1
|
Gooran N, Kopra K. Fluorescence-Based Protein Stability Monitoring-A Review. Int J Mol Sci 2024; 25:1764. [PMID: 38339045 PMCID: PMC10855643 DOI: 10.3390/ijms25031764] [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: 12/31/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Proteins are large biomolecules with a specific structure that is composed of one or more long amino acid chains. Correct protein structures are directly linked to their correct function, and many environmental factors can have either positive or negative effects on this structure. Thus, there is a clear need for methods enabling the study of proteins, their correct folding, and components affecting protein stability. There is a significant number of label-free methods to study protein stability. In this review, we provide a general overview of these methods, but the main focus is on fluorescence-based low-instrument and -expertise-demand techniques. Different aspects related to thermal shift assays (TSAs), also called differential scanning fluorimetry (DSF) or ThermoFluor, are introduced and compared to isothermal chemical denaturation (ICD). Finally, we discuss the challenges and comparative aspects related to these methods, as well as future opportunities and assay development directions.
Collapse
Affiliation(s)
| | - Kari Kopra
- Department of Chemistry, University of Turku, Henrikinkatu 2, 20500 Turku, Finland;
| |
Collapse
|
2
|
Beton JG, Cragnolini T, Kaleel M, Mulvaney T, Sweeney A, Topf M. Integrating model simulation tools and
cryo‐electron
microscopy. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Joseph George Beton
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Birkbeck and University College London London UK
| | - Manaz Kaleel
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB) Leibniz‐Institut für Virologie (LIV) Hamburg Germany
| |
Collapse
|
3
|
Malhotra S, Joseph AP, Thiyagalingam J, Topf M. Assessment of protein-protein interfaces in cryo-EM derived assemblies. Nat Commun 2021; 12:3399. [PMID: 34099703 PMCID: PMC8184972 DOI: 10.1038/s41467-021-23692-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/14/2021] [Indexed: 02/05/2023] Open
Abstract
Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.
Collapse
Affiliation(s)
- Sony Malhotra
- grid.4464.20000 0001 2161 2573Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London, UK ,grid.14467.30Scientific Computing Department, Science and Technology Facilities Council, Didcot, UK
| | - Agnel Praveen Joseph
- grid.14467.30Scientific Computing Department, Science and Technology Facilities Council, Didcot, UK
| | - Jeyan Thiyagalingam
- grid.14467.30Scientific Computing Department, Science and Technology Facilities Council, Didcot, UK
| | - Maya Topf
- grid.4464.20000 0001 2161 2573Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London, UK ,grid.13648.380000 0001 2180 3484Centre for Structural Systems Biology, Leibniz-Institut für Experimentelle Virologie and Universitätsklinikum Hamburg-Eppendorf (UKE), Hamburg, Germany
| |
Collapse
|
4
|
Furmanova K, Jurcik A, Kozlikova B, Hauser H, Byska J. Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:843-852. [PMID: 31425101 DOI: 10.1109/tvcg.2019.2934333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data - from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels. At each level, we offer a set of selection and filtering operations that enable the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.
Collapse
|
5
|
Alnabati E, Kihara D. Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules 2019; 25:molecules25010082. [PMID: 31878333 PMCID: PMC6982917 DOI: 10.3390/molecules25010082] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 12/20/2019] [Accepted: 12/20/2019] [Indexed: 01/16/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.
Collapse
Affiliation(s)
- Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- Correspondence:
| |
Collapse
|
6
|
Malhotra S, Träger S, Dal Peraro M, Topf M. Modelling structures in cryo-EM maps. Curr Opin Struct Biol 2019; 58:105-114. [PMID: 31394387 DOI: 10.1016/j.sbi.2019.05.024] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/23/2019] [Accepted: 05/25/2019] [Indexed: 12/20/2022]
Abstract
Recent advances in structure determination of sub-cellular structures using cryo-electron microscopy and tomography have enabled us to understand their architecture in a more detailed manner and gain insight into their function. The choice of approach to use for atomic model building, fitting, refinement and validation in the 3D map resulting from these experiments depends primarily on the resolution of the map and the prior information on the corresponding model. Here, we survey some of such methods and approaches and highlight their uses in specific recent examples.
Collapse
Affiliation(s)
- Sony Malhotra
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - Sylvain Träger
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Matteo Dal Peraro
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom.
| |
Collapse
|
7
|
Developments in integrative modeling with dynamical interfaces. Curr Opin Struct Biol 2019; 56:11-17. [DOI: 10.1016/j.sbi.2018.10.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 10/26/2018] [Accepted: 10/27/2018] [Indexed: 11/19/2022]
|
8
|
Computational approaches to macromolecular interactions in the cell. Curr Opin Struct Biol 2019; 55:59-65. [PMID: 30999240 DOI: 10.1016/j.sbi.2019.03.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 03/08/2019] [Indexed: 12/15/2022]
Abstract
Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.
Collapse
|
9
|
Ni D, Lu S, Zhang J. Emerging roles of allosteric modulators in the regulation of protein-protein interactions (PPIs): A new paradigm for PPI drug discovery. Med Res Rev 2019; 39:2314-2342. [PMID: 30957264 DOI: 10.1002/med.21585] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 03/12/2019] [Accepted: 03/24/2019] [Indexed: 12/26/2022]
Abstract
Protein-protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost "undruggable" for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.
Collapse
Affiliation(s)
- Duan Ni
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Shaoyong Lu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,Center for Single-Cell Omics, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| |
Collapse
|
10
|
Nussinov R, Tsai CJ, Shehu A, Jang H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019; 24:molecules24030637. [PMID: 30759724 PMCID: PMC6384756 DOI: 10.3390/molecules24030637] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/05/2019] [Accepted: 02/10/2019] [Indexed: 02/06/2023] Open
Abstract
Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| | - Amarda Shehu
- Departments of Computer Science, Department of Bioengineering, and School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| |
Collapse
|
11
|
Kuzu G, Keskin O, Nussinov R, Gursoy A. PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Corrigendum. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2018; 74:65-66. [PMID: 29372900 DOI: 10.1107/s2059798317017739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 12/12/2017] [Indexed: 05/29/2023]
Abstract
A revised Table 6 and Supporting Information are provided for the article by Kuzu et al. [(2016), Acta Cryst. D72, 1137-1148].
Collapse
Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ozlem Keskin
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, 34450 Istanbul, Turkey
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research Inc., National Cancer Institute, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Attila Gursoy
- Computer Engineering, Koc University, 34450 Istanbul, Turkey
| |
Collapse
|
12
|
Integrative modelling of cellular assemblies. Curr Opin Struct Biol 2017; 46:102-109. [PMID: 28735107 DOI: 10.1016/j.sbi.2017.07.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 07/01/2017] [Accepted: 07/04/2017] [Indexed: 02/06/2023]
Abstract
A wide variety of experimental techniques can be used for understanding the precise molecular mechanisms underlying the activities of cellular assemblies. The inherent limitations of a single experimental technique often requires integration of data from complementary approaches to gain sufficient insights into the assembly structure and function. Here, we review popular computational approaches for integrative modelling of cellular assemblies, including protein complexes and genomic assemblies. We provide recent examples of integrative models generated for such assemblies by different experimental techniques, especially including data from 3D electron microscopy (3D-EM) and chromosome conformation capture experiments, respectively. We highlight general concepts in integrative modelling and discuss the need for careful formulation and merging of different types of information.
Collapse
|
13
|
Computational modeling of protein assemblies. Curr Opin Struct Biol 2017; 44:179-189. [DOI: 10.1016/j.sbi.2017.04.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
|