1
|
Amisaki T. Multilevel superposition for deciphering the conformational variability of protein ensembles. Brief Bioinform 2024; 25:bbae137. [PMID: 38557679 PMCID: PMC10983786 DOI: 10.1093/bib/bbae137] [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: 09/26/2023] [Revised: 02/14/2024] [Accepted: 03/10/2024] [Indexed: 04/04/2024] Open
Abstract
The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.
Collapse
Affiliation(s)
- Takashi Amisaki
- Department of Biological Regulation, Faculty of Medicine, Tottori University, Yonago, Tottori 683-8503, Japan
| |
Collapse
|
2
|
Ezanno P, Picault S, Bareille S, Beaunée G, Boender GJ, Dankwa EA, Deslandes F, Donnelly CA, Hagenaars TJ, Hayes S, Jori F, Lambert S, Mancini M, Munoz F, Pleydell DRJ, Thompson RN, Vergu E, Vignes M, Vergne T. The African swine fever modelling challenge: Model comparison and lessons learnt. Epidemics 2022; 40:100615. [PMID: 35970067 DOI: 10.1016/j.epidem.2022.100615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/26/2022] Open
Abstract
Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.
Collapse
Affiliation(s)
| | | | - Servane Bareille
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | | | | | | | | | - Christl A Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom; Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | | | - Sarah Hayes
- Department of Infectious Disease Epidemiology, Faculty of Medicine, School of Public Health, Imperial College London, United Kingdom
| | - Ferran Jori
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Sébastien Lambert
- Centre for Emerging, Endemic and Exotic Diseases, Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, United Kingdom
| | - Matthieu Mancini
- INRAE, Oniris, BIOEPAR, 44300 Nantes, France; INRAE, ENVT, IHAP, Toulouse, France
| | - Facundo Munoz
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - David R J Pleydell
- CIRAD, INRAE, Université de Montpellier, ASTRE, 34398 Montpellier, France
| | - Robin N Thompson
- Mathematics Institute and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Elisabeta Vergu
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Matthieu Vignes
- School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand
| | | |
Collapse
|
4
|
Vistoli G, Pedretti A, Mazzolari A, Testa B. Approaching Pharmacological Space: Events and Components. Methods Mol Biol 2018; 1800:245-274. [PMID: 29934897 DOI: 10.1007/978-1-4939-7899-1_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.
Collapse
Affiliation(s)
- Giulio Vistoli
- Dipartimento di Scienze Farmaceutiche Università degli Studi di Milano, Milan, Italy.
| | - Alessandro Pedretti
- Dipartimento di Scienze Farmaceutiche Università degli Studi di Milano, Milan, Italy
| | - Angelica Mazzolari
- Dipartimento di Scienze Farmaceutiche Università degli Studi di Milano, Milan, Italy
| | | |
Collapse
|
5
|
Thangappan J, Madan B, Wu S, Lee SG. Measuring the Conformational Distance of GPCR-related Proteins Using a Joint-based Descriptor. Sci Rep 2017; 7:15205. [PMID: 29123217 PMCID: PMC5680341 DOI: 10.1038/s41598-017-15513-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 10/27/2017] [Indexed: 01/19/2023] Open
Abstract
Joint-based descriptor is a new level of macroscopic descriptor for protein structure using joints of secondary structures as a basic element. Here, we propose how the joint-based descriptor can be applied to examine the conformational distances or differences of transmembrane (TM) proteins. Specifically, we performed three independent studies that measured the global and conformational distances between GPCR A family and its related structures. First, the conformational distances of GPCR A family and other 7TM proteins were evaluated. This provided the information on the distant and close families or superfamilies to GPCR A family and permitted the identification of conserved local conformations. Second, computational models of GPCR A family proteins were validated, which enabled us to estimate how much they reproduce the native conformation of GPCR A proteins at global and local conformational level. Finally, the conformational distances between active and inactive states of GPCR proteins were estimated, which identified the difference of local conformation. The proposed macroscopic joint-based approach is expected to allow us to investigate structural features, evolutionary relationships, computational models and conformational changes of TM proteins in a more simplistic manner.
Collapse
Affiliation(s)
- Jayaraman Thangappan
- Department of Chemical Engineering, Pusan National University, Busan, 609-735, Republic of Korea
| | - Bharat Madan
- Department of Chemical Engineering, Pusan National University, Busan, 609-735, Republic of Korea
| | - Sangwook Wu
- Department of Physics, Pukyong National University, Busan, 608-737, Republic of Korea.
| | - Sun-Gu Lee
- Department of Chemical Engineering, Pusan National University, Busan, 609-735, Republic of Korea.
| |
Collapse
|
6
|
Dudola D, Kovács B, Gáspári Z. CoNSEnsX+ Webserver for the Analysis of Protein Structural Ensembles Reflecting Experimentally Determined Internal Dynamics. J Chem Inf Model 2017; 57:1728-1734. [DOI: 10.1021/acs.jcim.7b00066] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Dániel Dudola
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Práter u. 50/A, Hungary
| | - Bertalan Kovács
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Práter u. 50/A, Hungary
| | - Zoltán Gáspári
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Práter u. 50/A, Hungary
| |
Collapse
|