1
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Dong B, Liu Z, Xu D, Hou C, Dong G, Zhang T, Wang G. SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model. Comput Struct Biotechnol J 2024; 23:1364-1375. [PMID: 38596312 PMCID: PMC11001767 DOI: 10.1016/j.csbj.2024.03.018] [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: 12/05/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024] Open
Abstract
Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q 3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.
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Affiliation(s)
- Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zheng Liu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Dali Xu
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Chang Hou
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guanghui Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
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2
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Concha-Eloko R, Stock M, De Baets B, Briers Y, Sanjuán R, Domingo-Calap P, Boeckaerts D. DepoScope: Accurate phage depolymerase annotation and domain delineation using large language models. PLoS Comput Biol 2024; 20:e1011831. [PMID: 39102416 PMCID: PMC11326577 DOI: 10.1371/journal.pcbi.1011831] [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: 01/15/2024] [Revised: 08/15/2024] [Accepted: 07/20/2024] [Indexed: 08/07/2024] Open
Abstract
Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.
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Affiliation(s)
- Robby Concha-Eloko
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Paterna, Spain
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Yves Briers
- Laboratory of Applied Biotechnology, Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Rafael Sanjuán
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Paterna, Spain
| | - Pilar Domingo-Calap
- Institute for Integrative Systems Biology (I2SysBio), Universitat de Valencia-CSIC, Paterna, Spain
| | - Dimitri Boeckaerts
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
- Laboratory of Applied Biotechnology, Department of Biotechnology, Ghent University, Ghent, Belgium
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3
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Madan S, Lentzen M, Brandt J, Rueckert D, Hofmann-Apitius M, Fröhlich H. Transformer models in biomedicine. BMC Med Inform Decis Mak 2024; 24:214. [PMID: 39075407 PMCID: PMC11287876 DOI: 10.1186/s12911-024-02600-5] [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: 06/18/2023] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence (AI) field. The transformer model is a type of DNN that was originally used for the natural language processing tasks and has since gained more and more attention for processing various kinds of sequential data, including biological sequences and structured electronic health records. Along with this development, transformer-based models such as BioBERT, MedBERT, and MassGenie have been trained and deployed by researchers to answer various scientific questions originating in the biomedical domain. In this paper, we review the development and application of transformer models for analyzing various biomedical-related datasets such as biomedical textual data, protein sequences, medical structured-longitudinal data, and biomedical images as well as graphs. Also, we look at explainable AI strategies that help to comprehend the predictions of transformer-based models. Finally, we discuss the limitations and challenges of current models, and point out emerging novel research directions.
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Affiliation(s)
- Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
- Institute of Computer Science, University of Bonn, Bonn, 53115, Germany.
| | - Manuel Lentzen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany
| | - Johannes Brandt
- School of Medicine, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany
| | - Daniel Rueckert
- School of Medicine, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53757, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, 53115, Germany.
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4
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Susanty M, Mursalim MKN, Hertadi R, Purwarianti A, LE Rajab T. Leveraging protein language model embeddings and logistic regression for efficient and accurate in-silico acidophilic proteins classification. Comput Biol Chem 2024; 112:108163. [PMID: 39098138 DOI: 10.1016/j.compbiolchem.2024.108163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/02/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024]
Abstract
The increasing demand for eco-friendly technologies in biotechnology necessitates effective and sustainable catalysts. Acidophilic proteins, functioning optimally in highly acidic environments, hold immense promise for various applications, including food production, biofuels, and bioremediation. However, limited knowledge about these proteins hinders their exploration. This study addresses this gap by employing in silico methods utilizing computational tools and machine learning. We propose a novel approach to predict acidophilic proteins using protein language models (PLMs), accelerating discovery without extensive lab work. Our investigation highlights the potential of PLMs in understanding and harnessing acidophilic proteins for scientific and industrial advancements. We introduce the ACE model, which combines a simple Logistic Regression model with embeddings derived from protein sequences processed by the ProtT5 PLM. This model achieves high performance on an independent test set, with accuracy (0.91), F1-score (0.93), and Matthew's correlation coefficient (0.76). To our knowledge, this is the first application of pre-trained PLM embeddings for acidophilic protein classification. The ACE model serves as a powerful tool for exploring protein acidophilicity, paving the way for future advancements in protein design and engineering.
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Affiliation(s)
- Meredita Susanty
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Universitas Pertamina, School of Computer Science, Jl Teuku Nyak Arief Jakarta Selatan DKI, Jakarta, Indonesia
| | - Muhammad Khaerul Naim Mursalim
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Universitas UniversalKompleks Maha Vihara Duta Maitreya Bukit Beruntung, Sei Panas Batam, Kepulauan, Riau 29456, Indonesia
| | - Rukman Hertadi
- Institut Teknologi Bandung Faculty of Math and Natural Sciences, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia
| | - Ayu Purwarianti
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia; Center for Artificial Intelligence (U-CoE AI-VLB), Institut Teknologi Bandung, Bandung, Indonesia
| | - Tati LE Rajab
- Institut Teknologi Bandung School of Electrical Engineering and Informatics, Jl. Ganesa 10, Bandung, Jawa Barat, Indonesia.
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5
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Thompson M, Martín M, Olmo TS, Rajesh C, Koo PK, Bolognesi B, Lehner B. Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.13.603366. [PMID: 39071305 PMCID: PMC11275847 DOI: 10.1101/2024.07.13.603366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Insoluble amyloid aggregates are the hallmarks of more than fifty human diseases, including the most common neurodegenerative disorders. The process by which soluble proteins nucleate to form amyloid fibrils is, however, quite poorly characterized. Relatively few sequences are known that form amyloids with high propensity and this data shortage likely limits our capacity to understand, predict, engineer, and prevent the formation of amyloid fibrils. Here we quantify the nucleation of amyloids at an unprecedented scale and use the data to train a deep learning model of amyloid nucleation. In total, we quantify the nucleation rates of >100,000 20-amino-acid-long peptides. This large and diverse dataset allows us to train CANYA, a convolution-attention hybrid neural network. CANYA is fast and outperforms existing methods with stable performance across diverse prediction tasks. Interpretability analyses reveal CANYA's decision-making process and learned grammar, providing mechanistic insights into amyloid nucleation. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.
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6
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Cuturello F, Celoria M, Ansuini A, Cazzaniga A. Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models. Bioinformatics 2024; 40:btae447. [PMID: 39012369 PMCID: PMC11269464 DOI: 10.1093/bioinformatics/btae447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/19/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
MOTIVATION Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting. RESULTS We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. AVAILABILITY AND IMPLEMENTATION Code and data at https://github.com/RitAreaSciencePark/PLM4Muts. SUPPLEMENTARY INFORMATION Supplementary Information is available at Bioinformatics online.
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Affiliation(s)
- Francesca Cuturello
- Research and Technology Institute, , AREA Science Park, Trieste 34149, Italy
| | - Marco Celoria
- Research and Technology Institute, , AREA Science Park, Trieste 34149, Italy
- HPC Department, , CINECA National Supercomputing Center, Bologna 40033, Italy
| | - Alessio Ansuini
- Research and Technology Institute, , AREA Science Park, Trieste 34149, Italy
| | - Alberto Cazzaniga
- Research and Technology Institute, , AREA Science Park, Trieste 34149, Italy
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7
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Li MM, Huang Y, Sumathipala M, Liang MQ, Valdeolivas A, Ananthakrishnan AN, Liao K, Marbach D, Zitnik M. Contextual AI models for single-cell protein biology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549602. [PMID: 37503080 PMCID: PMC10370131 DOI: 10.1101/2023.07.18.549602] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.
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Affiliation(s)
- Michelle M. Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yepeng Huang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marissa Sumathipala
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Man Qing Liang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alberto Valdeolivas
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Ashwin N. Ananthakrishnan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | - Katherine Liao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, USA
| | - Daniel Marbach
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
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8
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Randall JR, Vieira LC, Wilke CO, Davies BW. Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity. Nat Biomed Eng 2024; 8:842-853. [PMID: 39085646 DOI: 10.1038/s41551-024-01243-1] [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: 08/20/2023] [Accepted: 05/12/2024] [Indexed: 08/02/2024]
Abstract
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However, this is difficult even for short peptides owing to the large combinatorial space of amino acid sequences. Here we describe a method for measuring the loss or maintenance of antimicrobial-peptide activity for thousands of peptide-sequence variants simultaneously, and its application to Protegrin-1, a potent yet toxic antimicrobial peptide, to determine the positional importance and flexibility of residues across its sequence while identifying variants with changes in membrane selectivity. More bacterially selective variants maintained a membrane-bound secondary structure while avoiding aromatic residues and cysteine pairs. A machine-learning model trained with our datasets accurately predicted membrane-specific activities for over 5.7 million Protegrin-1 variants, and identified one variant that showed substantially reduced toxicity and retention of activity in a mouse model of intraperitoneal infection. The high-throughput methodology may help elucidate sequence-structure-function relationships in antimicrobial peptides and inform the design of peptide-based synthetic drugs.
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Affiliation(s)
- Justin R Randall
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Luiz C Vieira
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Bryan W Davies
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA.
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9
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Norton-Baker B, Denton MCR, Murphy NP, Fram B, Lim S, Erickson E, Gauthier NP, Beckham GT. Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline. Sci Rep 2024; 14:14449. [PMID: 38914665 PMCID: PMC11196671 DOI: 10.1038/s41598-024-64938-0] [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: 04/08/2024] [Accepted: 06/14/2024] [Indexed: 06/26/2024] Open
Abstract
As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.
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Grants
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-SC0022024 U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- DE-AC36-08GO28308 Advanced Materials and Manufacturing Technologies Office (AMMTO)
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office (BETO)
- Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium
- Dana-Farber Cancer Institute
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Affiliation(s)
- Brenna Norton-Baker
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
- Agile BioFoundry, Emeryville, CA, USA
| | - Mackenzie C R Denton
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Natasha P Murphy
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Benjamin Fram
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuel Lim
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Erika Erickson
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
- BOTTLE Consortium, Golden, CO, USA
| | - Nicholas P Gauthier
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Gregg T Beckham
- Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA.
- BOTTLE Consortium, Golden, CO, USA.
- Agile BioFoundry, Emeryville, CA, USA.
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10
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Martínez Gascueña A, Wu H, Wang R, Owen CD, Hernando PJ, Monaco S, Penner M, Xing K, Le Gall G, Gardner R, Ndeh D, Urbanowicz PA, Spencer DIR, Walsh M, Angulo J, Juge N. Exploring the sequence-function space of microbial fucosidases. Commun Chem 2024; 7:137. [PMID: 38890439 PMCID: PMC11189522 DOI: 10.1038/s42004-024-01212-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024] Open
Abstract
Microbial α-L-fucosidases catalyse the hydrolysis of terminal α-L-fucosidic linkages and can perform transglycosylation reactions. Based on sequence identity, α-L-fucosidases are classified in glycoside hydrolases (GHs) families of the carbohydrate-active enzyme database. Here we explored the sequence-function space of GH29 fucosidases. Based on sequence similarity network (SSN) analyses, 15 GH29 α-L-fucosidases were selected for functional characterisation. HPAEC-PAD and LC-FD-MS/MS analyses revealed substrate and linkage specificities for α1,2, α1,3, α1,4 and α1,6 linked fucosylated oligosaccharides and glycoconjugates, consistent with their SSN clustering. The structural basis for the substrate specificity of GH29 fucosidase from Bifidobacterium asteroides towards α1,6 linkages and FA2G2 N-glycan was determined by X-ray crystallography and STD NMR. The capacity of GH29 fucosidases to carry out transfucosylation reactions with GlcNAc and 3FN as acceptors was evaluated by TLC combined with ESI-MS and NMR. These experimental data supported the use of SSN to further explore the GH29 sequence-function space through machine-learning models. Our lightweight protein language models could accurately allocate test sequences in their respective SSN clusters and assign 34,258 non-redundant GH29 sequences into SSN clusters. It is expected that the combination of these computational approaches will be used in the future for the identification of novel GHs with desired specificities.
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Affiliation(s)
- Ana Martínez Gascueña
- The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
| | - Haiyang Wu
- The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
- GuangDong Engineering Technology Research Center of Enzyme and Biocatalysis, Institute of Biological and Medical Engineering, Guangdong Academy of Sciences, Guangzhou, China
| | - Rui Wang
- Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
- Collaborative Innovation Center of Railway Traffic Safety, Beijing Jiaotong University, Beijing, China
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - C David Owen
- Diamond Light Source Ltd, Diamond House, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot, OX11 0FA, UK
| | - Pedro J Hernando
- The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
- Iceni Glycoscience Ltd., Norwich Research Park, Norwich, NR4 7JG, UK
| | - Serena Monaco
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | - Matthew Penner
- Diamond Light Source Ltd, Diamond House, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot, OX11 0FA, UK
| | - Ke Xing
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Gwenaelle Le Gall
- Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
| | | | - Didier Ndeh
- The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK
- University of Dundee, School of Life Sciences, Dundee, DD1 5EH, Scotland, UK
| | | | | | - Martin Walsh
- Diamond Light Source Ltd, Diamond House, Harwell Science and Innovation Campus, Didcot, OX11 0FA, UK
- Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot, OX11 0FA, UK
| | - Jesus Angulo
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
- Departamento de Química Orgánica, Universidad de Sevilla, 41012, Sevilla, Spain
- Instituto de Investigaciones Químicas (CSIC-US), 41092, Sevilla, Spain
| | - Nathalie Juge
- The Gut Microbes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, NR4 7UQ, UK.
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11
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Fooladi H, Hirte S, Kirchmair J. Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning. J Chem Inf Model 2024; 64:4031-4046. [PMID: 38739465 PMCID: PMC11134514 DOI: 10.1021/acs.jcim.4c00160] [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: 01/29/2024] [Revised: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multitask learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks; in other words, how difficult ("hard") a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning on the FS-Mol data set, we demonstrate that the proposed task hardness metric is inversely correlated with performance (Pearson's correlation coefficient r = -0.72). The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning.
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Affiliation(s)
- Hosein Fooladi
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry,
Faculty of Life Sciences, University of
Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
- Christian
Doppler Laboratory for Molecular Informatics in the Biosciences, Department
for Pharmaceutical Sciences, University
of Vienna, 1090 Vienna, Austria
- Vienna
Doctoral School of Pharmaceutical, Nutritional and Sport Sciences
(PhaNuSpo), University of Vienna, 1090 Vienna, Austria
| | - Steffen Hirte
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry,
Faculty of Life Sciences, University of
Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
- Vienna
Doctoral School of Pharmaceutical, Nutritional and Sport Sciences
(PhaNuSpo), University of Vienna, 1090 Vienna, Austria
| | - Johannes Kirchmair
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry,
Faculty of Life Sciences, University of
Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
- Christian
Doppler Laboratory for Molecular Informatics in the Biosciences, Department
for Pharmaceutical Sciences, University
of Vienna, 1090 Vienna, Austria
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12
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Leary AY, Scott D, Gupta NT, Waite JC, Skokos D, Atwal GS, Hawkins PG. Designing meaningful continuous representations of T cell receptor sequences with deep generative models. Nat Commun 2024; 15:4271. [PMID: 38769289 PMCID: PMC11106309 DOI: 10.1038/s41467-024-48198-0] [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: 06/18/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.
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Affiliation(s)
- Allen Y Leary
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
| | - Darius Scott
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Janelle C Waite
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Dimitris Skokos
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Gurinder S Atwal
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Peter G Hawkins
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
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13
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García Sánchez N, Ugarte Carro E, Prieto-Santamaría L, Rodríguez-González A. Protein sequence analysis in the context of drug repurposing. BMC Med Inform Decis Mak 2024; 24:122. [PMID: 38741115 DOI: 10.1186/s12911-024-02531-1] [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: 12/01/2023] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Drug repurposing speeds up the development of new treatments, being less costly, risky, and time consuming than de novo drug discovery. There are numerous biological elements that contribute to the development of diseases and, as a result, to the repurposing of drugs. METHODS In this article, we analysed the potential role of protein sequences in drug repurposing scenarios. For this purpose, we embedded the protein sequences by performing four state of the art methods and validated their capacity to encapsulate essential biological information through visualization. Then, we compared the differences in sequence distance between protein-drug target pairs of drug repurposing and non - drug repurposing data. Thus, we were able to uncover patterns that define protein sequences in repurposing cases. RESULTS We found statistically significant sequence distance differences between protein pairs in the repurposing data and the rest of protein pairs in non-repurposing data. In this manner, we verified the potential of using numerical representations of sequences to generate repurposing hypotheses in the future.
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Affiliation(s)
- Natalia García Sánchez
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, 28660, Spain
| | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain.
- ETS de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, 28660, Spain.
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14
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Hu F, Zhang W, Huang H, Li W, Li Y, Yin P. A Transferability-Based Method for Evaluating the Protein Representation Learning. IEEE J Biomed Health Inform 2024; 28:3158-3166. [PMID: 38416611 DOI: 10.1109/jbhi.2024.3370680] [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: 03/01/2024]
Abstract
Self-supervised pre-trained language models have recently risen as a powerful approach in learning protein representations, showing exceptional effectiveness in various biological tasks, such as drug discovery. Amidst the evolving trend in protein language model development, there is an observable shift towards employing large-scale multimodal and multitask models. However, the predominant reliance on empirical assessments using specific benchmark datasets for evaluating these models raises concerns about the comprehensiveness and efficiency of current evaluation methods. Addressing this gap, our study introduces a novel quantitative approach for estimating the performance of transferring multi-task pre-trained protein representations to downstream tasks. This transferability-based method is designed to quantify the similarities in latent space distributions between pre-trained features and those fine-tuned for downstream tasks. It encompasses a broad spectrum, covering multiple domains and a variety of heterogeneous tasks. To validate this method, we constructed a diverse set of protein-specific pre-training tasks. The resulting protein representations were then evaluated across several downstream biological tasks. Our experimental results demonstrate a robust correlation between the transferability scores obtained using our method and the actual transfer performance observed. This significant correlation highlights the potential of our method as a more comprehensive and efficient tool for evaluating protein representation learning.
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15
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Michael R, Kæstel-Hansen J, Mørch Groth P, Bartels S, Salomon J, Tian P, Hatzakis NS, Boomsma W. A systematic analysis of regression models for protein engineering. PLoS Comput Biol 2024; 20:e1012061. [PMID: 38701099 PMCID: PMC11095727 DOI: 10.1371/journal.pcbi.1012061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 05/15/2024] [Accepted: 04/10/2024] [Indexed: 05/05/2024] Open
Abstract
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.
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Affiliation(s)
- Richard Michael
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Peter Mørch Groth
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
- Enzyme Research, Novozymes A/S, Kongens Lyngby, Denmark
| | - Simon Bartels
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Pengfei Tian
- Enzyme Research, Novozymes A/S, Kongens Lyngby, Denmark
| | - Nikos S. Hatzakis
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
| | - Wouter Boomsma
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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16
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Vitale R, Bugnon LA, Fenoy EL, Milone DH, Stegmayer G. Evaluating large language models for annotating proteins. Brief Bioinform 2024; 25:bbae177. [PMID: 38706315 PMCID: PMC11070647 DOI: 10.1093/bib/bbae177] [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: 12/22/2023] [Revised: 03/15/2024] [Accepted: 03/27/2024] [Indexed: 05/07/2024] Open
Abstract
In UniProtKB, up to date, there are more than 251 million proteins deposited. However, only 0.25% have been annotated with one of the more than 15000 possible Pfam family domains. The current annotation protocol integrates knowledge from manually curated family domains, obtained using sequence alignments and hidden Markov models. This approach has been successful for automatically growing the Pfam annotations, however at a low rate in comparison to protein discovery. Just a few years ago, deep learning models were proposed for automatic Pfam annotation. However, these models demand a considerable amount of training data, which can be a challenge with poorly populated families. To address this issue, we propose and evaluate here a novel protocol based on transfer learningṪhis requires the use of protein large language models (LLMs), trained with self-supervision on big unnanotated datasets in order to obtain sequence embeddings. Then, the embeddings can be used with supervised learning on a small and annotated dataset for a specialized task. In this protocol we have evaluated several cutting-edge protein LLMs together with machine learning architectures to improve the actual prediction of protein domain annotations. Results are significatively better than state-of-the-art for protein families classification, reducing the prediction error by an impressive 60% compared to standard methods. We explain how LLMs embeddings can be used for protein annotation in a concrete and easy way, and provide the pipeline in a github repo. Full source code and data are available at https://github.com/sinc-lab/llm4pfam.
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Affiliation(s)
- Rosario Vitale
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Leandro A Bugnon
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Emilio Luis Fenoy
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
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17
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Chu HY, Fong JHC, Thean DGL, Zhou P, Fung FKC, Huang Y, Wong ASL. Accurate top protein variant discovery via low-N pick-and-validate machine learning. Cell Syst 2024; 15:193-203.e6. [PMID: 38340729 DOI: 10.1016/j.cels.2024.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/11/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Toward this goal, we present a simple and effective machine learning-based strategy that outperforms other state-of-the-art methods. Our strategy integrates zero-shot prediction and multi-round sampling to direct active learning via experimenting with only a few predicted top variants. We find that four rounds of low-N pick-and-validate sampling of 12 variants for machine learning yielded the best accuracy of up to 92.6% in selecting the true top 1% variants in combinatorial mutant libraries, whereas two rounds of 24 variants can also be used. We demonstrate our strategy in successfully discovering high-performance protein variants from diverse families including the CRISPR-based genome editors, supporting its generalizable application for solving protein engineering tasks. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Hoi Yee Chu
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - John H C Fong
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Dawn G L Thean
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Peng Zhou
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Frederic K C Fung
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Yuanhua Huang
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Alan S L Wong
- Laboratory of Combinatorial Genetics and Synthetic Biology, School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China.
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18
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Wang M, Patsenker J, Li H, Kluger Y, Kleinstein S. Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity. Nucleic Acids Res 2024; 52:548-557. [PMID: 38109302 PMCID: PMC10810273 DOI: 10.1093/nar/gkad1128] [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: 06/28/2023] [Revised: 10/18/2023] [Accepted: 11/11/2023] [Indexed: 12/20/2023] Open
Abstract
High throughput sequencing of B cell receptors (BCRs) is increasingly applied to study the immense diversity of antibodies. Learning biologically meaningful embeddings of BCR sequences is beneficial for predictive modeling. Several embedding methods have been developed for BCRs, but no direct performance benchmarking exists. Moreover, the impact of the input sequence length and paired-chain information on the prediction remains to be explored. We evaluated the performance of multiple embedding models to predict BCR sequence properties and receptor specificity. Despite the differences in model architectures, most embeddings effectively capture BCR sequence properties and specificity. BCR-specific embeddings slightly outperform general protein language models in predicting specificity. In addition, incorporating full-length heavy chains and paired light chain sequences improves the prediction performance of all embeddings. This study provides insights into the properties of BCR embeddings to improve downstream prediction applications for antibody analysis and discovery.
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Affiliation(s)
- Meng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | - Henry Li
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
| | - Yuval Kluger
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Program in Applied Mathematics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
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19
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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20
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James JK, Norland K, Johar AS, Kullo IJ. Deep generative models of LDLR protein structure to predict variant pathogenicity. J Lipid Res 2023; 64:100455. [PMID: 37821076 PMCID: PMC10696256 DOI: 10.1016/j.jlr.2023.100455] [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: 03/22/2023] [Revised: 09/16/2023] [Accepted: 10/05/2023] [Indexed: 10/13/2023] Open
Abstract
The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.
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Affiliation(s)
- Jose K James
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kristjan Norland
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Angad S Johar
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
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21
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Xie WJ, Warshel A. Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering. Natl Sci Rev 2023; 10:nwad331. [PMID: 38299119 PMCID: PMC10829072 DOI: 10.1093/nsr/nwad331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/27/2023] [Accepted: 10/13/2023] [Indexed: 02/02/2024] Open
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, Genetics Institute, University of Florida, Gainesville, FL 32610, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
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22
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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23
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Markus B, C GC, Andreas K, Arkadij K, Stefan L, Gustav O, Elina S, Radka S. Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design. ACS Catal 2023; 13:14454-14469. [PMID: 37942268 PMCID: PMC10629211 DOI: 10.1021/acscatal.3c03417] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology's limitations, unanswered questions, and unmet challenges.
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Affiliation(s)
- Braun Markus
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Gruber Christian C
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Krassnigg Andreas
- Enzyme
and Drug Discovery, Innophore. 1700 Montgomery Street, San Francisco, California 94111, United States
| | - Kummer Arkadij
- Moderna,
Inc., 200 Technology
Square, Cambridge, Massachusetts 02139, United States
| | - Lutz Stefan
- Codexis
Inc., 200 Penobscot Drive, Redwood City, California 94063, United States
| | - Oberdorfer Gustav
- Department
of Biochemistry, Graz University of Technology, Petersgasse 12/2, 8010 Graz, Austria
| | - Siirola Elina
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
| | - Snajdrova Radka
- Novartis
Institute for Biomedical Research, Global Discovery Chemistry, Basel CH-4108, Switzerland
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24
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Xie WJ, Warshel A. Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561808. [PMID: 37873334 PMCID: PMC10592750 DOI: 10.1101/2023.10.10.561808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.
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Affiliation(s)
- Wen Jun Xie
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
- Departmet of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development (CNPD3), Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Arieh Warshel
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA
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25
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Qiu Y, Wei GW. Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. Brief Bioinform 2023; 24:bbad289. [PMID: 37580175 PMCID: PMC10516362 DOI: 10.1093/bib/bbad289] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 08/16/2023] Open
Abstract
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, 48824 MI, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, 48824 MI, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824 MI, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824 MI, USA
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26
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Randall JR, Vieira LC, Wilke CO, Davies BW. Deep mutational scanning and machine learning uncover antimicrobial peptide features driving membrane selectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.28.551017. [PMID: 37547010 PMCID: PMC10402124 DOI: 10.1101/2023.07.28.551017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Antimicrobial peptides commonly act by disrupting bacterial membranes, but also frequently damage mammalian membranes. Deciphering the rules governing membrane selectivity is critical to understanding their function and enabling their therapeutic use. Past attempts to decipher these rules have failed because they cannot interrogate adequate peptide sequence variation. To overcome this problem, we develop deep mutational surface localized antimicrobial display (dmSLAY), which reveals comprehensive positional residue importance and flexibility across an antimicrobial peptide sequence. We apply dmSLAY to Protegrin-1, a potent yet toxic antimicrobial peptide, and identify thousands of sequence variants that positively or negatively influence its antibacterial activity. Further analysis reveals that avoiding large aromatic residues and eliminating disulfide bound cysteine pairs while maintaining membrane bound secondary structure greatly improves Protegrin-1 bacterial specificity. Moreover, dmSLAY datasets enable machine learning to expand our analysis to include over 5.7 million sequence variants and reveal full Protegrin-1 mutational profiles driving either bacterial or mammalian membrane specificity. Our results describe an innovative, high-throughput approach for elucidating antimicrobial peptide sequence-structure-function relationships which can inform synthetic peptide-based drug design.
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Affiliation(s)
- Justin R. Randall
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712
| | - Luiz C. Vieira
- Department of Integrative Biology, University of Texas at Austin; Austin, Texas, 78712
| | - Claus O. Wilke
- Department of Integrative Biology, University of Texas at Austin; Austin, Texas, 78712
| | - Bryan W. Davies
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712
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27
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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: 5] [Impact Index Per Article: 5.0] [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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28
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Scientific discovery in the age of artificial intelligence. Nature 2023; 620:47-60. [PMID: 37532811 DOI: 10.1038/s41586-023-06221-2] [Citation(s) in RCA: 105] [Impact Index Per Article: 105.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
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Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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29
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Qiu Y, Wei GW. Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. ARXIV 2023:arXiv:2307.14587v1. [PMID: 37547662 PMCID: PMC10402185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, 48824, MI, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, 48824, MI, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824, MI, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, 48824, MI, USA
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30
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Saar KL, Qian D, Good LL, Morgunov AS, Collepardo-Guevara R, Best RB, Knowles TPJ. Theoretical and Data-Driven Approaches for Biomolecular Condensates. Chem Rev 2023; 123:8988-9009. [PMID: 37171907 PMCID: PMC10375482 DOI: 10.1021/acs.chemrev.2c00586] [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: 08/23/2022] [Indexed: 05/14/2023]
Abstract
Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.
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Affiliation(s)
- Kadi L. Saar
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Transition
Bio Ltd., Cambridge, United Kingdom
| | - Daoyuan Qian
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Lydia L. Good
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Laboratory
of Chemical Physics, National Institute of Diabetes and Digestive
and Kidney Diseases, National Institutes
of Health, Bethesda, Maryland 20892, United States
| | - Alexey S. Morgunov
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Rosana Collepardo-Guevara
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Department
of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom
| | - Robert B. Best
- Laboratory
of Chemical Physics, National Institute of Diabetes and Digestive
and Kidney Diseases, National Institutes
of Health, Bethesda, Maryland 20892, United States
| | - Tuomas P. J. Knowles
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Cambridge CB2 1EW, United Kingdom
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, Cambridge CB3 0HE, United Kingdom
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31
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Li Y, Yao Y, Xia Y, Tang M. Searching for protein variants with desired properties using deep generative models. BMC Bioinformatics 2023; 24:297. [PMID: 37480001 PMCID: PMC10362698 DOI: 10.1186/s12859-023-05415-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 07/17/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Protein engineering aims to improve the functional properties of existing proteins to meet people's needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing varieties. RESULTS To improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original sequence. CONCLUSION Compared to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model's generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model.
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Affiliation(s)
- Yan Li
- School of Information, Yunnan Normal University, Kunming, China
| | - Yinying Yao
- National Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yu Xia
- School of Information, Yunnan Normal University, Kunming, China
| | - Mingjing Tang
- Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, China.
- School of Life Science, Yunnan Normal University, Kunming, China.
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32
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Kroll A, Rousset Y, Hu XP, Liebrand NA, Lercher MJ. Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat Commun 2023; 14:4139. [PMID: 37438349 DOI: 10.1038/s41467-023-39840-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/27/2023] [Indexed: 07/14/2023] Open
Abstract
The turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through differential reaction fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server.
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Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Yvan Rousset
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Xiao-Pan Hu
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Nina A Liebrand
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
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33
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Kroll A, Ranjan S, Engqvist MKM, Lercher MJ. A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nat Commun 2023; 14:2787. [PMID: 37188731 DOI: 10.1038/s41467-023-38347-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 04/21/2023] [Indexed: 05/17/2023] Open
Abstract
For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.
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Affiliation(s)
- Alexander Kroll
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany
| | - Sahasra Ranjan
- Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Martin K M Engqvist
- Department of Biology and Bioengineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- EnginZyme AB, Tomtebodevägen 6, 17165, Stockholm, Sweden
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, D-40225, Düsseldorf, Germany.
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34
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Vasina M, Kovar D, Damborsky J, Ding Y, Yang T, deMello A, Mazurenko S, Stavrakis S, Prokop Z. In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnol Adv 2023; 66:108171. [PMID: 37150331 DOI: 10.1016/j.biotechadv.2023.108171] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/04/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - David Kovar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland; Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 602 00 Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, 656 91 Brno, Czech Republic.
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35
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Ziegler C, Martin J, Sinner C, Morcos F. Latent generative landscapes as maps of functional diversity in protein sequence space. Nat Commun 2023; 14:2222. [PMID: 37076519 PMCID: PMC10113739 DOI: 10.1038/s41467-023-37958-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 04/05/2023] [Indexed: 04/21/2023] Open
Abstract
Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.
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Affiliation(s)
- Cheyenne Ziegler
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Jonathan Martin
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Claude Sinner
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, 75080, USA.
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, 75080, USA.
- Center for Systems Biology, University of Texas at Dallas, Richardson, TX, 75080, USA.
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36
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Derry A, Altman RB. COLLAPSE: A representation learning framework for identification and characterization of protein structural sites. Protein Sci 2023; 32:e4541. [PMID: 36519247 PMCID: PMC9847082 DOI: 10.1002/pro.4541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/02/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure-function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein-protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.
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Affiliation(s)
- Alexander Derry
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
| | - Russ B. Altman
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
- Departments of Bioengineering, Genetics, and MedicineStanford UniversityStanfordCaliforniaUSA
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37
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
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38
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Castro E, Godavarthi A, Rubinfien J, Givechian K, Bhaskar D, Krishnaswamy S. Transformer-based protein generation with regularized latent space optimization. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00532-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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39
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Capel H, Weiler R, Dijkstra M, Vleugels R, Bloem P, Feenstra KA. ProteinGLUE multi-task benchmark suite for self-supervised protein modeling. Sci Rep 2022; 12:16047. [PMID: 36163232 PMCID: PMC9512797 DOI: 10.1038/s41598-022-19608-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue .
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Affiliation(s)
- Henriette Capel
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Robin Weiler
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Maurits Dijkstra
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Reinier Vleugels
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Peter Bloem
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - K Anton Feenstra
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands.
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40
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Li Q, Zhang X, Wu L, Bo X, He S, Wang S. PLA-MoRe: A Protein-Ligand Binding Affinity Prediction Model via Comprehensive Molecular Representations. J Chem Inf Model 2022; 62:4380-4390. [PMID: 36054653 DOI: 10.1021/acs.jcim.2c00960] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accurately predicting the binding affinity of protein-ligand pairs is an essential part of drug discovery. Since wet laboratory experiments to determine the binding affinity are expensive and time-consuming, several computational methods for binding affinity prediction have been proposed. In the representation of compounds, most methods only focus on the structural properties such as SMILES and ignore the bioactive properties. In this study, we proposed a novel model named PLA-MoRe to predict protein-ligand binding affinity, which represents compounds based on both structural and bioactive properties and mainly contains three feature extractors. First, a structure feature extractor based on the graph isomorphism network was constructed to learn the representations of the molecular graphs. Second, we designed an Autoencoder-based bioactive feature extractor to integrate the multisource bioactive information including chemical, target, network, cellular, and clinical. The above two parts aimed to learn representations of compounds in terms of structures and bioactivities, respectively. Then, we constructed a sequence feature extractor to learn embeddings for protein sequences. The output of the three extractors was concatenated and fed into a fully connected network for affinity prediction. We compared PLA-MoRe with three state-of-the-art methods, and an ablation study was conducted to test the role of each part of the model. Further attention visualization showed that our model had the potential to locate the binding sites, which might help explain the mechanism of interaction. These results prove that PLA-MoRe is competitive and reliable. The resource codes are freely available at the GitHub repository https://github.com/QingyuLiaib/PLA-MoRe.
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Affiliation(s)
- Qingyu Li
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xiaochang Zhang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Song He
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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41
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Beck JD, Roberts JM, Kitzhaber JM, Trapp A, Serra E, Spezzano F, Hayden EJ. Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data. Front Mol Biosci 2022; 9:893864. [PMID: 36046603 PMCID: PMC9421044 DOI: 10.3389/fmolb.2022.893864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.
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Affiliation(s)
| | - Jessica M. Roberts
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, United States
| | - Joey M. Kitzhaber
- Department of Computer Science, Boise State University, Boise, ID, United States
| | - Ashlyn Trapp
- Department of Biological Sciences, Boise State University, Boise, ID, United States
| | | | | | - Eric J. Hayden
- Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID, United States
- Department of Computer Science, Boise State University, Boise, ID, United States
- *Correspondence: Eric J. Hayden,
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