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Sharma A, Chandrashekar CR, Krishna S, Sowdhamini R. Computational Analysis of the Accumulation of Mutations in Therapeutically Important RNA Viral Proteins During Pandemics with Special Emphasis on SARS-CoV-2. J Mol Biol 2024; 436:168716. [PMID: 39047897 DOI: 10.1016/j.jmb.2024.168716] [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: 04/07/2024] [Revised: 07/06/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024]
Abstract
Single stranded RNA viruses are primary causative agents for pandemics, causing extensive morbidity and mortality worldwide. A pivotal question in pandemic preparedness and therapeutic intervention is what are the specific mutations which are more likely to emerge during such global health crises? This study aims to identify markers for mutations with the highest probability of emergence in these pandemics, focusing on the SARS-CoV-2 spike protein, an essential and therapeutically significant viral protein, starting from sequence information from the onset of the pandemic until July 2022. Quite consistently, we observed that emerged mutations tended to demonstrate a high genetic score, which reflects high similarity of the type of codon required for translation between an amino acid and to the mutated one. Further, this pattern is also observed in therapeutically significant proteins of other ssRNA pandemic viruses, including influenza (HA, NA), spike proteins of Ebola, envelope of Dengue and Chikungunya. We propose that the genetic score serves as an initial indicator, preceding the actual impact of the mutation on viral fitness. Finally, we developed a comprehensive computational pipeline to further explore and predict the subsequent effects of mutations on viral fitness. We believe that our pipeline can narrow down and predict future mutations in therapeutically important viral proteins during a pandemic.
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Affiliation(s)
- Abhishek Sharma
- National Centre for Biological Science, GKVK Campus, Bengaluru 560065, India
| | - C R Chandrashekar
- National Centre for Biological Science, GKVK Campus, Bengaluru 560065, India
| | - Sudhir Krishna
- National Centre for Biological Science, GKVK Campus, Bengaluru 560065, India
| | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Banagalore 560012, India; Institute of Bioinformatics and Applied Biotechnology, Bangalore, 560100, India.
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2
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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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3
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Visani GM, Pun MN, Galvin W, Daniel E, Borisiak K, Wagura U, Nourmohammad A. HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602403. [PMID: 39026838 PMCID: PMC11257601 DOI: 10.1101/2024.07.09.602403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives.
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Affiliation(s)
- Gian Marco Visani
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Michael N. Pun
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
| | - William Galvin
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Eric Daniel
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Kevin Borisiak
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
| | - Utheri Wagura
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
- Department of Physics, Massachusetts Institute of Technology, 182 Memorial Dr, Cambridge, MA 02139
| | - Armita Nourmohammad
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
- Department of Applied Mathematics, University of Washington, Seattle, USA
- Fred Hutchinson cancer Research Center, 1100 Fairview ave N, Seattle, WA 98109, USA
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4
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Li P, Liu ZP. MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402918. [PMID: 38995072 DOI: 10.1002/advs.202402918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/04/2024] [Indexed: 07/13/2024]
Abstract
Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.
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Affiliation(s)
- Pengpai Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
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Visani GM, Pun MN, Galvin W, Daniel E, Borisiak K, Wagura U, Nourmohammad A. HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction. ARXIV 2024:arXiv:2407.06703v1. [PMID: 39040640 PMCID: PMC11261993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced significantly in predicting mutational effects. Here, we introduce HERMES, a 3D rotationally equivariant structure-based neural network model for mutational effect and stability prediction. Pre-trained to predict amino acid propensity from its surrounding 3D structure, HERMES can be fine-tuned for mutational effects using our open-source code. We present a suite of HERMES models, pre-trained with different strategies, and fine-tuned to predict the stability effect of mutations. Benchmarking against other models shows that HERMES often outperforms or matches their performance in predicting mutational effect on stability, binding, and fitness. HERMES offers versatile tools for evaluating mutational effects and can be fine-tuned for specific predictive objectives.
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Affiliation(s)
- Gian Marco Visani
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Michael N. Pun
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
| | - William Galvin
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Eric Daniel
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Kevin Borisiak
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
| | - Utheri Wagura
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
- Department of Physics, Massachusetts Institute of Technology, 182 Memorial Dr, Cambridge, MA 02139
| | - Armita Nourmohammad
- Department of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, WA 98195, USA
- Department of Applied Mathematics, University of Washington, Seattle, USA
- Fred Hutchinson cancer Research Center, 1100 Fairview ave N, Seattle, WA 98109, USA
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6
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Dadonaite B, Brown J, McMahon TE, Farrell AG, Figgins MD, Asarnow D, Stewart C, Lee J, Logue J, Bedford T, Murrell B, Chu HY, Veesler D, Bloom JD. Spike deep mutational scanning helps predict success of SARS-CoV-2 clades. Nature 2024; 631:617-626. [PMID: 38961298 PMCID: PMC11254757 DOI: 10.1038/s41586-024-07636-1] [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: 11/13/2023] [Accepted: 05/31/2024] [Indexed: 07/05/2024]
Abstract
SARS-CoV-2 variants acquire mutations in the spike protein that promote immune evasion1 and affect other properties that contribute to viral fitness, such as ACE2 receptor binding and cell entry2,3. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning4 to measure how more than 9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully affected ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456 and 473; however, the antigenic effects of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however, many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.
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Affiliation(s)
- Bernadeta Dadonaite
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jack Brown
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Teagan E McMahon
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ariana G Farrell
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Marlin D Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Daniel Asarnow
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Cameron Stewart
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Jimin Lee
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Jenni Logue
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, USA
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Ben Murrell
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Helen Y Chu
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA, USA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [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: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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8
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Xue S, Han Y, Wu F, Wang Q. Mutations in the SARS-CoV-2 spike receptor binding domain and their delicate balance between ACE2 affinity and antibody evasion. Protein Cell 2024; 15:403-418. [PMID: 38442025 PMCID: PMC11131022 DOI: 10.1093/procel/pwae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Abstract
Intensive selection pressure constrains the evolutionary trajectory of SARS-CoV-2 genomes and results in various novel variants with distinct mutation profiles. Point mutations, particularly those within the receptor binding domain (RBD) of SARS-CoV-2 spike (S) protein, lead to the functional alteration in both receptor engagement and monoclonal antibody (mAb) recognition. Here, we review the data of the RBD point mutations possessed by major SARS-CoV-2 variants and discuss their individual effects on ACE2 affinity and immune evasion. Many single amino acid substitutions within RBD epitopes crucial for the antibody evasion capacity may conversely weaken ACE2 binding affinity. However, this weakened effect could be largely compensated by specific epistatic mutations, such as N501Y, thus maintaining the overall ACE2 affinity for the spike protein of all major variants. The predominant direction of SARS-CoV-2 evolution lies neither in promoting ACE2 affinity nor evading mAb neutralization but in maintaining a delicate balance between these two dimensions. Together, this review interprets how RBD mutations efficiently resist antibody neutralization and meanwhile how the affinity between ACE2 and spike protein is maintained, emphasizing the significance of comprehensive assessment of spike mutations.
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Affiliation(s)
- Song Xue
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yuru Han
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Fan Wu
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Qiao Wang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
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9
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Quddusi DM, Hiremath SA, Bajcinca N. Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5996-6018. [PMID: 38872567 DOI: 10.3934/mbe.2024264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has been evolving rapidly after causing havoc worldwide in 2020. Since then, it has been very hard to contain the virus owing to its frequently mutating nature. Changes in its genome lead to viral evolution, rendering it more resistant to existing vaccines and drugs. Predicting viral mutations beforehand will help in gearing up against more infectious and virulent versions of the virus in turn decreasing the damage caused by them. In this paper, we have proposed different NMT (neural machine translation) architectures based on RNNs (recurrent neural networks) to predict mutations in the SARS-CoV-2-selected non-structural proteins (NSP), i.e., NSP1, NSP3, NSP5, NSP8, NSP9, NSP13, and NSP15. First, we created and pre-processed the pairs of sequences from two languages using k-means clustering and nearest neighbors for training a neural translation machine. We also provided insights for training NMTs on long biological sequences. In addition, we evaluated and benchmarked our models to demonstrate their efficiency and reliability.
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Affiliation(s)
- Darrak Moin Quddusi
- Chair of Mechatronics in the Faculty of Mechanical and Process Engineering, Rheinland-Pfalz Technical University of Kaiserslautern-Landau, Kaiserslautern 67663, Germany
| | - Sandesh Athni Hiremath
- Chair of Mechatronics in the Faculty of Mechanical and Process Engineering, Rheinland-Pfalz Technical University of Kaiserslautern-Landau, Kaiserslautern 67663, Germany
| | - Naim Bajcinca
- Chair of Mechatronics in the Faculty of Mechanical and Process Engineering, Rheinland-Pfalz Technical University of Kaiserslautern-Landau, Kaiserslautern 67663, Germany
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10
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585703. [PMID: 38746108 PMCID: PMC11092427 DOI: 10.1101/2024.03.19.585703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .
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11
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Meijers M, Ruchnewitz D, Eberhardt J, Karmakar M, Łuksza M, Lässig M. Concepts and methods for predicting viral evolution. ARXIV 2024:arXiv:2403.12684v2. [PMID: 38745695 PMCID: PMC11092678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.
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Affiliation(s)
- Matthijs Meijers
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Denis Ruchnewitz
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Jan Eberhardt
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Malancha Karmakar
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
| | - Marta Łuksza
- Tisch Cancer Institute, Departments of Oncological Sciences and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Lässig
- Institute for Biological Physics, University of Cologne, Zülpicherstr. 77, 50937, Köln, Germany
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12
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Li Y, Wu A, Zhou HY. EVEscape: Revealing potential escape sites based on the viral variation landscape. BIOPHYSICS REPORTS 2024; 10:133-134. [PMID: 38774351 PMCID: PMC11103722 DOI: 10.52601/bpr.2024.240902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/03/2024] [Indexed: 05/24/2024] Open
Affiliation(s)
| | - Aiping Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
| | - Hang-Yu Zhou
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, Jiangsu, China
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13
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Ordaya EE, Razonable RR. Emerging anti-spike monoclonal antibodies against SARS-CoV-2. Expert Opin Biol Ther 2024:1-11. [PMID: 38432691 DOI: 10.1080/14712598.2024.2326647] [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: 12/15/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024]
Abstract
INTRODUCTION Anti-spike monoclonal antibodies (mAbs) were previously authorized for the prevention and treatment of COVID-19 in immunocompromised patients. However, they are no longer authorized in the U.S. due to their lack of neutralizing activity against current circulating SARS-CoV-2 Omicron variants. AREAS COVERED We summarized the available data on emergent mAbs in the early stages of clinical development. Consistent with data on prior mAbs, these novel agents have been well tolerated and demonstrated a good safety profile in early clinical trials. Additionally, many of them have been engineered to ensure prolonged half-life and combined with other mAbs to overcome the potential for emerging resistant mutants. Interestingly, one of these agents has been evaluated using an inhaled route of administration, and another agent is being evaluated for treatment of long COVID. EXPERT OPINION Although the available data of novel mAbs holds promise, we anticipate that these agents will face similar challenges encountered by prior authorized agents, including the continued evolution of SARS-CoV-2 and emergence of new escape mutations. Strategies to potentially mitigate this are discussed. Based on prior successful experience, immunocompromised patients will certainly benefit from the utilization of mAbs for the prevention and treatment of COVID-19; thus, we need to design potential interventions to ensure the sustained activity of these agents.
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Affiliation(s)
- Eloy E Ordaya
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, MN, USA
| | - Raymund R Razonable
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, MN, USA
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14
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Maurer DP, Vu M, Schmidt AG. Antigenic drift expands viral escape pathways from imprinted host humoral immunity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585891. [PMID: 38562862 PMCID: PMC10983950 DOI: 10.1101/2024.03.20.585891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
An initial virus exposure can imprint antibodies such that future responses to antigenically drifted strains are dependent on the identity of the imprinting strain. Subsequent exposure to antigenically distinct strains followed by affinity maturation can guide immune responses toward generation of cross-reactive antibodies. How viruses evolve in turn to escape these imprinted broad antibody responses is unclear. Here, we used clonal antibody lineages from two human donors recognizing conserved influenza virus hemagglutinin (HA) epitopes to assess viral escape potential using deep mutational scanning. We show that even though antibody affinity maturation does restrict the number of potential escape routes in the imprinting strain through repositioning the antibody variable domains, escape is still readily observed in drifted strains and attributed to epistatic networks within HA. These data explain how influenza virus continues to evolve in the human population by escaping even broad antibody responses.
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15
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Koff WC, Anandkumar A, Poland GA. AI and the future of vaccine development. Vaccine 2024; 42:1407-1408. [PMID: 38296704 DOI: 10.1016/j.vaccine.2024.01.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
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16
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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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17
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Ektefaie Y, Shen A, Bykova D, Marin M, Zitnik M, Farhat M. Evaluating generalizability of artificial intelligence models for molecular datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581982. [PMID: 38464295 PMCID: PMC10925170 DOI: 10.1101/2024.02.25.581982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.
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Affiliation(s)
- Yasha Ektefaie
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Daria Bykova
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Maximillian Marin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
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18
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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19
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Notin P, Marks DS, Weitzman R, Gal Y. ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570473. [PMID: 38106034 PMCID: PMC10723423 DOI: 10.1101/2023.12.06.570473] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. However, computational methods for protein engineering are confronted with significant challenges, such as an expansive design space, sparse functional regions, and a scarcity of available labels. These issues are further exacerbated in practice by the fact most real-life design scenarios necessitate the simultaneous optimization of multiple properties. In this work, we introduce ProteinNPT, a non-parametric transformer variant tailored to protein sequences and particularly suited to label-scarce and multi-task learning settings. We first focus on the supervised fitness prediction setting and develop several cross-validation schemes which support robust performance assessment. We subsequently reimplement prior top-performing baselines, introduce several extensions of these baselines by integrating diverse branches of the protein engineering literature, and demonstrate that ProteinNPT consistently outperforms all of them across a diverse set of protein property prediction tasks. Finally, we demonstrate the value of our approach for iterative protein design across extensive in silico Bayesian optimization and conditional sampling experiments.
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Affiliation(s)
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
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20
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Dadonaite B, Brown J, McMahon TE, Farrell AG, Asarnow D, Stewart C, Logue J, Murrell B, Chu HY, Veesler D, Bloom JD. Full-spike deep mutational scanning helps predict the evolutionary success of SARS-CoV-2 clades. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566961. [PMID: 38014024 PMCID: PMC10680755 DOI: 10.1101/2023.11.13.566961] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
SARS-CoV-2 variants acquire mutations in spike that promote immune evasion and impact other properties that contribute to viral fitness such as ACE2 receptor binding and cell entry. Knowledge of how mutations affect these spike phenotypes can provide insight into the current and potential future evolution of the virus. Here we use pseudovirus deep mutational scanning to measure how >9,000 mutations across the full XBB.1.5 and BA.2 spikes affect ACE2 binding, cell entry, or escape from human sera. We find that mutations outside the receptor-binding domain (RBD) have meaningfully impacted ACE2 binding during SARS-CoV-2 evolution. We also measure how mutations to the XBB.1.5 spike affect neutralization by serum from individuals who recently had SARS-CoV-2 infections. The strongest serum escape mutations are in the RBD at sites 357, 420, 440, 456, and 473-however, the antigenic impacts of these mutations vary across individuals. We also identify strong escape mutations outside the RBD; however many of them decrease ACE2 binding, suggesting they act by modulating RBD conformation. Notably, the growth rates of human SARS-CoV-2 clades can be explained in substantial part by the measured effects of mutations on spike phenotypes, suggesting our data could enable better prediction of viral evolution.
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Affiliation(s)
- Bernadeta Dadonaite
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA
| | - Jack Brown
- Department of Biochemistry, University of Washington, Seattle, Washington, USA
| | - Teagan E McMahon
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA
| | - Ariana G Farrell
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA
| | - Daniel Asarnow
- Department of Biochemistry, University of Washington, Seattle, Washington, USA
| | - Cameron Stewart
- Department of Biochemistry, University of Washington, Seattle, Washington, USA
| | - Jenni Logue
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA
| | - Ben Murrell
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Helen Y. Chu
- University of Washington, Department of Medicine, Division of Allergy and Infectious Diseases, Seattle, WA
| | - David Veesler
- Department of Biochemistry, University of Washington, Seattle, Washington, USA
- Howard Hughes Medical Institute, Seattle, WA, 98195, USA
| | - Jesse D Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, 98109, USA
- Howard Hughes Medical Institute, Seattle, WA, 98195, USA
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21
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Parums DV. Editorial: Factors Driving New Variants of SARS-CoV-2, Immune Escape, and Resistance to Antiviral Treatments as the End of the COVID-19 Pandemic is Declared. Med Sci Monit 2023; 29:e942960. [PMID: 37908161 PMCID: PMC10626992 DOI: 10.12659/msm.942960] [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: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
On May 5, 2023, the Director-General of the World Health Organization (WHO) advised the transition to long-term management of the COVID-19 pandemic and that COVID-19 is now an established and ongoing health issue that is no longer a public health emergency of international concern (PHEIC). The WHO decision was based on an analysis of the decreasing trend in mortality, the decline in hospital admissions and Intensive Care Unit (ICU) admissions from COVID-19, and the increasing levels of population immunity to SARS-CoV-2. This Editorial aims to highlight what is known of the factors that drive new variants, subvariants, and lineages of SARS-CoV-2 associated with immune escape to previous infection or vaccines and resistance to antiviral treatments as the end of the COVID-19 pandemic is declared.
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Affiliation(s)
- Dinah V Parums
- Science Editor, Medical Science Monitor, International Scientific Information, Inc., Melville, NY, USA
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22
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Rochman ND, Koonin EV. Learn from the past to predict viral pandemics. Nature 2023; 622:700-702. [PMID: 37821603 DOI: 10.1038/d41586-023-02931-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
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23
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Callaway E. How AlphaFold and other AI tools could help us prepare for the next pandemic. Nature 2023; 622:440-441. [PMID: 37821614 DOI: 10.1038/d41586-023-03201-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
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