1
|
Gillani M, Pollastri G. Protein subcellular localization prediction tools. Comput Struct Biotechnol J 2024; 23:1796-1807. [PMID: 38707539 PMCID: PMC11066471 DOI: 10.1016/j.csbj.2024.04.032] [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: 02/13/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.
Collapse
Affiliation(s)
- Maryam Gillani
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
| | - Gianluca Pollastri
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
| |
Collapse
|
2
|
Gonzales J, Kim I, Hwang W, Cho JH. Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of influenza A virus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595776. [PMID: 38826371 PMCID: PMC11142230 DOI: 10.1101/2024.05.24.595776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Viral proteins frequently mutate to evade or antagonize host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present the deep analysis for the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveal how the mutations rewired inter-residue communications which underlies long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects. Notably, these mutations also exhibited long-range epistatic effects. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, enhancing its affinity to p85β. Complementary MD simulations and graph-based network analysis uncover how these mutations rewire dynamic residue interaction networks, which underlies the long-range epistasis and allosteric effects on p85β-binding affinity. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provides insight into their role in the molecular evolution of NS1.
Collapse
|
3
|
Versini R, Sritharan S, Aykac Fas B, Tubiana T, Aimeur SZ, Henri J, Erard M, Nüsse O, Andreani J, Baaden M, Fuchs P, Galochkina T, Chatzigoulas A, Cournia Z, Santuz H, Sacquin-Mora S, Taly A. A Perspective on the Prospective Use of AI in Protein Structure Prediction. J Chem Inf Model 2024; 64:26-41. [PMID: 38124369 DOI: 10.1021/acs.jcim.3c01361] [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: 12/23/2023]
Abstract
AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.
Collapse
Affiliation(s)
- Raphaelle Versini
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sujith Sritharan
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Burcu Aykac Fas
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Thibault Tubiana
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Sana Zineb Aimeur
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Julien Henri
- Sorbonne Université, CNRS, Laboratoire de Biologie, Computationnelle et Quantitative UMR 7238, Institut de Biologie Paris-Seine, 4 Place Jussieu, F-75005 Paris, France
| | - Marie Erard
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Oliver Nüsse
- Université Paris-Saclay, CNRS, Institut de Chimie Physique, 91405 Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Patrick Fuchs
- Sorbonne Université, École Normale Supérieure, PSL University, CNRS, Laboratoire des Biomolécules, LBM, 75005 Paris, France
- Université de Paris, UFR Sciences du Vivant, 75013 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Hubert Santuz
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Sophie Sacquin-Mora
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| | - Antoine Taly
- Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, F-75005 Paris, France
| |
Collapse
|
4
|
Aspromonte MC, Nugnes MV, Quaglia F, Bouharoua A, Tosatto SCE, Piovesan D. DisProt in 2024: improving function annotation of intrinsically disordered proteins. Nucleic Acids Res 2024; 52:D434-D441. [PMID: 37904585 PMCID: PMC10767923 DOI: 10.1093/nar/gkad928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 11/01/2023] Open
Abstract
DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt's curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.
Collapse
Affiliation(s)
| | | | - Federica Quaglia
- Department of Biomedical Sciences, University of Padova, Padova, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy
| | - Adel Bouharoua
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| |
Collapse
|
5
|
Xiao N, Yang W, Wang J, Li J, Zhao R, Li M, Li C, Liu K, Li Y, Yin C, Chen Z, Li X, Jiang Y. Protein structuromics: A new method for protein structure-function crosstalk in glioma. Proteins 2024; 92:24-36. [PMID: 37497743 DOI: 10.1002/prot.26555] [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: 03/25/2023] [Revised: 06/16/2023] [Accepted: 07/04/2023] [Indexed: 07/28/2023]
Abstract
Glioma is a type of tumor that starts in the glial cells of the brain or spine. Since the 1800s, when the disease was first named, its survival rates have always been unsatisfactory. Despite great advances in molecular biology and traditional treatment methods, many questions regarding cancer occurrence and the underlying mechanism remain to be answered. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. All of these results directly indicate that unfavorable group proteins show more complex structures than favorable group proteins. As the tumor cell microenvironment changes, the balance of oncogene-related and anti-oncogene-related proteins is disrupted, and most of the structures of the two groups of proteins will be disrupted. However, more unfavorable group proteins will maintain and refold to achieve their correct shape faster and perform their functions more quickly than favorable group proteins, and the former thus support cancer development. We hope that these analyses will help promote mechanistic research and the development of new treatments for glioma.
Collapse
Affiliation(s)
- Nan Xiao
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Wenming Yang
- Department of Neurosurgery, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Jin Wang
- Department of Rehabilitation, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Jiarong Li
- Department of Rehabilitation, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Ruoxuan Zhao
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Muzheng Li
- Department of Rehabilitation, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Chi Li
- Department of Anesthesiology, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Kang Liu
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Yingxin Li
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Chaoqun Yin
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Zhibo Chen
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Xingqi Li
- Department of Medicine, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| | - Yun Jiang
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou, Liaoning, China
| |
Collapse
|
6
|
Efraimidis E, Krokidis MG, Exarchos TP, Lazar T, Vlamos P. In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease. Int J Mol Sci 2023; 24:13543. [PMID: 37686347 PMCID: PMC10487466 DOI: 10.3390/ijms241713543] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/26/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.
Collapse
Affiliation(s)
- Evangelos Efraimidis
- Bioinformatics and Neuroinformatics MSc Program, Hellenic Open University, 26335 Patras, Greece;
| | - Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
| | - Tamas Lazar
- VIB–VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), B1050 Brussels, Belgium;
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel, B1050 Brussels, Belgium
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (T.P.E.)
| |
Collapse
|
7
|
Xiao N, Ma H, Gao H, Yang J, Tong D, Gan D, Yang J, Li C, Liu K, Li Y, Chen Z, Yin C, Li X, Wang H. Structure-function crosstalk in liver cancer research: Protein structuromics. Int J Biol Macromol 2023:125291. [PMID: 37315670 DOI: 10.1016/j.ijbiomac.2023.125291] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/04/2023] [Accepted: 06/07/2023] [Indexed: 06/16/2023]
Abstract
Liver cancer can be primary (starting in the liver) or secondary (cancer that has spread from elsewhere to the liver, known as liver metastasis). Liver metastasis is more common than primary liver cancer. Despite great advances in molecular biology methods and treatments, liver cancer is still associated with a poor survival rate and a high death rate, and there is no cure. Many questions remain regarding the mechanisms of liver cancer occurrence and development as well as tumor reoccurrence after treatment. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. Our aim was to provide new insights that may inform research on the development and treatment of liver cancer.
Collapse
Affiliation(s)
- Nan Xiao
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China.
| | - Hongming Ma
- Department of Oncology, China Emergency General Hospital City, Beijing, China
| | - Hong Gao
- Department of Oncology, China Emergency General Hospital City, Beijing, China
| | - Jing Yang
- Department of Computer Center, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Dan Tong
- Department of Nurse, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Dingzhu Gan
- Department of Publicity, Peking Union Medical College, Beijing, China
| | - Jinhua Yang
- Department of Development and Production, Institute of Medical Biology, Peking Union Medical College, Kunming City, Yunnan Province, China
| | - Chi Li
- Department of Anesthesiology, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Kang Liu
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Yingxin Li
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Zhibo Chen
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Chaoqun Yin
- Department of Medical Science, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Xingqi Li
- Department of Medicine, Medical College of Jinzhou Medical University, Jinzhou City, Liaoning Province, China
| | - Hongwu Wang
- Department of Respiratory and Critical Care Medicine, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| |
Collapse
|