1
|
Marsili G, Pallotto C, Fortuna C, Amendola A, Fiorentini C, Esperti S, Blanc P, Suardi LR, Giulietta V, Argentini C. Fifty years after the first identification of Toscana virus in Italy: Genomic characterization of viral isolates within lineage A and aminoacidic markers of evolution. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2024; 122:105601. [PMID: 38830443 DOI: 10.1016/j.meegid.2024.105601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/18/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024]
Abstract
Toscana Virus (TosV) was firstly isolated from phlebotomine in our Institute about fifty years ago. Later, in 1984-1985, TosV infection, although asymptomatic in most cases, was shown to cause disease in humans, mainly fever and meningitis. By means of genetic analysis of part of M segment, we describe 3 new viral isolates obtained directly from cerebrospinal fluid or sera samples of patients diagnosed with TosV infection in July 2020 in Tuscany region. Phylogenesis was used to propose the clustering of TosV lineage A strains in 3 main groups, whereas deep mutational analysis based on 12 amino acid positions, allowed the identification of 9 putative strains. We discuss deep mutational analysis as a method to identify molecular signature of host adaptation and/or pathogenesis.
Collapse
Affiliation(s)
- Giulia Marsili
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Roma, Italy
| | - Carlo Pallotto
- SOC Malattie Infettive 1, Azienda USL Toscana Centro, Bagno a Ripoli, Firenze, Italy; Clinica delle Malattie Infettive, Azienda Ospedaliera Santa Maria della Misericordia, Università di Perugia, Perugia, Italy
| | - Claudia Fortuna
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Roma, Italy
| | - Antonello Amendola
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Roma, Italy
| | | | - Sara Esperti
- SOC Malattie Infettive 1, Azienda USL Toscana Centro, Bagno a Ripoli, Firenze, Italy; Dipartimento di Malattie Infettive, Azienda Ospedaliero-Universitaria di Modena, Policlinico di Modena, Università di Modena e Reggio Emilia, Modena, Italy
| | - Pierluigi Blanc
- SOC Malattie Infettive 1, Azienda USL Toscana Centro, Bagno a Ripoli, Firenze, Italy; SOC Malattie Infettive 2, Azienda USL Toscana Centro, Pistoia, Italy
| | - Lorenzo Roberto Suardi
- SOC Malattie Infettive 1, Azienda USL Toscana Centro, Bagno a Ripoli, Firenze, Italy; UO Malattie Infettive, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Venturi Giulietta
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Roma, Italy
| | - Claudio Argentini
- Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Roma, Italy.
| |
Collapse
|
2
|
Ozkan S, Padilla N, de la Cruz X. QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning. Hum Genet 2024:10.1007/s00439-024-02692-z. [PMID: 39048855 DOI: 10.1007/s00439-024-02692-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/14/2024] [Indexed: 07/27/2024]
Abstract
Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.
Collapse
Affiliation(s)
- Selen Ozkan
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Natàlia Padilla
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier de la Cruz
- Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Cheng P, Mao C, Tang J, Yang S, Cheng Y, Wang W, Gu Q, Han W, Chen H, Li S, Chen Y, Zhou J, Li W, Pan A, Zhao S, Huang X, Zhu S, Zhang J, Shu W, Wang S. Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering. Cell Res 2024:10.1038/s41422-024-00989-2. [PMID: 38969803 DOI: 10.1038/s41422-024-00989-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/03/2024] [Indexed: 07/07/2024] Open
Abstract
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.
Collapse
Affiliation(s)
- Peng Cheng
- Bioinformatics Center of AMMS, Beijing, China
| | - Cong Mao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jin Tang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Sen Yang
- Bioinformatics Center of AMMS, Beijing, China
| | - Yu Cheng
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wuke Wang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Qiuxi Gu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Han
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hao Chen
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sihan Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | | | | | - Wuju Li
- Bioinformatics Center of AMMS, Beijing, China
| | - Aimin Pan
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xingxu Huang
- Zhejiang Lab, Hangzhou, Zhejiang, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | | | - Jun Zhang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Wenjie Shu
- Bioinformatics Center of AMMS, Beijing, China.
| | | |
Collapse
|
5
|
Dereli O, Kuru N, Akkoyun E, Bircan A, Tastan O, Adebali O. PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting. Mol Biol Evol 2024; 41:msae136. [PMID: 38934805 PMCID: PMC11251492 DOI: 10.1093/molbev/msae136] [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: 12/12/2023] [Revised: 05/30/2024] [Accepted: 06/24/2024] [Indexed: 06/28/2024] Open
Abstract
Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.
Collapse
Affiliation(s)
- Onur Dereli
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Emrah Akkoyun
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
- Network Technologies Department, TÜBİTAK-ULAKBİM Turkish Academic Network and Information Center, Ankara 06530, Turkey
| | - Aylin Bircan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Ogün Adebali
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
- Biological Sciences, TÜBİTAK Research Institute for Fundamental Sciences, Gebze 41470, Turkey
| |
Collapse
|
6
|
Zhou Z, Zhang L, Yu Y, Wu B, Li M, Hong L, Tan P. Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning. Nat Commun 2024; 15:5566. [PMID: 38956442 PMCID: PMC11219809 DOI: 10.1038/s41467-024-49798-6] [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: 02/02/2024] [Accepted: 06/11/2024] [Indexed: 07/04/2024] Open
Abstract
Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.
Collapse
Affiliation(s)
- Ziyi Zhou
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Liang Zhang
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yuanxi Yu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Banghao Wu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingchen Li
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Liang Hong
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
- Zhang Jiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 201203, China.
| | - Pan Tan
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| |
Collapse
|
7
|
Cocco S, Posani L, Monasson R. Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information. Proc Natl Acad Sci U S A 2024; 121:e2312335121. [PMID: 38889151 PMCID: PMC11214004 DOI: 10.1073/pnas.2312335121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 04/21/2024] [Indexed: 06/20/2024] Open
Abstract
Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.
Collapse
Affiliation(s)
- Simona Cocco
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 and Paris Sciences & Lettres (PSL) Research, Sorbonne Université, 75005Paris, France
| | - Lorenzo Posani
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 and Paris Sciences & Lettres (PSL) Research, Sorbonne Université, 75005Paris, France
| | - Rémi Monasson
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 and Paris Sciences & Lettres (PSL) Research, Sorbonne Université, 75005Paris, France
| |
Collapse
|
8
|
Gouliaev F, Jonsson N, Gersing S, Lisby M, Lindorff-Larsen K, Hartmann-Petersen R. Destabilization and Degradation of a Disease-Linked PGM1 Protein Variant. Biochemistry 2024; 63:1423-1433. [PMID: 38743592 DOI: 10.1021/acs.biochem.4c00042] [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: 05/16/2024]
Abstract
PGM1-linked congenital disorder of glycosylation (PGM1-CDG) is an autosomal recessive disease characterized by several phenotypes, some of which are life-threatening. Research focusing on the disease-related variants of the α-D-phosphoglucomutase 1 (PGM1) protein has shown that several are insoluble in vitro and expressed at low levels in patient fibroblasts. Due to these observations, we hypothesized that some disease-linked PGM1 protein variants are structurally destabilized and subject to protein quality control (PQC) and rapid intracellular degradation. Employing yeast-based assays, we show that a disease-associated human variant, PGM1 L516P, is insoluble, inactive, and highly susceptible to ubiquitylation and rapid degradation by the proteasome. In addition, we show that PGM1 L516P forms aggregates in S. cerevisiae and that both the aggregation pattern and the abundance of PGM1 L516P are chaperone-dependent. Finally, using computational methods, we perform saturation mutagenesis to assess the impact of all possible single residue substitutions in the PGM1 protein. These analyses identify numerous missense variants with predicted detrimental effects on protein function and stability. We suggest that many disease-linked PGM1 variants are subject to PQC-linked degradation and that our in silico site-saturated data set may assist in the mechanistic interpretation of PGM1 variants.
Collapse
Affiliation(s)
- Frederik Gouliaev
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| | - Nicolas Jonsson
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| | - Sarah Gersing
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- Department of Biology, University of Copenhagen, Ole Maalo̷es Vej 5, DK2200N Copenhagen, Denmark
| |
Collapse
|
9
|
Yee SW, Ferrández-Peral L, Alentorn-Moron P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Wolfreys FD, Yang J, Gestwicki JE, Capra JA, Artursson P, Newman JW, Marquès-Bonet T, Giacomini KM. Illuminating the function of the orphan transporter, SLC22A10, in humans and other primates. Nat Commun 2024; 15:4380. [PMID: 38782905 PMCID: PMC11116522 DOI: 10.1038/s41467-024-48569-7] [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: 08/14/2023] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
SLC22A10 is an orphan transporter with unknown substrates and function. The goal of this study is to elucidate its substrate specificity and functional characteristics. In contrast to orthologs from great apes, human SLC22A10, tagged with green fluorescent protein, is not expressed on the plasma membrane. Cells expressing great ape SLC22A10 orthologs exhibit significant accumulation of estradiol-17β-glucuronide, unlike those expressing human SLC22A10. Sequence alignments reveal a proline at position 220 in humans, which is a leucine in great apes. Replacing proline with leucine in SLC22A10-P220L restores plasma membrane localization and uptake function. Neanderthal and Denisovan genomes show proline at position 220, akin to modern humans, indicating functional loss during hominin evolution. Human SLC22A10 is a unitary pseudogene due to a fixed missense mutation, P220, while in great apes, its orthologs transport sex steroid conjugates. Characterizing SLC22A10 across species sheds light on its biological role, influencing organism development and steroid homeostasis.
Collapse
Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Luis Ferrández-Peral
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Pol Alentorn-Moron
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
- Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352, Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Megan L Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Niklas Handin
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Virginia M Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA, 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394, Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, US
| | - Colin M Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Finn D Wolfreys
- Department of Ophthalmology, University of California, San Francisco, CA, USA
| | - Jia Yang
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Jason E Gestwicki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, USA
| | - John A Capra
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
- Science for Life Laboratories, Uppsala University, Uppsala, Sweden
| | - John W Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA, 95616, USA
- Department of Nutrition, University of California, Davis, Davis, CA, 95616, USA
| | - Tomàs Marquès-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003, Barcelona, Spain
- Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain
- CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028, Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193, Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
| |
Collapse
|
10
|
Grønbæk-Thygesen M, Voutsinos V, Johansson KE, Schulze TK, Cagiada M, Pedersen L, Clausen L, Nariya S, Powell RL, Stein A, Fowler DM, Lindorff-Larsen K, Hartmann-Petersen R. Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants. Nat Commun 2024; 15:4026. [PMID: 38740822 DOI: 10.1038/s41467-024-48481-0] [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: 10/18/2023] [Accepted: 05/02/2024] [Indexed: 05/16/2024] Open
Abstract
Unstable proteins are prone to form non-native interactions with other proteins and thereby may become toxic. To mitigate this, destabilized proteins are targeted by the protein quality control network. Here we present systematic studies of the cytosolic aspartoacylase, ASPA, where variants are linked to Canavan disease, a lethal neurological disorder. We determine the abundance of 6152 of the 6260 ( ~ 98%) possible single amino acid substitutions and nonsense ASPA variants in human cells. Most low abundance variants are degraded through the ubiquitin-proteasome pathway and become toxic upon prolonged expression. The data correlates with predicted changes in thermodynamic stability, evolutionary conservation, and separate disease-linked variants from benign variants. Mapping of degradation signals (degrons) shows that these are often buried and the C-terminal region functions as a degron. The data can be used to interpret Canavan disease variants and provide insight into the relationship between protein stability, degradation and cell fitness.
Collapse
Affiliation(s)
- Martin Grønbæk-Thygesen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Vasileios Voutsinos
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer E Johansson
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Thea K Schulze
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Line Pedersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Lene Clausen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Snehal Nariya
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rachel L Powell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Amelie Stein
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
11
|
Klink GV, Kalinina OV, Bazykin GA. Changing selection on amino acid substitutions in Gag protein between major HIV-1 subtypes. Virus Evol 2024; 10:veae036. [PMID: 38808036 PMCID: PMC11131029 DOI: 10.1093/ve/veae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 12/27/2023] [Accepted: 04/28/2024] [Indexed: 05/30/2024] Open
Abstract
Amino acid preferences at a protein site depend on the role of this site in protein function and structure as well as on external constraints. All these factors can change in the course of evolution, making amino acid propensities of a site time-dependent. When viral subtypes divergently evolve in different host subpopulations, such changes may depend on genetic, medical, and sociocultural differences between these subpopulations. Here, using our previously developed phylogenetic approach, we describe sixty-nine amino acid sites of the Gag protein of human immunodeficiency virus type 1 (HIV-1) where amino acids have different impact on viral fitness in six major subtypes of the type M. These changes in preferences trigger adaptive evolution; indeed, 32 (46 per cent) of these sites experienced strong positive selection at least in one of the subtypes. At some of the sites, changes in amino acid preferences may be associated with differences in immune escape between subtypes. The prevalence of an amino acid in a protein site within a subtype is only a poor predictor for whether this amino acid is preferred in this subtype according to the phylogenetic analysis. Therefore, attempts to identify the factors of viral evolution from comparative genomics data should integrate across multiple sources of information.
Collapse
Affiliation(s)
- Galya V Klink
- Laboratory of Molecular Evolution, Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences, Bolshoy Karetny per. 19, build.1, Moscow 127051, Russia
- Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard, 30, p.1, Skolkovo 121205, Russia
| | - Olga V Kalinina
- Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)/Helmholtz Centre for Infection Research (HZI), Campus E8.1, Saarbrücken 66123, Germany
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken 66123, Germany
- Medical Faculty, Saarland University, Kirrberger Str. 100, Homburg 66421, Germany
| | - Georgii A Bazykin
- Laboratory of Molecular Evolution, Institute for Information Transmission Problems (Kharkevich Institute) of the Russian Academy of Sciences, Bolshoy Karetny per. 19, build.1, Moscow 127051, Russia
| |
Collapse
|
12
|
Livesey BJ, Badonyi M, Dias M, Frazer J, Kumar S, Lindorff-Larsen K, McCandlish DM, Orenbuch R, Shearer CA, Muffley L, Foreman J, Glazer AM, Lehner B, Marks DS, Roth FP, Rubin AF, Starita LM, Marsh JA. Guidelines for releasing a variant effect predictor. ARXIV 2024:arXiv:2404.10807v1. [PMID: 38699161 PMCID: PMC11065047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.
Collapse
Affiliation(s)
- Benjamin J. Livesey
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Mihaly Badonyi
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Mafalda Dias
- Centre for Genomic Regulation (CRG),The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jonathan Frazer
- Centre for Genomic Regulation (CRG),The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Sushant Kumar
- Department of Medical Biophysics, University of Toronto; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - David M. McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rose Orenbuch
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | | | - Lara Muffley
- Department of Genome Sciences, University of Washington and the Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Julia Foreman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Ben Lehner
- Wellcome Sanger Institute, Cambridge, UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Debora S. Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Frederick P. Roth
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alan F. Rubin
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research; Department of Medical Biology, University of Melbourne, Parkville, Australia
| | - Lea M. Starita
- Department of Genome Sciences, University of Washington and the Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Joseph A. Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
13
|
Grønbæk-Thygesen M, Hartmann-Petersen R. Cellular and molecular mechanisms of aspartoacylase and its role in Canavan disease. Cell Biosci 2024; 14:45. [PMID: 38582917 PMCID: PMC10998430 DOI: 10.1186/s13578-024-01224-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/24/2024] [Indexed: 04/08/2024] Open
Abstract
Canavan disease is an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. The disease is caused by a deficiency of the cytosolic aspartoacylase (ASPA) enzyme, which catalyzes the hydrolysis of N-acetyl-aspartate (NAA), an abundant brain metabolite, into aspartate and acetate. On the physiological level, the mechanism of pathogenicity remains somewhat obscure, with multiple, not mutually exclusive, suggested hypotheses. At the molecular level, recent studies have shown that most disease linked ASPA gene variants lead to a structural destabilization and subsequent proteasomal degradation of the ASPA protein variants, and accordingly Canavan disease should in general be considered a protein misfolding disorder. Here, we comprehensively summarize the molecular and cell biology of ASPA, with a particular focus on disease-linked gene variants and the pathophysiology of Canavan disease. We highlight the importance of high-throughput technologies and computational prediction tools for making genotype-phenotype predictions as we await the results of ongoing trials with gene therapy for Canavan disease.
Collapse
Affiliation(s)
- Martin Grønbæk-Thygesen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200N, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200N, Copenhagen, Denmark.
| |
Collapse
|
14
|
Dibyachintan S, Dube AK, Bradley D, Lemieux P, Dionne U, Landry CR. Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.23.581840. [PMID: 38464075 PMCID: PMC10925128 DOI: 10.1101/2024.02.23.581840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Paralogous genes are often redundant for long periods of time before they diverge in function. While their functions are preserved, paralogous proteins can accumulate mutations that, through epistasis, could impact their fate in the future. By quantifying the impact of all single-amino acid substitutions on the binding of two myosin proteins to their interaction partners, we find that the future evolution of these proteins is highly contingent on their regulatory divergence and the mutations that have silently accumulated in their protein binding domains. Differences in the promoter strength of the two paralogs amplify the impact of mutations on binding in the lowly expressed one. While some mutations would be sufficient to non-functionalize one paralog, they would have minimal impact on the other. Our results reveal how functionally equivalent protein domains could be destined to specific fates by regulatory and cryptic coding sequence changes that currently have little to no functional impact.
Collapse
Affiliation(s)
- Soham Dibyachintan
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Alexandre K Dube
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - David Bradley
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - Pascale Lemieux
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Ugo Dionne
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Current affiliation: Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Christian R Landry
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| |
Collapse
|
15
|
Wang X, Li A, Li X, Cui H. Empowering Protein Engineering through Recombination of Beneficial Substitutions. Chemistry 2024; 30:e202303889. [PMID: 38288640 DOI: 10.1002/chem.202303889] [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: 01/04/2024] [Indexed: 02/24/2024]
Abstract
Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.
Collapse
Affiliation(s)
- Xinyue Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Anni Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Xiujuan Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Haiyang Cui
- School of Life Sciences, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| |
Collapse
|
16
|
Saez-Matia A, Ibarluzea MG, M-Alicante S, Muguruza-Montero A, Nuñez E, Ramis R, Ballesteros OR, Lasa-Goicuria D, Fons C, Gallego M, Casis O, Leonardo A, Bergara A, Villarroel A. MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants. Int J Mol Sci 2024; 25:2910. [PMID: 38474157 DOI: 10.3390/ijms25052910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.
Collapse
Affiliation(s)
| | - Markel G Ibarluzea
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Sara M-Alicante
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | | | - Eider Nuñez
- Instituto Biofisika, CSIC-UPV/EHU, 48940 Leioa, Spain
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
| | - Rafael Ramis
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Oscar R Ballesteros
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
| | | | - Carmen Fons
- Pediatric Neurology Department, Sant Joan de Déu Hospital, Institut de Recerca Sant Joan de Déu, Barcelona University, 08950 Barcelona, Spain
| | - Mónica Gallego
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Oscar Casis
- Departamento de Fisiología, Universidad del País Vasco, UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Aritz Leonardo
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
| | - Aitor Bergara
- Physics Department, Universidad del País Vasco, UPV/EHU, 48940 Leioa, Spain
- Donostia International Physics Center, 20018 Donostia, Spain
- Centro de Física de Materiales CFM, CSIC-UPV/EHU, 20018 Donostia, Spain
| | | |
Collapse
|
17
|
Clausen L, Voutsinos V, Cagiada M, Johansson KE, Grønbæk-Thygesen M, Nariya S, Powell RL, Have MKN, Oestergaard VH, Stein A, Fowler DM, Lindorff-Larsen K, Hartmann-Petersen R. A mutational atlas for Parkin proteostasis. Nat Commun 2024; 15:1541. [PMID: 38378758 PMCID: PMC10879094 DOI: 10.1038/s41467-024-45829-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
Proteostasis can be disturbed by mutations affecting folding and stability of the encoded protein. An example is the ubiquitin ligase Parkin, where gene variants result in autosomal recessive Parkinsonism. To uncover the pathological mechanism and provide comprehensive genotype-phenotype information, variant abundance by massively parallel sequencing (VAMP-seq) is leveraged to quantify the abundance of Parkin variants in cultured human cells. The resulting mutational map, covering 9219 out of the 9300 possible single-site amino acid substitutions and nonsense Parkin variants, shows that most low abundance variants are proteasome targets and are located within the structured domains of the protein. Half of the known disease-linked variants are found at low abundance. Systematic mapping of degradation signals (degrons) reveals an exposed degron region proximal to the so-called "activation element". This work provides examples of how missense variants may cause degradation either via destabilization of the native protein, or by introducing local signals for degradation.
Collapse
Affiliation(s)
- Lene Clausen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Vasileios Voutsinos
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer E Johansson
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Snehal Nariya
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rachel L Powell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Magnus K N Have
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | | | - Amelie Stein
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
18
|
Andorf CM, Haley OC, Hayford RK, Portwood JL, Harding S, Sen S, Cannon EK, Gardiner JM, Kim HS, Woodhouse MR. PanEffect: a pan-genome visualization tool for variant effects in maize. Bioinformatics 2024; 40:btae073. [PMID: 38337024 PMCID: PMC10881103 DOI: 10.1093/bioinformatics/btae073] [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: 12/16/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024] Open
Abstract
SUMMARY Understanding the effects of genetic variants is crucial for accurately predicting traits and functional outcomes. Recent approaches have utilized artificial intelligence and protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 50 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to potential functional consequences. In addition, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. AVAILABILITY AND IMPLEMENTATION The PanEffect code is freely available on GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https://www.maizegdb.org/effect/maize/).
Collapse
Affiliation(s)
- Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
- Department of Computer Science, Iowa State University, Ames, IA 50011, United States
| | - Olivia C Haley
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Rita K Hayford
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - John L Portwood
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Stephen Harding
- USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States
| | - Shatabdi Sen
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, United States
| | - Ethalinda K Cannon
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| | - Jack M Gardiner
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, United States
| | - Hye-Seon Kim
- USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States
| | - Margaret R Woodhouse
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States
| |
Collapse
|
19
|
Fannjiang C, Listgarten J. Is Novelty Predictable? Cold Spring Harb Perspect Biol 2024; 16:a041469. [PMID: 38052497 PMCID: PMC10835614 DOI: 10.1101/cshperspect.a041469] [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: 12/07/2023]
Abstract
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.
Collapse
Affiliation(s)
- Clara Fannjiang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Jennifer Listgarten
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| |
Collapse
|
20
|
Nourbakhsh M, Degn K, Saksager A, Tiberti M, Papaleo E. Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. Brief Bioinform 2024; 25:bbad519. [PMID: 38261338 PMCID: PMC10805075 DOI: 10.1093/bib/bbad519] [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: 06/09/2023] [Revised: 11/27/2023] [Accepted: 12/11/2023] [Indexed: 01/24/2024] Open
Abstract
The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.
Collapse
Affiliation(s)
- Mona Nourbakhsh
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Astrid Saksager
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, Denmark
- Cancer Structural Biology, Danish Cancer Institute, 2100 Copenhagen, Denmark
| |
Collapse
|
21
|
Weissenow K, Rost B. Rendering protein mutation movies with MutAmore. BMC Bioinformatics 2023; 24:469. [PMID: 38087198 PMCID: PMC10714560 DOI: 10.1186/s12859-023-05610-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations. RESULTS We introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures. CONCLUSION MutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.
Collapse
Affiliation(s)
- Konstantin Weissenow
- Department of Informatics, Bioinformatics and Computational Biology i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and Its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology i12, TUM (Technical University of Munich), Boltzmannstr. 3, 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
| |
Collapse
|
22
|
Notin P, Kollasch AW, Ritter D, van Niekerk L, Paul S, Spinner H, Rollins N, Shaw A, Weitzman R, Frazer J, Dias M, Franceschi D, Orenbuch R, Gal Y, Marks DS. ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570727. [PMID: 38106144 PMCID: PMC10723403 DOI: 10.1101/2023.12.07.570727] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Ada Shaw
- Applied Mathematics, Harvard University
| | | | | | - Mafalda Dias
- Centre for Genomic Regulation, Universitat Pompeu Fabra
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
| | | |
Collapse
|
23
|
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.
Collapse
Affiliation(s)
| | | | | | - Yarin Gal
- Computer Science, University of Oxford
| |
Collapse
|
24
|
Abakarova M, Marquet C, Rera M, Rost B, Laine E. Alignment-based Protein Mutational Landscape Prediction: Doing More with Less. Genome Biol Evol 2023; 15:evad201. [PMID: 37936309 PMCID: PMC10653582 DOI: 10.1093/gbe/evad201] [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: 02/20/2023] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 11/09/2023] Open
Abstract
The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.
Collapse
Affiliation(s)
- Marina Abakarova
- CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université, UMR 7238, Paris 75005, France
- Université Paris Cité, INSERM UMR U1284, 75004 Paris, France
| | - Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748 Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Michael Rera
- Université Paris Cité, INSERM UMR U1284, 75004 Paris, France
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748 Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748 Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| | - Elodie Laine
- CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université, UMR 7238, Paris 75005, France
- Institut universitaire de France (IUF)
| |
Collapse
|
25
|
Bradley D, Hogrebe A, Dandage R, Dubé AK, Leutert M, Dionne U, Chang A, Villén J, Landry CR. The fitness cost of spurious phosphorylation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.08.561337. [PMID: 37873463 PMCID: PMC10592693 DOI: 10.1101/2023.10.08.561337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known, but quantifying this is required to understand the constraints faced by cell systems as they evolve. Here, we use the model organism S. cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, most of the resulting tyrosine phosphorylation is spurious. This provides a suitable system to measure the impact of artificial protein interactions on fitness. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3,500 proteins. Examination of the fitness costs in each strain revealed a strong correlation between the number of spurious pY sites and decreased growth. Moreover, the analysis of pY effects on protein structure and on protein function revealed over 1000 pY events that we predict to be deleterious. However, we also find that a large number of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with bona fide tyrosine kinases. Taken together, our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.
Collapse
Affiliation(s)
- David Bradley
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexander Hogrebe
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rohan Dandage
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Ugo Dionne
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexis Chang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Christian R Landry
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| |
Collapse
|
26
|
Posani L, Rizzato F, Monasson R, Cocco S. Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data. PLoS Comput Biol 2023; 19:e1011521. [PMID: 37883593 PMCID: PMC10645369 DOI: 10.1371/journal.pcbi.1011521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 11/14/2023] [Accepted: 09/15/2023] [Indexed: 10/28/2023] Open
Abstract
Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.
Collapse
Affiliation(s)
- Lorenzo Posani
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 & PSL Research, Sorbonne Université, Paris, France
| | - Francesca Rizzato
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 & PSL Research, Sorbonne Université, Paris, France
| | - Rémi Monasson
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 & PSL Research, Sorbonne Université, Paris, France
| | - Simona Cocco
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR8023 & PSL Research, Sorbonne Université, Paris, France
| |
Collapse
|
27
|
Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, Pritzel A, Wong LH, Zielinski M, Sargeant T, Schneider RG, Senior AW, Jumper J, Hassabis D, Kohli P, Avsec Ž. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 2023; 381:eadg7492. [PMID: 37733863 DOI: 10.1126/science.adg7492] [Citation(s) in RCA: 225] [Impact Index Per Article: 225.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
Collapse
|
28
|
Wang H, Zang Y, Kang Y, Zhang J, Zhang L, Zhang S. ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction. Brief Bioinform 2023; 24:bbad290. [PMID: 37598423 DOI: 10.1093/bib/bbad290] [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: 04/12/2023] [Revised: 06/21/2023] [Accepted: 07/26/2023] [Indexed: 08/22/2023] Open
Abstract
The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a growing body of supervised and unsupervised machine learning methods have been applied to this field, yielding fruitful results. Here, we propose a novel self-supervised model, called encoder-transformation layer-decoder (ETLD) architecture, capable of capturing protein sequence latent features directly from MSAs. Compared to the typical autoencoder model, ETLD introduces a transformation layer with the ability to learn inter-site couplings, which can be used to parse out the two-dimensional residue-residue contacts map after a simple mathematical derivation or an additional supervised neural network. ETLD retains the process of encoding and decoding sequences, and the predicted probabilities of amino acids at each site can be further used to construct the mutation landscapes for mutation effects prediction, outperforming advanced models such as GEMME, DeepSequence and EVmutation in general. Overall, ETLD is a highly interpretable unsupervised model with great potential for improvement and can be further combined with supervised methods for more extensive and accurate predictions.
Collapse
Affiliation(s)
- He Wang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Yongjian Zang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Ying Kang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Jianwen Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Lei Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Shengli Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| |
Collapse
|
29
|
Yee SW, Ferrández-Peral L, Alentorn P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Capra JA, Artursson P, Newman JW, Marques-Bonet T, Giacomini KM. Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates. RESEARCH SQUARE 2023:rs.3.rs-3263845. [PMID: 37790518 PMCID: PMC10543398 DOI: 10.21203/rs.3.rs-3263845/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17β-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.
Collapse
Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | | | - Pol Alentorn
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352 Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Niklas Handin
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Virginia M. Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, CA 94143, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States
| | - Colin M. Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - John W. Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA; Department of Nutrition, University of California, Davis, Davis, CA 95616, USA; UC Davis West Coast Metabolomics Center, Davis, CA 95616, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain; CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain; Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| |
Collapse
|
30
|
Yee SW, Ferrández-Peral L, Alentorn P, Fontsere C, Ceylan M, Koleske ML, Handin N, Artegoitia VM, Lara G, Chien HC, Zhou X, Dainat J, Zalevsky A, Sali A, Brand CM, Capra JA, Artursson P, Newman JW, Marques-Bonet T, Giacomini KM. Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552553. [PMID: 37609337 PMCID: PMC10441401 DOI: 10.1101/2023.08.08.552553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17β-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.
Collapse
Affiliation(s)
- Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | | | - Pol Alentorn
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain
| | - Claudia Fontsere
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Øster Farimagsgade 5A, 1352 Copenhagen, Denmark
| | - Merve Ceylan
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Megan L. Koleske
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Niklas Handin
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - Virginia M. Artegoitia
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA
| | - Giovanni Lara
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Jacques Dainat
- Joint Research Unit for Infectious Diseases and Vectors Ecology Genetics Evolution and Control (MIVEGEC), University of Montpellier, French National Center for Scientific Research (CNRS 5290), French National Research Institute for Sustainable Development (IRD 224), 911 Avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5, France
| | - Arthur Zalevsky
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, CA 94143, United States; Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, CA, United States
| | - Colin M. Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - John A. Capra
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Per Artursson
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, P.O. Box 580, 75123, Uppsala, Sweden
| | - John W. Newman
- United States Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA 95616, USA; Department of Nutrition, University of California, Davis, Davis, CA 95616, USA; UC Davis West Coast Metabolomics Center, Davis, CA 95616, USA
| | - Tomas Marques-Bonet
- Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain; Institute of Evolutionary Biology (UPF-CSIC), PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain; Catalan Institution of Research and Advanced Studies (ICREA), Passeig de Lluís Companys, 23, 08010, Barcelona, Spain; CNAG, Centro Nacional de Analisis Genomico, Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain; Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Edifici ICTA-ICP, c/ Columnes s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| |
Collapse
|
31
|
Jagota M, Ye C, Albors C, Rastogi R, Koehl A, Ioannidis N, Song YS. Cross-protein transfer learning substantially improves disease variant prediction. Genome Biol 2023; 24:182. [PMID: 37550700 PMCID: PMC10408151 DOI: 10.1186/s13059-023-03024-6] [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: 12/06/2022] [Accepted: 07/27/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. RESULTS We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. CONCLUSIONS Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.
Collapse
Affiliation(s)
- Milind Jagota
- Computer Science Division, University of California, Berkeley, 94720, CA, USA
| | - Chengzhong Ye
- Department of Statistics, University of California, Berkeley, 94720, CA, USA
| | - Carlos Albors
- Computer Science Division, University of California, Berkeley, 94720, CA, USA
| | - Ruchir Rastogi
- Computer Science Division, University of California, Berkeley, 94720, CA, USA
| | - Antoine Koehl
- Department of Statistics, University of California, Berkeley, 94720, CA, USA
| | - Nilah Ioannidis
- Computer Science Division, University of California, Berkeley, 94720, CA, USA
- Chan Zuckerberg Biohub, San Francisco, 94158, CA, USA
- Center for Computational Biology, University of California, Berkeley, 94720, CA, USA
| | - Yun S Song
- Computer Science Division, University of California, Berkeley, 94720, CA, USA.
- Department of Statistics, University of California, Berkeley, 94720, CA, USA.
- Center for Computational Biology, University of California, Berkeley, 94720, CA, USA.
| |
Collapse
|
32
|
Tsuboyama K, Dauparas J, Chen J, Laine E, Mohseni Behbahani Y, Weinstein JJ, Mangan NM, Ovchinnikov S, Rocklin GJ. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 2023; 620:434-444. [PMID: 37468638 PMCID: PMC10412457 DOI: 10.1038/s41586-023-06328-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/14/2023] [Indexed: 07/21/2023]
Abstract
Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.
Collapse
Affiliation(s)
- Kotaro Tsuboyama
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- PRESTO, Japan Science and Technology Agency, Tokyo, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Justas Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jonathan Chen
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Yasser Mohseni Behbahani
- Sorbonne Université, CNRS, IBPS, Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Paris, France
| | - Jonathan J Weinstein
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Niall M Mangan
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Gabriel J Rocklin
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Evanston, IL, USA.
| |
Collapse
|
33
|
Nagar N, Tubiana J, Loewenthal G, Wolfson HJ, Ben Tal N, Pupko T. EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning. J Mol Biol 2023; 435:168155. [PMID: 37356902 DOI: 10.1016/j.jmb.2023.168155] [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: 03/09/2023] [Revised: 05/13/2023] [Accepted: 05/17/2023] [Indexed: 06/27/2023]
Abstract
Multiple sequence alignments (MSAs) are the workhorse of molecular evolution and structural biology research. From MSAs, the amino acids that are tolerated at each site during protein evolution can be inferred. However, little is known regarding the repertoire of tolerated amino acids in proteins when only a few or no sequence homologs are available, such as orphan and de novo designed proteins. Here we present EvoRator2, a deep-learning algorithm trained on over 15,000 protein structures that can predict which amino acids are tolerated at any given site, based exclusively on protein structural information mined from atomic coordinate files. We show that EvoRator2 obtained satisfying results for the prediction of position-weighted scoring matrices (PSSM). We further show that EvoRator2 obtained near state-of-the-art performance on proteins with high quality structures in predicting the effect of mutations in deep mutation scanning (DMS) experiments and that for certain DMS targets, EvoRator2 outperformed state-of-the-art methods. We also show that by combining EvoRator2's predictions with those obtained by a state-of-the-art deep-learning method that accounts for the information in the MSA, the prediction of the effect of mutation in DMS experiments was improved in terms of both accuracy and stability. EvoRator2 is designed to predict which amino-acid substitutions are tolerated in such proteins without many homologous sequences, including orphan or de novo designed proteins. We implemented our approach in the EvoRator web server (https://evorator.tau.ac.il).
Collapse
Affiliation(s)
- Natan Nagar
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jérôme Tubiana
- Blavatnik School of Computer Science, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Gil Loewenthal
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Nir Ben Tal
- School of Neurobiology, Biochemistry & Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
| |
Collapse
|
34
|
Cagiada M, Bottaro S, Lindemose S, Schenstrøm SM, Stein A, Hartmann-Petersen R, Lindorff-Larsen K. Discovering functionally important sites in proteins. Nat Commun 2023; 14:4175. [PMID: 37443362 PMCID: PMC10345196 DOI: 10.1038/s41467-023-39909-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/02/2023] [Indexed: 07/15/2023] Open
Abstract
Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
Collapse
Affiliation(s)
- Matteo Cagiada
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sandro Bottaro
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Søren Lindemose
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Signe M Schenstrøm
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
35
|
Mohseni Behbahani Y, Laine E, Carbone A. Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation. Bioinformatics 2023; 39:i544-i552. [PMID: 37387162 DOI: 10.1093/bioinformatics/btad231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association. RESULTS In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex. AVAILABILITY AND IMPLEMENTATION Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.
Collapse
Affiliation(s)
- Yasser Mohseni Behbahani
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
| | - Elodie Laine
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
| | - Alessandra Carbone
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS, Paris 75005, France
| |
Collapse
|
36
|
Kampmeyer C, Grønbæk-Thygesen M, Oelerich N, Tatham MH, Cagiada M, Lindorff-Larsen K, Boomsma W, Hofmann K, Hartmann-Petersen R. Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components. Cell Mol Life Sci 2023; 80:143. [PMID: 37160462 PMCID: PMC10169902 DOI: 10.1007/s00018-023-04782-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/15/2023] [Accepted: 04/17/2023] [Indexed: 05/11/2023]
Abstract
In terms of its relative frequency, lysine is a common amino acid in the human proteome. However, by bioinformatics we find hundreds of proteins that contain long and evolutionarily conserved stretches completely devoid of lysine residues. These so-called lysine deserts show a high prevalence in intrinsically disordered proteins with known or predicted functions within the ubiquitin-proteasome system (UPS), including many E3 ubiquitin-protein ligases and UBL domain proteasome substrate shuttles, such as BAG6, RAD23A, UBQLN1 and UBQLN2. We show that introduction of lysine residues into the deserts leads to a striking increase in ubiquitylation of some of these proteins. In case of BAG6, we show that ubiquitylation is catalyzed by the E3 RNF126, while RAD23A is ubiquitylated by E6AP. Despite the elevated ubiquitylation, mutant RAD23A appears stable, but displays a partial loss of function phenotype in fission yeast. In case of UBQLN1 and BAG6, introducing lysine leads to a reduced abundance due to proteasomal degradation of the proteins. For UBQLN1 we show that arginine residues within the lysine depleted region are critical for its ability to form cytosolic speckles/inclusions. We propose that selective pressure to avoid lysine residues may be a common evolutionary mechanism to prevent unwarranted ubiquitylation and/or perhaps other lysine post-translational modifications. This may be particularly relevant for UPS components as they closely and frequently encounter the ubiquitylation machinery and are thus more susceptible to nonspecific ubiquitylation.
Collapse
Affiliation(s)
- Caroline Kampmeyer
- Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Martin Grønbæk-Thygesen
- Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Nicole Oelerich
- Institute for Genetics, University of Cologne, Cologne, Germany
| | - Michael H Tatham
- Centre for Gene Regulation and Expression, Sir James Black Centre, School of Life Sciences, University of Dundee, Dundee, UK
| | - Matteo Cagiada
- Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Wouter Boomsma
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
| | - Kay Hofmann
- Institute for Genetics, University of Cologne, Cologne, Germany.
| | - Rasmus Hartmann-Petersen
- Department of Biology, The Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
37
|
Gersing S, Cagiada M, Gebbia M, Gjesing AP, Coté AG, Seesankar G, Li R, Tabet D, Weile J, Stein A, Gloyn AL, Hansen T, Roth FP, Lindorff-Larsen K, Hartmann-Petersen R. A comprehensive map of human glucokinase variant activity. Genome Biol 2023; 24:97. [PMID: 37101203 PMCID: PMC10131484 DOI: 10.1186/s13059-023-02935-8] [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: 07/14/2022] [Accepted: 04/10/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. RESULT Here, we exploit a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlate with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants are concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. CONCLUSION Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.
Collapse
Affiliation(s)
- Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Marinella Gebbia
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
| | - Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Atina G Coté
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
| | - Gireesh Seesankar
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
| | - Roujia Li
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada
| | - Daniel Tabet
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada
| | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark
| | - Anna L Gloyn
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A8, Canada.
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, M5G 1X5, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, M5T 3A1, Canada.
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200, Copenhagen, Denmark.
| |
Collapse
|
38
|
Abildgaard AB, Nielsen SV, Bernstein I, Stein A, Lindorff-Larsen K, Hartmann-Petersen R. Lynch syndrome, molecular mechanisms and variant classification. Br J Cancer 2023; 128:726-734. [PMID: 36434153 PMCID: PMC9978028 DOI: 10.1038/s41416-022-02059-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.
Collapse
Affiliation(s)
- Amanda B Abildgaard
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sofie V Nielsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Inge Bernstein
- Department of Surgical Gastroenterology, Aalborg University Hospital, Aalborg, Denmark
- Institute of Clinical Medicine, Aalborg University Hospital, Aalborg University, Aalborg, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
39
|
Cisneros AF, Gagnon-Arsenault I, Dubé AK, Després PC, Kumar P, Lafontaine K, Pelletier JN, Landry CR. Epistasis between promoter activity and coding mutations shapes gene evolvability. SCIENCE ADVANCES 2023; 9:eadd9109. [PMID: 36735790 PMCID: PMC9897669 DOI: 10.1126/sciadv.add9109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/22/2022] [Indexed: 06/01/2023]
Abstract
The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.
Collapse
Affiliation(s)
- Angel F. Cisneros
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, G1V 0A6, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Centre de recherche sur les données massives, Université Laval, G1V 0A6, Québec, Canada
| | - Isabelle Gagnon-Arsenault
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, G1V 0A6, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Centre de recherche sur les données massives, Université Laval, G1V 0A6, Québec, Canada
- Département de biologie, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
| | - Alexandre K. Dubé
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, G1V 0A6, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Centre de recherche sur les données massives, Université Laval, G1V 0A6, Québec, Canada
- Département de biologie, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
| | - Philippe C. Després
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, G1V 0A6, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Centre de recherche sur les données massives, Université Laval, G1V 0A6, Québec, Canada
| | - Pradum Kumar
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India
| | - Kiana Lafontaine
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Département de biochimie et de médecine moléculaire, Faculté de médecine, Université de Montréal, H3C 3J7, Montréal, Canada
| | - Joelle N. Pelletier
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Département de biochimie et de médecine moléculaire, Faculté de médecine, Université de Montréal, H3C 3J7, Montréal, Canada
- Département de chimie, Faculté des arts et des sciences, Université de Montréal, H3C 3J7, Montréal, Canada
| | - Christian R. Landry
- Département de biochimie, de microbiologie et de bio-informatique, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, G1V 0A6, Québec, Canada
- PROTEO, Le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications des protéines, Université Laval, G1V 0A6, Québec, Canada
- Centre de recherche sur les données massives, Université Laval, G1V 0A6, Québec, Canada
- Département de biologie, Faculté des sciences et de génie, Université Laval, G1V 0A6, Québec, Canada
| |
Collapse
|
40
|
Yildirim A, Tekpinar M. Building Quantitative Bridges between Dynamics and Sequences of SARS-CoV-2 Main Protease and a Diverse Set of Thirty-Two Proteins. J Chem Inf Model 2023; 63:9-19. [PMID: 36513349 DOI: 10.1021/acs.jcim.2c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Proteases are major drug targets for many viral diseases. However, mutations can render several antiprotease drugs inefficient rapidly even though these mutations may not alter protein structures significantly. Understanding relations between quickly mutating residues, protease structures, and the dynamics of the proteases is crucial for designing potent drugs. Due to this reason, we studied relations between the evolutionary information on residues in the amino acid sequences and protein dynamics for SARS-CoV-2 main protease. More precisely, we analyzed three dynamical quantities (Schlitter entropy, root-mean-square fluctuations, and dynamical flexibility index) and their relation to the amino acid conservation extracted from multiple sequence alignments of the main protease. We showed that a quantifiable similarity can be built between a sequence-based quantity called Jensen-Shannon conservation and those three dynamical quantities. We validated this similarity for a diverse set of 32 different proteins, other than the SARS-CoV-2 main protease. We believe that establishing these kinds of quantitative bridges will have larger implications for all viral proteases as well as all proteins.
Collapse
Affiliation(s)
- Ahmet Yildirim
- Department of Biology, Siirt University, 56100Siirt, Turkey
| | - Mustafa Tekpinar
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Sorbonne University, 75005Paris, France
| |
Collapse
|
41
|
Tiemann JKS, Zschach H, Lindorff-Larsen K, Stein A. Interpreting the molecular mechanisms of disease variants in human transmembrane proteins. Biophys J 2023:S0006-3495(22)03941-8. [PMID: 36600598 DOI: 10.1016/j.bpj.2022.12.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/19/2022] [Accepted: 12/21/2022] [Indexed: 01/06/2023] Open
Abstract
Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. Here, we investigate missense variants in membrane proteins-key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the underlying molecular processes. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins, however, are widely understudied. Here, we interpret variant effects on a larger scale by performing structure-based estimations of changes in thermodynamic stability using a membrane-specific energy function and analyses of sequence conservation during evolution of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome.
Collapse
Affiliation(s)
- Johanna Katarina Sofie Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Henrike Zschach
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Amelie Stein
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
42
|
Olenyi T, Marquet C, Heinzinger M, Kröger B, Nikolova T, Bernhofer M, Sändig P, Schütze K, Littmann M, Mirdita M, Steinegger M, Dallago C, Rost B. LambdaPP: Fast and accessible protein-specific phenotype predictions. Protein Sci 2023; 32:e4524. [PMID: 36454227 PMCID: PMC9793974 DOI: 10.1002/pro.4524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022]
Abstract
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.
Collapse
Affiliation(s)
- Tobias Olenyi
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Céline Marquet
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Michael Heinzinger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Benjamin Kröger
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Tiha Nikolova
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Michael Bernhofer
- TUM Graduate SchoolCenter of Doctoral Studies in Informatics and its Applications (CeDoSIA)GarchingGermany
| | - Philip Sändig
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Konstantin Schütze
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Maria Littmann
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany
| | - Milot Mirdita
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea
| | - Martin Steinegger
- School of Biological SciencesSeoul National UniversitySeoulSouth Korea,Korea Artificial Intelligence InstituteSeoul National UniversitySeoulSouth Korea,Korea Institute of Molecular Biology and GeneticsSeoul National UniversitySeoulSouth Korea
| | - Christian Dallago
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,VantAINew YorkUSA
| | - Burkhard Rost
- TUM (Technical University of Munich) Department of InformaticsBioinformatics‐ & Computational Biology—i12GarchingGermany,Institute for Advanced Study (TUM‐IAS)Lichtenbergstr. 2a, 85748 Garching/Munich, Germany & TUM School of Life Sciences Weihenstephan (WZW)FreisingGermany
| |
Collapse
|
43
|
Fu Y, Bedő J, Papenfuss AT, Rubin AF. Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants. Gigascience 2022; 12:giad073. [PMID: 37721410 PMCID: PMC10506130 DOI: 10.1093/gigascience/giad073] [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: 02/14/2023] [Revised: 07/02/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors. RESULTS In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins. CONCLUSIONS We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.
Collapse
Affiliation(s)
- Yunfan Fu
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| | - Justin Bedő
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| | - Anthony T Papenfuss
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
- Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Alan F Rubin
- The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 1G Royal Pde, Parkville, Victoria 3052, Australia
- The University of Melbourne, Department of Medical Biology, Parkville, Victoria 3010, Australia
| |
Collapse
|
44
|
Gjesing AP, Engelbrechtsen L, Cathrine B Thuesen A, Have CT, Hollensted M, Grarup N, Linneberg A, Steen Nielsen J, Christensen LB, Thomsen RW, Johansson KE, Cagiada M, Gersing S, Hartmann-Petersen R, Lindorff-Larsen K, Vaag A, Sørensen HT, Brandslund I, Beck-Nielsen H, Pedersen O, Rungby J, Hansen T. 14-fold increased prevalence of rare glucokinase gene variant carriers in unselected Danish patients with newly diagnosed type 2 diabetes. Diabetes Res Clin Pract 2022; 194:110159. [PMID: 36400171 DOI: 10.1016/j.diabres.2022.110159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
AIMS Rare variants in the glucokinase gene (GCK) cause Maturity-Onset Diabetes of the Young (MODY2/GCK-MODY). We investigated the prevalence of GCK variants, phenotypic characteristics, micro- and macrovascular disease at baseline and follow-up, and treatment among individuals with and without pathogenic GCK variants. METHODS This is a cross-sectional study in a population-based cohort of 5,433 individuals without diabetes (Inter99 cohort) and in 2,855 patients with a new clinical diagnosis of type 2 diabetes (DD2 cohort) with sequencing of GCK. Phenotypic characteristics, presence of micro- and macrovascular disease and treatment information were available for patients in the DD2 cohort at baseline and after an average follow-up of 7.4 years. RESULTS Twenty-two carriers of potentially deleterious GCK variants were found among patients with type 2 diabetes compared to three among 5,433 nondiabetic individuals [OR = 14.1 (95 % CI 4.2; 47.0), p = 8.9*10-6]. Patients with type 2 diabetes carrying GCK variants had significantly lower waist circumference, hip circumference and BMI, compared to non-carriers. Three GCK variant carriers with diabetes had microvascular complications during follow-up. CONCLUSIONS Approximately 0.8% of Danish patients with newly diagnosed type 2 diabetes carry non-synonymous variants in GCK and resemble patients with GCK-MODY. Glucose-lowering treatment cessation should be considered in this subset of diabetes patients.
Collapse
Affiliation(s)
- Anette P Gjesing
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Gynecology and Obstetrics, Herlev Hospital, Denmark
| | - Anne Cathrine B Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Christian T Have
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Hollensted
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark; Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Steen Nielsen
- The Danish Centre for Strategic Research in Type 2 Diabetes (DD2), Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Lotte B Christensen
- Department of Clinical Epidemiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Reimar W Thomsen
- Department of Clinical Epidemiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kristoffer E Johansson
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen, Denmark
| | - Allan Vaag
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ivan Brandslund
- Department of Clinical Biochemistry, Hospital Lillebaelt, Vejle, Denmark; Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Henning Beck-Nielsen
- Diabetes Research Centre, Department of Endocrinology, Centre for Individualized Medicine in Arterial Diseases, Odense University Hospital, Odense, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jørgen Rungby
- The Danish Centre for Strategic Research in Type 2 Diabetes (DD2), Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark; Department of Endocrinology and Copenhagen Center for Translational Research, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| |
Collapse
|
45
|
Pacheco-Garcia JL, Cagiada M, Tienne-Matos K, Salido E, Lindorff-Larsen K, L. Pey A. Effect of naturally-occurring mutations on the stability and function of cancer-associated NQO1: Comparison of experiments and computation. Front Mol Biosci 2022; 9:1063620. [PMID: 36504709 PMCID: PMC9730889 DOI: 10.3389/fmolb.2022.1063620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
Recent advances in DNA sequencing technologies are revealing a large individual variability of the human genome. Our capacity to establish genotype-phenotype correlations in such large-scale is, however, limited. This task is particularly challenging due to the multifunctional nature of many proteins. Here we describe an extensive analysis of the stability and function of naturally-occurring variants (found in the COSMIC and gnomAD databases) of the cancer-associated human NAD(P)H:quinone oxidoreductase 1 (NQO1). First, we performed in silico saturation mutagenesis studies (>5,000 substitutions) aimed to identify regions in NQO1 important for stability and function. We then experimentally characterized twenty-two naturally-occurring variants in terms of protein levels during bacterial expression, solubility, thermal stability, and coenzyme binding. These studies showed a good overall correlation between experimental analysis and computational predictions; also the magnitude of the effects of the substitutions are similarly distributed in variants from the COSMIC and gnomAD databases. Outliers in these experimental-computational genotype-phenotype correlations remain, and we discuss these on the grounds and limitations of our approaches. Our work represents a further step to characterize the mutational landscape of NQO1 in the human genome and may help to improve high-throughput in silico tools for genotype-phenotype correlations in this multifunctional protein associated with disease.
Collapse
Affiliation(s)
| | - Matteo Cagiada
- Department of Biology, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Eduardo Salido
- Center for Rare Diseases (CIBERER), Hospital Universitario de Canarias, Universidad de la Laguna, La Laguna, TenerifeTenerife, Spain
| | - Kresten Lindorff-Larsen
- Department of Biology, Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, Copenhagen, Denmark
| | - Angel L. Pey
- Departamento de Química Física, Unidad de Excelencia en Química Aplicada a Biomedicina y Medioambiente e Instituto de Biotecnología, Universidad de Granada, Granada, Spain,*Correspondence: Angel L. Pey,
| |
Collapse
|
46
|
The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma. Cell Death Dis 2022; 13:872. [PMID: 36243772 PMCID: PMC9569343 DOI: 10.1038/s41419-022-05318-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 11/07/2022]
Abstract
Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.
Collapse
|
47
|
Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
Collapse
Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| |
Collapse
|
48
|
Marquet C, Heinzinger M, Olenyi T, Dallago C, Erckert K, Bernhofer M, Nechaev D, Rost B. Embeddings from protein language models predict conservation and variant effects. Hum Genet 2022; 141:1629-1647. [PMID: 34967936 PMCID: PMC8716573 DOI: 10.1007/s00439-021-02411-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/06/2021] [Indexed: 12/13/2022]
Abstract
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.
Collapse
Affiliation(s)
- Céline Marquet
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany.
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany.
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Tobias Olenyi
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Christian Dallago
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Kyra Erckert
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Michael Bernhofer
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Dmitrii Nechaev
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748, Garching, Germany
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology - i12, TUM-Technical University of Munich, Boltzmannstr. 3, Garching, 85748, Munich, Germany
- Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching, 85748, Munich, Germany
- TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| |
Collapse
|
49
|
Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes. Nat Commun 2022; 13:4030. [PMID: 35821377 PMCID: PMC9276797 DOI: 10.1038/s41467-022-31643-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 06/27/2022] [Indexed: 12/05/2022] Open
Abstract
Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term. We use computational approaches to accurately predict the polymorphisms segregating in a panel of 61,157 Escherichia coli genomes from the analysis of distant homologues. By comparing a context-aware Direct-Coupling Analysis modelling to a non-epistatic approach, we show that the genetic context strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. The study of more distant species suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions. Predicting the effects of mutations in a species is a major challenge in genetics. Here, the authors investigate protein sequence landscapes using diverged E. coli sequences, to predict tolerated mutations and capture interactions between mutations.
Collapse
|
50
|
Kuru N, Dereli O, Akkoyun E, Bircan A, Tastan O, Adebali O. PHACT: Phylogeny-aware computing of tolerance for missense mutations. Mol Biol Evol 2022; 39:6593375. [PMID: 35639618 PMCID: PMC9178230 DOI: 10.1093/molbev/msac114] [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] [Indexed: 11/12/2022] Open
Abstract
Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and loss of function in proteins. The use of multiple sequence alignment alone-without considering the evolutionary relationships among sequences-results in the redundant counting of evolutionarily related alteration events as if they were independent. Here we propose a new method, PHACT that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3023 proteins and 61662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved better predictive performance than did other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.
Collapse
Affiliation(s)
- Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Onur Dereli
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Emrah Akkoyun
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Aylin Bircan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| | - Ogun Adebali
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
| |
Collapse
|