX 77002, USA
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7
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Yan Z, Ge F, Liu Y, Zhang Y, Li F, Song J, Yu DJ. TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion. J Chem Inf Model 2024; 64:1407-1418. [PMID: 38334115 DOI: 10.1021/acs.jcim.3c02019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.
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Affiliation(s)
- Zihao Yan
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and lnformation Displays & lnstitute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, PR China
| | - Yan Liu
- Department of Computer Science, Yangzhou University, Yangzhou 225100, PR China
| | - Yumeng Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria 3000, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
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8
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Zhu YH, Liu Z, Liu Y, Ji Z, Yu DJ. ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction. Brief Bioinform 2024; 25:bbae040. [PMID: 38349057 PMCID: PMC10939370 DOI: 10.1093/bib/bbae040] [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: 09/10/2023] [Revised: 01/02/2024] [Accepted: 01/22/2024] [Indexed: 02/15/2024] Open
Abstract
Efficient and accurate recognition of protein-DNA interactions is vital for understanding the molecular mechanisms of related biological processes and further guiding drug discovery. Although the current experimental protocols are the most precise way to determine protein-DNA binding sites, they tend to be labor-intensive and time-consuming. There is an immediate need to design efficient computational approaches for predicting DNA-binding sites. Here, we proposed ULDNA, a new deep-learning model, to deduce DNA-binding sites from protein sequences. This model leverages an LSTM-attention architecture, embedded with three unsupervised language models that are pre-trained on large-scale sequences from multiple database sources. To prove its effectiveness, ULDNA was tested on 229 protein chains with experimental annotation of DNA-binding sites. Results from computational experiments revealed that ULDNA significantly improves the accuracy of DNA-binding site prediction in comparison with 17 state-of-the-art methods. In-depth data analyses showed that the major strength of ULDNA stems from employing three transformer language models. Specifically, these language models capture complementary feature embeddings with evolution diversity, in which the complex DNA-binding patterns are buried. Meanwhile, the specially crafted LSTM-attention network effectively decodes evolution diversity-based embeddings as DNA-binding results at the residue level. Our findings demonstrated a new pipeline for predicting DNA-binding sites on a large scale with high accuracy from protein sequence alone.
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Affiliation(s)
- Yi-Heng Zhu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
| | - Zi Liu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Yan Liu
- School of Information Engineering, Yangzhou University, Yangzhou 225000, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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9
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Shenoy A, Kalakoti Y, Sundar D, Elofsson A. M-Ionic: prediction of metal-ion-binding sites from sequence using residue embeddings. Bioinformatics 2024; 40:btad782. [PMID: 38175787 PMCID: PMC10792727 DOI: 10.1093/bioinformatics/btad782] [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: 04/04/2023] [Revised: 12/20/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION Understanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding. RESULTS On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e. Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. AVAILABILITY AND IMPLEMENTATION M-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). The GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data.
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Affiliation(s)
- Aditi Shenoy
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna 17121, Sweden
| | - Yogesh Kalakoti
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
| | - Durai Sundar
- Department of Biochemical Engineering & Biotechnology, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
- Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi 110016, India
| | - Arne Elofsson
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna 17121, Sweden
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Haselbeck F, John M, Zhang Y, Pirnay J, Fuenzalida-Werner J, Costa R, Grimm D. Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom Bioinform 2023; 5:lqad087. [PMID: 37829176 PMCID: PMC10566323 DOI: 10.1093/nargab/lqad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/14/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Protein thermostability is important in many areas of biotechnology, including enzyme engineering and protein-hybrid optoelectronics. Ever-growing protein databases and information on stability at different temperatures allow the training of machine learning models to predict whether proteins are thermophilic. In silico predictions could reduce costs and accelerate the development process by guiding researchers to more promising candidates. Existing models for predicting protein thermophilicity rely mainly on features derived from physicochemical properties. Recently, modern protein language models that directly use sequence information have demonstrated superior performance in several tasks. In this study, we evaluate the usefulness of protein language model embeddings for thermophilicity prediction with ProLaTherm, a Protein Language model-based Thermophilicity predictor. ProLaTherm significantly outperforms all feature-, sequence- and literature-based comparison partners on multiple evaluation metrics. In terms of the Matthew's correlation coefficient, ProLaTherm outperforms the second-best competitor by 18.1% in a nested cross-validation setup. Using proteins from species not overlapping with species from the training data, ProLaTherm outperforms all competitors by at least 9.7%. On these data, it misclassified only one nonthermophilic protein as thermophilic. Furthermore, it correctly identified 97.4% of all thermophilic proteins in our test set with an optimal growth temperature above 70°C.
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Affiliation(s)
- Florian Haselbeck
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Maura John
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Yuqi Zhang
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
| | - Jonathan Pirnay
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
| | - Juan Pablo Fuenzalida-Werner
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany
| | - Rubén D Costa
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Chair of Biogenic Functional Materials, 94315 Straubing, Germany
| | - Dominik G Grimm
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, 94315 Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, 94315 Straubing, Germany
- Technical University of Munich, TUM School of Computation, Information and Technology (CIT), 85748 Garching, Germany
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11
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Poretsky E, Andorf CM, Sen TZ. PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models. PLANT DIRECT 2023; 7:e554. [PMID: 38124705 PMCID: PMC10732782 DOI: 10.1002/pld3.554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein-protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the performance of PhosBoost to existing protein phosphorylation prediction methods, PhosphoLingo and DeepPhos. For serine and threonine prediction, PhosBoost achieved higher recall than PhosphoLingo and DeepPhos (.78, .56, and .14, respectively) while maintaining a competitive area under the precision-recall curve (.54, .56, and .42, respectively). PhosphoLingo and DeepPhos failed to predict any tyrosine phosphorylation sites, while PhosBoost achieved a recall score of .6. Despite the precision-recall tradeoff, PhosBoost offers improved performance when recall is prioritized while consistently providing more confident probability scores. A sequence-based pairwise alignment step improved prediction results for all classifiers by effectively increasing the number of inferred positive phosphosites. We provide evidence to show that PhosBoost models are transferable across species and scalable for genome-wide protein phosphorylation predictions. PhosBoost is freely and publicly available on GitHub.
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Affiliation(s)
- Elly Poretsky
- Agricultural Research Service, Crop Improvement and Genetics Research UnitU.S. Department of AgricultureAlbanyCAUnited States
| | - Carson M. Andorf
- Agricultural Research Service, Corn Insects and Crop Genetics ResearchU.S. Department of AgricultureAmesIAUnited States
- Department of Computer ScienceIowa State UniversityAmesIAUnited States
| | - Taner Z. Sen
- Agricultural Research Service, Crop Improvement and Genetics Research UnitU.S. Department of AgricultureAlbanyCAUnited States
- Department of BioengineeringUniversity of CaliforniaBerkeleyCAUnited States
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12
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Avraham O, Tsaban T, Ben-Aharon Z, Tsaban L, Schueler-Furman O. Protein language models can capture protein quaternary state. BMC Bioinformatics 2023; 24:433. [PMID: 37964216 PMCID: PMC10647083 DOI: 10.1186/s12859-023-05549-w] [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: 03/31/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Determining a protein's quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models, such as ESM-2, that apply computational natural-language models to proteins successfully capture secondary structure, protein cell localization and other characteristics, from a single sequence. Here we hypothesize that information about the protein quaternary state may be contained within protein sequences as well, allowing us to benefit from these novel approaches in the context of quaternary state prediction. RESULTS We generated ESM-2 embeddings for a large dataset of proteins with quaternary state labels from the curated QSbio dataset. We trained a model for quaternary state classification and assessed it on a non-overlapping set of distinct folds (ECOD family level). Our model, named QUEEN (QUaternary state prediction using dEEp learNing), performs worse than approaches that include information from solved crystal structures. However, it successfully learned to distinguish multimers from monomers, and predicts the specific quaternary state with moderate success, better than simple sequence similarity-based annotation transfer. Our results demonstrate that complex, quaternary state related information is included in such embeddings. CONCLUSIONS QUEEN is the first to investigate the power of embeddings for the prediction of the quaternary state of proteins. As such, it lays out strengths as well as limitations of a sequence-based protein language model approach, compared to structure-based approaches. Since it does not require any structural information and is fast, we anticipate that it will be of wide use both for in-depth investigation of specific systems, as well as for studies of large sets of protein sequences. A simple colab implementation is available at: https://colab. RESEARCH google.com/github/Furman-Lab/QUEEN/blob/main/QUEEN_prediction_notebook.ipynb .
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Affiliation(s)
- Orly Avraham
- Department of Microbiology and Molecular Genetics, Faculty of Medicine, Institute for Biomedical Research Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tomer Tsaban
- Department of Microbiology and Molecular Genetics, Faculty of Medicine, Institute for Biomedical Research Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Faculty of Medicine, Institute for Biomedical Research Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Linoy Tsaban
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
- The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Faculty of Medicine, Institute for Biomedical Research Israel-Canada, The Hebrew University of Jerusalem, Jerusalem, Israel.
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13
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Lee M, Min K. AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint. Biochemistry 2023; 62:2700-2709. [PMID: 37622182 DOI: 10.1021/acs.biochem.3c00253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing-based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree-based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature-based methods, (4) feature importance analysis, and (5) protein space analysis. Consequently, the significantly improved model performance and data-set-independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands.
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Affiliation(s)
- Myeonghun Lee
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Kyoungmin Min
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
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14
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Magyar ZÉ, Bauer J, Bauerová-Hlinková V, Jóna I, Gaburjakova J, Gaburjakova M, Almássy J. Eu 3+ detects two functionally distinct luminal Ca 2+ binding sites in ryanodine receptors. Biophys J 2023; 122:3516-3531. [PMID: 37533257 PMCID: PMC10502479 DOI: 10.1016/j.bpj.2023.07.029] [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: 02/23/2023] [Revised: 06/26/2023] [Accepted: 07/31/2023] [Indexed: 08/04/2023] Open
Abstract
Ryanodine receptors (RyRs) are Ca2+ release channels, gated by Ca2+ in the cytosol and the sarcoplasmic reticulum lumen. Their regulation is impaired in certain cardiac and muscle diseases. Although a lot of data is available on the luminal Ca2+ regulation of RyR, its interpretation is complicated by the possibility that the divalent ions used to probe the luminal binding sites may contaminate the cytoplasmic sites by crossing the channel pore. In this study, we used Eu3+, an impermeable agonist of Ca2+ binding sites, as a probe to avoid this complication and to gain more specific information about the function of the luminal Ca2+ sensor. Single-channel currents were measured from skeletal muscle and cardiac RyRs (RyR1 and RyR2) using the lipid bilayer technique. We show that RyR2 is activated by the luminal addition of Ca2+, whereas RyR1 is inhibited. These results were qualitatively reproducible using Eu3+. The luminal regulation of RyR1 carrying a mutation associated with malignant hyperthermia was not different from that of the wild-type. RyR1 inhibition by Eu3+ was extremely voltage dependent, whereas RyR2 activation did not depend on the membrane potential. These results suggest that the RyR1 inhibition site is in the membrane's electric field (channel pore), whereas the RyR2 activation site is outside. Using in silico analysis and previous results, we predicted putative Ca2+ binding site sequences. We propose that RyR2 bears an activation site, which is missing in RyR1, but both isoforms share the same inhibitory Ca2+ binding site near the channel gate.
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Affiliation(s)
- Zsuzsanna É Magyar
- Department of Physiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Jacob Bauer
- Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
| | | | - István Jóna
- Department of Physiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Jana Gaburjakova
- Institute of Molecular Physiology and Genetics, Centre of Biosciences, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Marta Gaburjakova
- Institute of Molecular Physiology and Genetics, Centre of Biosciences, Slovak Academy of Sciences, Bratislava, Slovakia
| | - János Almássy
- Department of Physiology, Semmelweis University, Budapest, Hungary.
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15
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Koludarov I, Senoner T, Jackson TNW, Dashevsky D, Heinzinger M, Aird SD, Rost B. Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily. Nat Commun 2023; 14:4861. [PMID: 37567881 PMCID: PMC10421932 DOI: 10.1038/s41467-023-40550-0] [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: 12/15/2022] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Three-finger toxins (3FTXs) are a functionally diverse family of toxins, apparently unique to venoms of caenophidian snakes. Although the ancestral function of 3FTXs is antagonism of nicotinic acetylcholine receptors, redundancy conferred by the accumulation of duplicate genes has facilitated extensive neofunctionalization, such that derived members of the family interact with a range of targets. 3FTXs are members of the LY6/UPAR family, but their non-toxin ancestor remains unknown. Combining traditional phylogenetic approaches, manual synteny analysis, and machine learning techniques (including AlphaFold2 and ProtT5), we have reconstructed a detailed evolutionary history of 3FTXs. We identify their immediate ancestor as a non-secretory LY6, unique to squamate reptiles, and propose that changes in molecular ecology resulting from loss of a membrane-anchoring domain and changes in gene expression, paved the way for the evolution of one of the most important families of snake toxins.
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Affiliation(s)
- Ivan Koludarov
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany.
| | - Tobias Senoner
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Timothy N W Jackson
- Australian Venom Research Unit, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel Dashevsky
- Australian National Insect Collection, Commonwealth Scientific & Industrial Research Organisation, Canberra, ACT, Australia
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
| | - Steven D Aird
- 7744-23 Hotaka Ariake, 399-8301, Azumino-shi, Nagano-ken, Japan
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Boltzmannstr. 3, 85748, Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), Alte Akademie 8, Freising, Germany
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16
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Pakhrin SC, Pokharel S, Pratyush P, Chaudhari M, Ismail HD, Kc DB. LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model. J Proteome Res 2023; 22:2548-2557. [PMID: 37459437 DOI: 10.1021/acs.jproteome.2c00667] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.
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Affiliation(s)
- Subash C Pakhrin
- School of Computing, Wichita State University, 1845 Fairmount St., Wichita, Kansas 67260, United States
- Department of Computer Science & Engineering Technology, University of Houston-Downtown, 1 Main St., Houston, Texas 77002, United States
| | - Suresh Pokharel
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Pawel Pratyush
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Meenal Chaudhari
- Department of Biology, North Carolina A&T State University, Greensboro, North Carolina 27411, United States
| | - Hamid D Ismail
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
| | - Dukka B Kc
- Department of Computer Science, Michigan Technological University, Houghton, Michigan 49931, United States
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17
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Gonzales MEM, Ureta JC, Shrestha AMS. Protein embeddings improve phage-host interaction prediction. PLoS One 2023; 18:e0289030. [PMID: 37486915 PMCID: PMC10365317 DOI: 10.1371/journal.pone.0289030] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023] Open
Abstract
With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.
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Affiliation(s)
- Mark Edward M Gonzales
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
| | - Jennifer C Ureta
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
| | - Anish M S Shrestha
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Systems and Computational Biology Research Unit, Center for Natural Sciences and Environmental Research, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
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18
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Choi SR, Lee M. Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. BIOLOGY 2023; 12:1033. [PMID: 37508462 PMCID: PMC10376273 DOI: 10.3390/biology12071033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
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Affiliation(s)
- Sanghyuk Roy Choi
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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19
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McWhite CD, Armour-Garb I, Singh M. Leveraging protein language models for accurate multiple sequence alignments. Genome Res 2023; 33:1145-1153. [PMID: 37414576 PMCID: PMC10538487 DOI: 10.1101/gr.277675.123] [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: 01/06/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Multiple sequence alignment (MSA) is a critical step in the study of protein sequence and function. Typically, MSA algorithms progressively align pairs of sequences and combine these alignments with the aid of a guide tree. These alignment algorithms use scoring systems based on substitution matrices to measure amino acid similarities. Although successful, standard methods struggle on sets of proteins with low sequence identity: the so-called twilight zone of protein alignment. For these difficult cases, another source of information is needed. Protein language models are a powerful new approach that leverages massive sequence data sets to produce high-dimensional contextual embeddings for each amino acid in a sequence. These embeddings have been shown to reflect physicochemical and higher-order structural and functional attributes of amino acids within proteins. Here, we present a novel approach to MSA, based on clustering and ordering amino acid contextual embeddings. Our method for aligning semantically consistent groups of proteins circumvents the need for many standard components of MSA algorithms, avoiding initial guide tree construction, intermediate pairwise alignments, gap penalties, and substitution matrices. The added information from contextual embeddings leads to higher accuracy alignments for structurally similar proteins with low amino-acid similarity. We anticipate that protein language models will become a fundamental component of the next generation of algorithms for generating MSAs.
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Affiliation(s)
- Claire D McWhite
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
| | - Isabel Armour-Garb
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, USA
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20
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Yu Z, Yang Z, Lan Q, Wang Y, Huang F, Cai Y. Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083774 DOI: 10.1109/embc40787.2023.10341090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Learning low-dimensional continuous vector representation for short k-mers divided from long DNA sequences is key to DNA sequence modeling that can be utilized in many bioinformatics investigations, such as DNA sequence retrieval and classification. DNA2Vec is the most widely used method for DNA sequence embedding. However, it poorly scales to large data sets due to its extremely long training time in kmer embedding. In this paper, we propose a novel efficient graph-based kmer embedding method, named Kmer-Node2Vec, to tackle this concern. Our method converts the large DNA corpus into one kmer co-occurrence graph, and extracts kmer relation on the graph by random walks to learn fast and high-quality kmer embedding. Extensive experiments show that our method is faster than DNA2Vec by 29 times for training on a 4GB data set, and on par with DNA2Vec in terms of task-specific accuracy of sequence retrieval and classification.
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21
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Singh R, Sledzieski S, Bryson B, Cowen L, Berger B. Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proc Natl Acad Sci U S A 2023; 120:e2220778120. [PMID: 37289807 PMCID: PMC10268324 DOI: 10.1073/pnas.2220778120] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/10/2023] [Indexed: 06/10/2023] Open
Abstract
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Samuel Sledzieski
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Bryan Bryson
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA02139
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA02155
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA02139
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA02139
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22
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Varadi M, Bordin N, Orengo C, Velankar S. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr Opin Struct Biol 2023; 79:102543. [PMID: 36807079 DOI: 10.1016/j.sbi.2023.102543] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/21/2023]
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
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Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK. https://twitter.com/nicolabordin
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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23
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Bordin N, Dallago C, Heinzinger M, Kim S, Littmann M, Rauer C, Steinegger M, Rost B, Orengo C. Novel machine learning approaches revolutionize protein knowledge. Trends Biochem Sci 2023; 48:345-359. [PMID: 36504138 PMCID: PMC10570143 DOI: 10.1016/j.tibs.2022.11.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/24/2022] [Accepted: 11/17/2022] [Indexed: 12/10/2022]
Abstract
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.
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Affiliation(s)
- Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Christian Dallago
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; VantAI, 151 W 42nd Street, New York, NY 10036, USA
| | - Michael Heinzinger
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Stephanie Kim
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Maria Littmann
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Clemens Rauer
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Burkhard Rost
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany; TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK.
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24
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Tran C, Khadkikar S, Porollo A. Survey of Protein Sequence Embedding Models. Int J Mol Sci 2023; 24:3775. [PMID: 36835188 PMCID: PMC9963412 DOI: 10.3390/ijms24043775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/23/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
Derived from the natural language processing (NLP) algorithms, protein language models enable the encoding of protein sequences, which are widely diverse in length and amino acid composition, in fixed-size numerical vectors (embeddings). We surveyed representative embedding models such as Esm, Esm1b, ProtT5, and SeqVec, along with their derivatives (GoPredSim and PLAST), to conduct the following tasks in computational biology: embedding the Saccharomyces cerevisiae proteome, gene ontology (GO) annotation of the uncharacterized proteins of this organism, relating variants of human proteins to disease status, correlating mutants of beta-lactamase TEM-1 from Escherichia coli with experimentally measured antimicrobial resistance, and analyzing diverse fungal mating factors. We discuss the advances and shortcomings, differences, and concordance of the models. Of note, all of the models revealed that the uncharacterized proteins in yeast tend to be less than 200 amino acids long, contain fewer aspartates and glutamates, and are enriched for cysteine. Less than half of these proteins can be annotated with GO terms with high confidence. The distribution of the cosine similarity scores of benign and pathogenic mutations to the reference human proteins shows a statistically significant difference. The differences in embeddings of the reference TEM-1 and mutants have low to no correlation with minimal inhibitory concentrations (MIC).
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Affiliation(s)
- Chau Tran
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Siddharth Khadkikar
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Aleksey Porollo
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH 45267, USA
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25
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Pratyush P, Pokharel S, Saigo H, KC DB. pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model. BMC Bioinformatics 2023; 24:41. [PMID: 36755242 PMCID: PMC9909867 DOI: 10.1186/s12859-023-05164-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. RESULTS Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. CONCLUSION Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite .
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Affiliation(s)
- Pawel Pratyush
- grid.259979.90000 0001 0663 5937Department of Computer Science, Michigan Technological University, Houghton, MI USA
| | - Suresh Pokharel
- grid.259979.90000 0001 0663 5937Department of Computer Science, Michigan Technological University, Houghton, MI USA
| | - Hiroto Saigo
- grid.177174.30000 0001 2242 4849Department of Electrical Engineering and Computer Science, Kyushu University, 744, Motooka, Nishi-Ku, 819-0395 Japan
| | - Dukka B. KC
- grid.259979.90000 0001 0663 5937Department of Computer Science, Michigan Technological University, Houghton, MI USA
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26
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UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 2023; 51:D523-D531. [PMID: 36408920 PMCID: PMC9825514 DOI: 10.1093/nar/gkac1052] [Citation(s) in RCA: 1315] [Impact Index Per Article: 1315.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/05/2022] [Accepted: 10/25/2022] [Indexed: 11/22/2022] Open
Abstract
The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.
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27
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Coudert E, Gehant S, de Castro E, Pozzato M, Baratin D, Neto T, Sigrist CJA, Redaschi N, Bridge A. Annotation of biologically relevant ligands in UniProtKB using ChEBI. Bioinformatics 2023; 39:6885442. [PMID: 36484697 PMCID: PMC9825770 DOI: 10.1093/bioinformatics/btac793] [Citation(s) in RCA: 62] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/09/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. RESULTS We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. AVAILABILITY AND IMPLEMENTATION Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Elisabeth Coudert
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Sebastien Gehant
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Edouard de Castro
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Monica Pozzato
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Delphine Baratin
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Teresa Neto
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Christian J A Sigrist
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | - Nicole Redaschi
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, Switzerland
| | | | - The UniProt Consortium
BridgeAlan JAimoLucilaArgoud-PuyGhislaineAuchinclossAndrea HAxelsenKristian BBansalParitBaratinDelphineNetoTeresa M BatistaBlatterMarie-ClaudeBollemanJerven TBoutetEmmanuelBreuzaLionelGilBlanca CabreraCasals-CasasCristinaEchioukhKamal ChikhCoudertElisabethCucheBeatricede CastroEdouardEstreicherAnneFamigliettiMaria LFeuermannMarcGasteigerElisabethGaudetPascaleGehantSebastienGerritsenVivienneGosArnaudGruazNadineHuloChantalHyka-NouspikelNevilaJungoFlorenceKerhornouArnaudLe MercierPhilippeLieberherrDamienMassonPatrickMorgatAnneMuthukrishnanVenkateshPaesanoSalvoPedruzziIvoPilboutSandrinePourcelLucillePouxSylvainPozzatoMonicaPruessManuelaRedaschiNicoleRivoireCatherineSigristChristian J ASonessonKarinSundaramShyamalaBatemanAlexMartinMaria-JesusOrchardSandraMagraneMicheleAhmadShadabAlpiEmanueleBowler-BarnettEmily HBrittoRamonaA-JeeHema Bye-CukuraAustraDennyPaulDoganTuncaEbenezerThankGodFanJunGarmiriPenelopeda Costa GonzalesLeonardo JoseHatton-EllisEmmaHusseinAbdulrahmanIgnatchenkoAlexandrInsanaGiuseppeIshtiaqRizwanJoshiVishalJyothiDushyanthKandasaamySwaathiLockAntoniaLucianiAurelienLugaricMarijaLuoJieLussiYvonneMacDougallAlistairMadeiraFabioMahmoudyMahdiMishraAlokMoulangKatieNightingaleAndrewPundirSangyaQiGuoyingRajShriyaRaposoPedroRiceDaniel LSaidiRabieSantosRafaelSperettaElenaStephensonJamesTotooPrabhatTurnerEdwardTyagiNidhiVasudevPreethiWarnerKateWatkinsXavierZaruRossanaZellnerHermannWuCathy HArighiCecilia NArminskiLeslieChenChumingChenYongxingHuangHongzhanLaihoKatiMcGarveyPeterNataleDarren ARossKarenVinayakaC RWangQinghuaWangYuqiSwiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneva 4, SwitzerlandEuropean Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UKProtein Information Resource, University of Delaware, Newark, DE 19711, USAProtein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA
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Olenyi T, Marquet C, Heinzinger M, Kröger B, Nikolova T, Bernhofer M, Sändig P, Schütze K, Littmann M, Mirdita M, Steinegger M, Dallago C, Rost B. LambdaPP: Fast and accessible protein-specific phenotype predictions. Protein Sci 2023; 32:e4524. [PMID: 36454227 PMCID: PMC9793974 DOI: 10.1002/pro.4524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022]
Abstract
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.
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Affiliation(s)
- Tobias Olenyi
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Céline Marquet
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Benjamin Kröger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Tiha Nikolova
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Michael Bernhofer
- TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Philip Sändig
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Milot Mirdita
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea
| | - Martin Steinegger
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea,Korea Artificial Intelligence InstituteSeoul National UniversitySeoulSouth Korea,Korea Institute of Molecular Biology and GeneticsSeoul National UniversitySeoulSouth Korea
| | - Christian Dallago
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,VantAINew YorkUSA
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,Institute for Advanced Study (TUM‐IAS)Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW)FreisingGermany
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29
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Zhu YH, Zhang C, Yu DJ, Zhang Y. Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction. PLoS Comput Biol 2022; 18:e1010793. [PMID: 36548439 PMCID: PMC9822105 DOI: 10.1371/journal.pcbi.1010793] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/06/2023] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Accurate identification of protein function is critical to elucidate life mechanisms and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained language models from protein sequences. The method was systematically tested on 1068 non-redundant benchmarking proteins and 3328 targets from the third Critical Assessment of Protein Function Annotation (CAFA) challenge. Experimental results showed that ATGO achieved a significant increase of the GO prediction accuracy compared to the state-of-the-art approaches in all aspects of molecular function, biological process, and cellular component. Detailed data analyses showed that the major advantage of ATGO lies in the utilization of pre-trained transformer language models which can extract discriminative functional pattern from the feature embeddings. Meanwhile, the proposed triplet network helps enhance the association of functional similarity with feature similarity in the sequence embedding space. In addition, it was found that the combination of the network scores with the complementary homology-based inferences could further improve the accuracy of the predicted models. These results demonstrated a new avenue for high-accuracy deep-learning function prediction that is applicable to large-scale protein function annotations from sequence alone.
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Affiliation(s)
- Yi-Heng Zhu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, People’s Republic of China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, People’s Republic of China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
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30
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Ferruz N, Heinzinger M, Akdel M, Goncearenco A, Naef L, Dallago C. From sequence to function through structure: Deep learning for protein design. Comput Struct Biotechnol J 2022; 21:238-250. [PMID: 36544476 PMCID: PMC9755234 DOI: 10.1016/j.csbj.2022.11.014] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 11/20/2022] Open
Abstract
The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.
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Key Words
- ADMM, Alternating Direction Method of Multipliers
- CNN, Convolutional Neural Network
- DL, Deep learning
- Deep learning
- Drug discovery
- FNN, fully-connected neural network
- GAN, Generative Adversarial Network
- GCN, Graph Convolutional Network
- GNN, Graph Neural Network
- GO, Gene Ontology
- GVP, Geometric Vector Perceptron
- LSTM, Long-Short Term Memory
- MLP, Multilayer Perceptron
- MSA, Multiple Sequence Alignment
- NLP, Natural Language Processing
- NSR, Natural Sequence Recovery
- Protein design
- Protein language models
- Protein prediction
- VAE, Variational Autoencoder
- pLM, protein Language Model
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Affiliation(s)
- Noelia Ferruz
- Institute of Informatics and Applications, University of Girona, Girona, Spain
- Department of Biochemistry, University of Bayreuth, Bayreuth, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany
| | - Mehmet Akdel
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
| | | | - Luca Naef
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
| | - Christian Dallago
- Department of Informatics, Bioinformatics & Computational Biology, Technische Universität München, 85748 Garching, Germany
- VantAI, 151 W 42nd Street, New York, NY 10036, United States
- NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany
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31
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Schütze K, Heinzinger M, Steinegger M, Rost B. Nearest neighbor search on embeddings rapidly identifies distant protein relations. FRONTIERS IN BIOINFORMATICS 2022; 2:1033775. [PMID: 36466147 PMCID: PMC9714024 DOI: 10.3389/fbinf.2022.1033775] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/31/2022] [Indexed: 11/29/2023] Open
Abstract
Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.
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Affiliation(s)
- Konstantin Schütze
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Garching, Germany
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology—i12, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Germany & TUM School of Life Sciences Weihenstephan (WZW), Freising, Germany
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32
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Ilzhöfer D, Heinzinger M, Rost B. SETH predicts nuances of residue disorder from protein embeddings. FRONTIERS IN BIOINFORMATICS 2022; 2:1019597. [PMID: 36304335 PMCID: PMC9580958 DOI: 10.3389/fbinf.2022.1019597] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/20/2022] [Indexed: 11/07/2022] Open
Abstract
Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer’s Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, “experimentally resolved” prediction and “relative solvent accessibility”) correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH’s nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.
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Affiliation(s)
- Dagmar Ilzhöfer
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany
| | - Michael Heinzinger
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany,Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), TUM Graduate School, Garching, Germany,*Correspondence: Michael Heinzinger,
| | - Burkhard Rost
- Faculty of Informatics, TUM (Technical University of Munich), Munich, Germany,Institute for Advanced Study (TUM-IAS), TUM (Technical University of Munich), Garching, Germany,TUM School of Life Sciences Weihenstephan (WZW), TUM (Technical University of Munich), Freising, Germany
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33
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Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet 2022; 141:1629-1647. [PMID: 34967936 PMCID: PMC8716573 DOI: 10.1007/s00439-021-02411-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/06/2021] [Indexed: 12/13/2022]
Abstract
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.
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Affiliation(s)
- Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Tobias Olenyi
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Kyra Erckert
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Dmitrii Nechaev
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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Bernhofer M, Rost B. TMbed: transmembrane proteins predicted through language model embeddings. BMC Bioinformatics 2022; 23:326. [PMID: 35941534 PMCID: PMC9358067 DOI: 10.1186/s12859-022-04873-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/03/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today's top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions. RESULTS Here, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94 ± 8% of the beta barrel TMPs (53 of 57) and 98 ± 1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060). CONCLUSIONS Based on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.
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Affiliation(s)
- Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology ‑ i12, Technical University of Munich (TUM), Boltzmannstr. 3, 85748, Garching, Germany. .,TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology ‑ i12, Technical University of Munich (TUM), Boltzmannstr. 3, 85748, Garching, Germany.,Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748, Garching, Germany.,TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
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Andreini C, Rosato A. Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications. Int J Mol Sci 2022; 23:ijms23147684. [PMID: 35887033 PMCID: PMC9323969 DOI: 10.3390/ijms23147684] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 02/04/2023] Open
Abstract
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.
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Affiliation(s)
- Claudia Andreini
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy;
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Antonio Rosato
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy;
- Magnetic Resonance Center (CERM), Department of Chemistry, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Correspondence:
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Tubiana J, Schneidman-Duhovny D, Wolfson HJ. ScanNet: A web server for structure-based prediction of protein binding sites with geometric deep learning. J Mol Biol 2022; 434:167758. [DOI: 10.1016/j.jmb.2022.167758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
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Heinzinger M, Littmann M, Sillitoe I, Bordin N, Orengo C, Rost B. Contrastive learning on protein embeddings enlightens midnight zone. NAR Genom Bioinform 2022; 4:lqac043. [PMID: 35702380 PMCID: PMC9188115 DOI: 10.1093/nargab/lqac043] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/25/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022] Open
Abstract
Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the 'midnight zone' of protein similarity, i.e. the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.
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Affiliation(s)
- Michael Heinzinger
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Maria Littmann
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Burkhard Rost
- TUM (Technical University of Munich) Dept Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
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Sokhansanj BA, Rosen GL. Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences. mSystems 2022; 7:e0003522. [PMID: 35311562 PMCID: PMC9040592 DOI: 10.1128/msystems.00035-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2022] [Indexed: 12/22/2022] Open
Abstract
Next-generation sequencing has been essential to the global response to the COVID-19 pandemic. As of January 2022, nearly 7 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences are available to researchers in public databases. Sequence databases are an abundant resource from which to extract biologically relevant and clinically actionable information. As the pandemic has gone on, SARS-CoV-2 has rapidly evolved, involving complex genomic changes that challenge current approaches to classifying SARS-CoV-2 variants. Deep sequence learning could be a potentially powerful way to build complex sequence-to-phenotype models. Unfortunately, while they can be predictive, deep learning typically produces "black box" models that cannot directly provide biological and clinical insight. Researchers should therefore consider implementing emerging methods for visualizing and interpreting deep sequence models. Finally, researchers should address important data limitations, including (i) global sequencing disparities, (ii) insufficient sequence metadata, and (iii) screening artifacts due to poor sequence quality control.
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Affiliation(s)
- Bahrad A. Sokhansanj
- Drexel University, Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Philadelphia, Pennsylvania, USA
| | - Gail L. Rosen
- Drexel University, Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Philadelphia, Pennsylvania, USA
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