1
|
Peng CX, Liang F, Xia YH, Zhao KL, Hou MH, Zhang GJ. Recent Advances and Challenges in Protein Structure Prediction. J Chem Inf Model 2024; 64:76-95. [PMID: 38109487 DOI: 10.1021/acs.jcim.3c01324] [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] [Indexed: 12/20/2023]
Abstract
Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.
Collapse
Affiliation(s)
- Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fang Liang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yu-Hao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ming-Hua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| |
Collapse
|
2
|
Vymětal J, Mertová K, Boušová K, Šulc J, Tripsianes K, Vondrasek J. Fusion of two unrelated protein domains in a chimera protein and its 3D prediction: Justification of the x-ray reference structures as a prediction benchmark. Proteins 2022; 90:2067-2079. [PMID: 35833233 PMCID: PMC9796088 DOI: 10.1002/prot.26398] [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: 08/09/2021] [Revised: 05/20/2022] [Accepted: 07/08/2022] [Indexed: 12/30/2022]
Abstract
Proteins are naturally formed by domains edging their functional and structural properties. A domain out of the context of an entire protein can retain its structure and to some extent also function on its own. These properties rationalize construction of artificial fusion multidomain proteins with unique combination of various functions. Information on the specific functional and structural characteristics of individual domains in the context of new artificial fusion proteins is inevitably encoded in sequential order of composing domains defining their mutual spatial positions. So the challenges in designing new proteins with new domain combinations lie dominantly in structure/function prediction and its context dependency. Despite the enormous body of publications on artificial fusion proteins, the task of their structure/function prediction is complex and nontrivial. The degree of spatial freedom facilitated by a linker between domains and their mutual orientation driven by noncovalent interactions is beyond a simple and straightforward methodology to predict their structure with reasonable accuracy. In the presented manuscript, we tested methodology using available modeling tools and computational methods. We show that the process and methodology of such prediction are not straightforward and must be done with care even when recently introduced AlphaFold II is used. We also addressed a question of benchmarking standards for prediction of multidomain protein structures-x-ray or Nuclear Magnetic Resonance experiments. On the study of six two-domain protein chimeras as well as their composing domains and their x-ray structures selected from PDB, we conclude that the major obstacle for justified prediction is inappropriate sampling of the conformational space by the explored methods. On the other hands, we can still address particular steps of the methodology and improve the process of chimera proteins prediction.
Collapse
Affiliation(s)
- Jiří Vymětal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
| | - Kateřina Mertová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic,Faculty of Natural SciencesCharles UniversityPraha 2Czech Republic
| | - Kristýna Boušová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
| | - Josef Šulc
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic,Faculty of Natural SciencesCharles UniversityPraha 2Czech Republic
| | | | - Jiri Vondrasek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
| |
Collapse
|
3
|
Sun Y, Li X, Chen R, Liu F, Wei S. Recent advances in structural characterization of biomacromolecules in foods via small-angle X-ray scattering. Front Nutr 2022; 9:1039762. [PMID: 36466419 PMCID: PMC9714470 DOI: 10.3389/fnut.2022.1039762] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/03/2022] [Indexed: 08/04/2023] Open
Abstract
Small-angle X-ray scattering (SAXS) is a method for examining the solution structure, oligomeric state, conformational changes, and flexibility of biomacromolecules at a scale ranging from a few Angstroms to hundreds of nanometers. Wide time scales ranging from real time (milliseconds) to minutes can be also covered by SAXS. With many advantages, SAXS has been extensively used, it is widely used in the structural characterization of biomacromolecules in food science and technology. However, the application of SAXS in charactering the structure of food biomacromolecules has not been reviewed so far. In the current review, the principle, theoretical calculations and modeling programs are summarized, technical advances in the experimental setups and corresponding applications of in situ capabilities: combination of chromatography, time-resolved, temperature, pressure, flow-through are elaborated. Recent applications of SAXS for monitoring structural properties of biomacromolecules in food including protein, carbohydrate and lipid are also highlighted, and limitations and prospects for developing SAXS based on facility upgraded and artificial intelligence to study the structural properties of biomacromolecules are finally discussed. Future research should focus on extending machine time, simplifying SAXS data treatment, optimizing modeling methods in order to achieve an integrated structural biology based on SAXS as a practical tool for investigating the structure-function relationship of biomacromolecules in food industry.
Collapse
Affiliation(s)
- Yang Sun
- College of Vocational and Technical Education, Yunnan Normal University, Kunming, China
| | - Xiujuan Li
- Pharmaceutical Department, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China
| | - Ruixin Chen
- College of Vocational and Technical Education, Yunnan Normal University, Kunming, China
| | - Fei Liu
- College of Vocational and Technical Education, Yunnan Normal University, Kunming, China
| | - Song Wei
- Tumor Precise Intervention and Translational Medicine Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China
| |
Collapse
|
4
|
Yadav RK, Krishnan V. New structural insights into the
PI
‐2 pilus from
Streptococcus oralis
, an early dental plaque colonizer. FEBS J 2022; 289:6342-6366. [DOI: 10.1111/febs.16527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/20/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Rajnesh Kumari Yadav
- Laboratory of Structural Microbiology, Regional Centre for Biotechnology NCR Biotech Science Cluster Faridabad India
- School of Biotechnology KIIT University Odisha India
| | - Vengadesan Krishnan
- Laboratory of Structural Microbiology, Regional Centre for Biotechnology NCR Biotech Science Cluster Faridabad India
| |
Collapse
|
5
|
Kursula P. Small-angle X-ray scattering for the proteomics community: current overview and future potential. Expert Rev Proteomics 2021; 18:415-422. [PMID: 34210208 DOI: 10.1080/14789450.2021.1951242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Proteins are biological nanoparticles. For structural proteomics and hybrid structural biology, complementary methods are required that allow both high throughput and accurate automated data analysis. Small-angle X-ray scattering (SAXS) is a method for observing the size and shape of particles, such as proteins and complexes, in solution. SAXS data can be used to model both the structure, oligomeric state, conformational changes, and flexibility of biomolecular samples.Areas covered: The key principles of SAXS, its sample requirements, and its current and future applications for structural proteomics are briefly reviewed. Recent technical developments in SAXS experiments are discussed, and future potential of the method in structural proteomics is evaluated.Expert opinion: SAXS is a method suitable for several aspects of integrative structural proteomics, with current technical developments allowing for higher throughput and time-resolved studies, as well as the analysis of complex samples, such as membrane proteins. Increasing automation and streamlined data analysis are expected to equip SAXS for structure-based screening workflows. Originally, structural genomics had a heavy focus on folded, crystallizable proteins and complexes - SAXS is a method allowing an expansion of this focus to flexible and disordered systems.
Collapse
Affiliation(s)
- Petri Kursula
- Department of Biomedicine, University of Bergen, Bergen, Norway.,Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| |
Collapse
|
6
|
Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
Collapse
Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| |
Collapse
|
7
|
Valle J, Fang X, Lasa I. Revisiting Bap Multidomain Protein: More Than Sticking Bacteria Together. Front Microbiol 2020; 11:613581. [PMID: 33424817 PMCID: PMC7785521 DOI: 10.3389/fmicb.2020.613581] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/03/2020] [Indexed: 12/21/2022] Open
Abstract
One of the major components of the staphylococcal biofilm is surface proteins that assemble as scaffold components of the biofilm matrix. Among the different surface proteins able to contribute to biofilm formation, this review is dedicated to the Biofilm Associated Protein (Bap). Bap is part of the accessory genome of Staphylococcus aureus but orthologs of Bap in other staphylococcal species belong to the core genome. When present, Bap promotes adhesion to abiotic surfaces and induces strong intercellular adhesion by self-assembling into amyloid like aggregates in response to the levels of calcium and the pH in the environment. During infection, Bap enhances the adhesion to epithelial cells where it binds directly to the host receptor Gp96 and inhibits the entry of the bacteria into the cells. To perform such diverse range of functions, Bap comprises several domains, and some of them include several motifs associated to distinct functions. Based on the knowledge accumulated with the Bap protein of S. aureus, this review aims to summarize the current knowledge of the structure and properties of each domain of Bap and their contribution to Bap functionality.
Collapse
Affiliation(s)
- Jaione Valle
- Instituto de Agrobiotecnología, CSIC-Gobierno de Navarra, Mutilva, Spain
| | - Xianyang Fang
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Iñigo Lasa
- Laboratory of Microbial Pathogenesis, Navarrabiomed-Universidad Pública de Navarra-Departamento de Salud, IDISNA, Pamplona, Spain
| |
Collapse
|
8
|
Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020; 18:3494-3506. [PMID: 33304450 PMCID: PMC7695898 DOI: 10.1016/j.csbj.2020.11.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
Collapse
Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| |
Collapse
|