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Procopio A, Cesarelli G, Donisi L, Merola A, Amato F, Cosentino C. Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107681. [PMID: 37385142 DOI: 10.1016/j.cmpb.2023.107681] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/14/2023] [Accepted: 06/14/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND AND OBJECTIVE Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements in modern technologies and the large availability of omics data allowed the application of Machine Learning (ML) techniques to different research fields, including systems biology. However, the availability of information regarding the analyzed biological context, sufficient experimental data, as well as the degree of computational complexity, represent some of the issues that both MMs and ML techniques could present individually. For this reason, recently, several studies suggest overcoming or significantly reducing these drawbacks by combining the above-mentioned two methods. In the wake of the growing interest in this hybrid analysis approach, with the present review, we want to systematically investigate the studies available in the scientific literature in which both MMs and ML have been combined to explain biological processes at genomics, proteomics, and metabolomics levels, or the behavior of entire cellular populations. METHODS Elsevier Scopus®, Clarivate Web of Science™ and National Library of Medicine PubMed® databases were enquired using the queries reported in Table 1, resulting in 350 scientific articles. RESULTS Only 14 of the 350 documents returned by the comprehensive search conducted on the three major online databases met our search criteria, i.e. present a hybrid approach consisting of the synergistic combination of MMs and ML to treat a particular aspect of systems biology. CONCLUSIONS Despite the recent interest in this methodology, from a careful analysis of the selected papers, it emerged how examples of integration between MMs and ML are already present in systems biology, highlighting the great potential of this hybrid approach to both at micro and macro biological scales.
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Affiliation(s)
- Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia
| | - Giuseppe Cesarelli
- Department of Electrical Engineering and Information Technology, Università degli Studi di Napoli Federico II, Napoli, 80125, Italy
| | - Leandro Donisi
- Department of Advanced Medical and Surgical Sciences, Università della Campania Luigi Vanvitelli, Napoli, 80138, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology, Università degli Studi di Napoli Federico II, Napoli, 80125, Italy.
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia.
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2
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Mondal A, Lenz S, MacCallum JL, Perez A. Hybrid computational methods combining experimental information with molecular dynamics. Curr Opin Struct Biol 2023; 81:102609. [PMID: 37224642 DOI: 10.1016/j.sbi.2023.102609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023]
Abstract
A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.
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Affiliation(s)
- Arup Mondal
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK. https://twitter.com/@amondal_chem
| | - Stefan Lenz
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada. https://twitter.com/@jlmaccal
| | - Alberto Perez
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK.
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3
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Kato S, Takada S, Fuchigami S. Particle Smoother to Assimilate Asynchronous Movie Data of High-Speed AFM with MD Simulations. J Chem Theory Comput 2023. [PMID: 37097918 DOI: 10.1021/acs.jctc.2c01268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
High-speed (HS) atomic force microscopy (AFM) can be used to observe structural dynamics of biomolecules under near-physiological conditions. In the AFM measurement, the probe tip scans an area of interest and acquires height data pixel by pixel so that the obtained AFM image contains a measurement time difference. In this study, to integrate molecular dynamics simulations with asynchronous HS-AFM movie data, we developed a particle smoother (PS) method for Bayesian data assimilation, one of the machine learning approaches, by extending the previous particle filter method. With a twin experiment with an asynchronous pseudo HS-AFM movie of a nucleosome, we found that the PS method with the pixel-by-pixel data acquisition reproduced the dynamic behavior of a nucleosome better than the previous particle filter method that ignored the data asynchronicity. We examined several frequencies of particle resampling in the PS method, and found that resampling once per one frame was optimal for reproducing the dynamic behavior. Thus, we found that the PS method with an appropriate resampling frequency is a powerful method for estimating the dynamic behavior of a target molecule from HS-AFM data with low spatiotemporal resolution.
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Affiliation(s)
- Suguru Kato
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Sotaro Fuchigami
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
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4
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Verkhivker G, Alshahrani M, Gupta G, Xiao S, Tao P. From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches. Int J Mol Sci 2023; 24:7747. [PMID: 37175454 PMCID: PMC10178073 DOI: 10.3390/ijms24097747] [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: 04/06/2023] [Revised: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
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5
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Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci 2023; 48:375-390. [PMID: 36564251 PMCID: PMC10023316 DOI: 10.1016/j.tibs.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
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6
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Donkor ED, Laio A, Hassanali A. Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water. J Chem Theory Comput 2023. [PMID: 36920997 DOI: 10.1021/acs.jctc.2c01205] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context involves encoding the information on chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface. By using a statistical test aimed at assessing the relative information content of different distance measures defined on the same data space, we investigate if these descriptors provide the same information as some of the common order parameters that are used to characterize local water structure such as hydrogen bonding, density, or tetrahedrality to name a few. Our analysis suggests that the ML description and the standard order parameters of the local water structure are not equivalent. In particular, a combination of these order parameters probing local water environments can predict SOAP similarity only approximately, and vice versa, the environments that are similar according to SOAP are not necessarily similar according to the standard order parameters. We also elucidate the role of some of the metaparameters in the SOAP definition in encoding chemical information.
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Affiliation(s)
- Edward Danquah Donkor
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy.,Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
| | - Alessandro Laio
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
| | - Ali Hassanali
- The Abdus Salam International Center for Theoretical Physics (ICTP), Strada Costiera 11, 34151 Trieste, Italy
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7
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Agajanian S, Alshahrani M, Bai F, Tao P, Verkhivker GM. Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches. J Chem Inf Model 2023; 63:1413-1428. [PMID: 36827465 PMCID: PMC11162550 DOI: 10.1021/acs.jcim.2c01634] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
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Affiliation(s)
- Steve Agajanian
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology and Information Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, United States
| | - Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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8
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Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images. PLoS Comput Biol 2022; 18:e1010384. [PMID: 36580448 PMCID: PMC9833559 DOI: 10.1371/journal.pcbi.1010384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/11/2023] [Accepted: 12/20/2022] [Indexed: 12/30/2022] Open
Abstract
High-speed atomic force microscopy (HS-AFM) is a powerful technique for capturing the time-resolved behavior of biomolecules. However, structural information in HS-AFM images is limited to the surface geometry of a sample molecule. Inferring latent three-dimensional structures from the surface geometry is thus important for getting more insights into conformational dynamics of a target biomolecule. Existing methods for estimating the structures are based on the rigid-body fitting of candidate structures to each frame of HS-AFM images. Here, we extend the existing frame-by-frame rigid-body fitting analysis to multiple frames to exploit orientational correlations of a sample molecule between adjacent frames in HS-AFM data due to the interaction with the stage. In the method, we treat HS-AFM data as time-series data, and they are analyzed with the hidden Markov modeling. Using simulated HS-AFM images of the taste receptor type 1 as a test case, the proposed method shows a more robust estimation of molecular orientations than the frame-by-frame analysis. The method is applicable in integrative modeling of conformational dynamics using HS-AFM data.
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9
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Huang Y, Darr CM, Gangopadhyay K, Gangopadhyay S, Bok S, Chakraborty S. Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis. PLoS One 2022; 17:e0275658. [PMID: 36282804 PMCID: PMC9595565 DOI: 10.1371/journal.pone.0275658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
Background Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. Methods and findings A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity. Conclusions The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.
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Affiliation(s)
- Yilun Huang
- Department of Statistics, University of Missouri, Columbia, Missouri, United States of America
| | - Charles M. Darr
- Department of Electrical Engineering & Computer Science, Center for Nano/Micro Systems & Nanotechnology, University of Missouri, Columbia, Missouri, United States of America
| | - Keshab Gangopadhyay
- Department of Electrical Engineering & Computer Science, Center for Nano/Micro Systems & Nanotechnology, University of Missouri, Columbia, Missouri, United States of America
| | - Shubhra Gangopadhyay
- Department of Electrical Engineering & Computer Science, Center for Nano/Micro Systems & Nanotechnology, University of Missouri, Columbia, Missouri, United States of America
- * E-mail: (SG); (SB); (SC)
| | - Sangho Bok
- Department of Electrical Engineering & Computer Science, Center for Nano/Micro Systems & Nanotechnology, University of Missouri, Columbia, Missouri, United States of America
- Department of Electrical & Computer Engineering, University of Denver, Denver, Colorado, United States of America
- * E-mail: (SG); (SB); (SC)
| | - Sounak Chakraborty
- Department of Statistics, University of Missouri, Columbia, Missouri, United States of America
- * E-mail: (SG); (SB); (SC)
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10
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Rickert CA, Lieleg O. Machine learning approaches for biomolecular, biophysical, and biomaterials research. BIOPHYSICS REVIEWS 2022; 3:021306. [PMID: 38505413 PMCID: PMC10914139 DOI: 10.1063/5.0082179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/12/2022] [Indexed: 03/21/2024]
Abstract
A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.
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11
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Structural Stability Analysis of Proteins Using End-to-End Distance: A 3D-RISM Approach. J 2022. [DOI: 10.3390/j5010009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The stability of a protein is determined from its properties and surrounding solvent. In our previous study, the total energy as a sum of the conformational and solvation free energies was demonstrated to be an appropriate energy function for evaluating the stability of a protein in a protein folding system. We plotted the various energies against the root mean square deviation, required as a reference structure. Herein, we replotted the various energies against the end-to-end distance between the N- and C-termini, which is not a required reference and is experimentally measurable. The solvation free energies for all proteins tend to be low as the end-to-end distance increases, whereas the conformational energies tend to be low as the end-to-end distance decreases. The end-to-end distance is one of interesting measures to study the behavior of proteins.
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12
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Atilgan AR, Atilgan C. Computational strategies for protein conformational ensemble detection. Curr Opin Struct Biol 2021; 72:79-87. [PMID: 34563946 DOI: 10.1016/j.sbi.2021.08.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/13/2021] [Accepted: 08/17/2021] [Indexed: 01/18/2023]
Abstract
Protein function is constrained by the three-dimensional structure but is delineated by its dynamics. This framework must satisfy specificity of function along with adaptability to changing environments and evolvability under external constraints. The accessibility of the available regions of the energy landscape for a set of conditions and shifts in the populations upon their modulation have effects propagating across scales, from biomolecular interactions, to organisms, to populations. Developing the ability to detect and juggle protein conformations supplemented by a physics-based understanding has implications for not only in vivo problems but also for resistance impeding drug discovery and bionano-sensor design.
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Affiliation(s)
- Ali Rana Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey
| | - Canan Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey.
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13
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Lerner E, Barth A, Hendrix J, Ambrose B, Birkedal V, Blanchard SC, Börner R, Sung Chung H, Cordes T, Craggs TD, Deniz AA, Diao J, Fei J, Gonzalez RL, Gopich IV, Ha T, Hanke CA, Haran G, Hatzakis NS, Hohng S, Hong SC, Hugel T, Ingargiola A, Joo C, Kapanidis AN, Kim HD, Laurence T, Lee NK, Lee TH, Lemke EA, Margeat E, Michaelis J, Michalet X, Myong S, Nettels D, Peulen TO, Ploetz E, Razvag Y, Robb NC, Schuler B, Soleimaninejad H, Tang C, Vafabakhsh R, Lamb DC, Seidel CAM, Weiss S. FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices. eLife 2021; 10:e60416. [PMID: 33779550 PMCID: PMC8007216 DOI: 10.7554/elife.60416] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/09/2021] [Indexed: 12/18/2022] Open
Abstract
Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current 'state of the art' from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage 'open science' practices.
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Affiliation(s)
- Eitan Lerner
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Anders Barth
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Jelle Hendrix
- Dynamic Bioimaging Lab, Advanced Optical Microscopy Centre and Biomedical Research Institute (BIOMED), Hasselt UniversityDiepenbeekBelgium
| | - Benjamin Ambrose
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Victoria Birkedal
- Department of Chemistry and iNANO center, Aarhus UniversityAarhusDenmark
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research HospitalMemphisUnited States
| | - Richard Börner
- Laserinstitut HS Mittweida, University of Applied Science MittweidaMittweidaGermany
| | - Hoi Sung Chung
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Thorben Cordes
- Physical and Synthetic Biology, Faculty of Biology, Ludwig-Maximilians-Universität MünchenPlanegg-MartinsriedGermany
| | - Timothy D Craggs
- Department of Chemistry, University of SheffieldSheffieldUnited Kingdom
| | - Ashok A Deniz
- Department of Integrative Structural and Computational Biology, The Scripps Research InstituteLa JollaUnited States
| | - Jiajie Diao
- Department of Cancer Biology, University of Cincinnati School of MedicineCincinnatiUnited States
| | - Jingyi Fei
- Department of Biochemistry and Molecular Biology and The Institute for Biophysical Dynamics, University of ChicagoChicagoUnited States
| | - Ruben L Gonzalez
- Department of Chemistry, Columbia UniversityNew YorkUnited States
| | - Irina V Gopich
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of HealthBethesdaUnited States
| | - Taekjip Ha
- Department of Biophysics and Biophysical Chemistry, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Howard Hughes Medical InstituteBaltimoreUnited States
| | - Christian A Hanke
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Gilad Haran
- Department of Chemical and Biological Physics, Weizmann Institute of ScienceRehovotIsrael
| | - Nikos S Hatzakis
- Department of Chemistry & Nanoscience Centre, University of CopenhagenCopenhagenDenmark
- Denmark Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Sungchul Hohng
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National UniversitySeoulRepublic of Korea
| | - Seok-Cheol Hong
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science and Department of Physics, Korea UniversitySeoulRepublic of Korea
| | - Thorsten Hugel
- Institute of Physical Chemistry and Signalling Research Centres BIOSS and CIBSS, University of FreiburgFreiburgGermany
| | - Antonino Ingargiola
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Chirlmin Joo
- Department of BioNanoScience, Kavli Institute of Nanoscience, Delft University of TechnologyDelftNetherlands
| | - Achillefs N Kapanidis
- Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of OxfordOxfordUnited Kingdom
| | - Harold D Kim
- School of Physics, Georgia Institute of TechnologyAtlantaUnited States
| | - Ted Laurence
- Physical and Life Sciences Directorate, Lawrence Livermore National LaboratoryLivermoreUnited States
| | - Nam Ki Lee
- School of Chemistry, Seoul National UniversitySeoulRepublic of Korea
| | - Tae-Hee Lee
- Department of Chemistry, Pennsylvania State UniversityUniversity ParkUnited States
| | - Edward A Lemke
- Departments of Biology and Chemistry, Johannes Gutenberg UniversityMainzGermany
- Institute of Molecular Biology (IMB)MainzGermany
| | - Emmanuel Margeat
- Centre de Biologie Structurale (CBS), CNRS, INSERM, Universitié de MontpellierMontpellierFrance
| | | | - Xavier Michalet
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
| | - Sua Myong
- Department of Biophysics, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel Nettels
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Thomas-Otavio Peulen
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Evelyn Ploetz
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Yair Razvag
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, and The Center for Nanoscience and Nanotechnology, Faculty of Mathematics & Science, The Edmond J. Safra Campus, The Hebrew University of JerusalemJerusalemIsrael
| | - Nicole C Robb
- Warwick Medical School, University of WarwickCoventryUnited Kingdom
| | - Benjamin Schuler
- Department of Biochemistry and Department of Physics, University of ZurichZurichSwitzerland
| | - Hamid Soleimaninejad
- Biological Optical Microscopy Platform (BOMP), University of MelbourneParkvilleAustralia
| | - Chun Tang
- College of Chemistry and Molecular Engineering, PKU-Tsinghua Center for Life Sciences, Beijing National Laboratory for Molecular Sciences, Peking UniversityBeijingChina
| | - Reza Vafabakhsh
- Department of Molecular Biosciences, Northwestern UniversityEvanstonUnited States
| | - Don C Lamb
- Physical Chemistry, Department of Chemistry, Center for Nanoscience (CeNS), Center for Integrated Protein Science Munich (CIPSM) and Nanosystems Initiative Munich (NIM), Ludwig-Maximilians-UniversitätMünchenGermany
| | - Claus AM Seidel
- Lehrstuhl für Molekulare Physikalische Chemie, Heinrich-Heine-UniversitätDüsseldorfGermany
| | - Shimon Weiss
- Department of Chemistry and Biochemistry, and Department of Physiology, University of California, Los AngelesLos AngelesUnited States
- Department of Physiology, CaliforniaNanoSystems Institute, University of California, Los AngelesLos AngelesUnited States
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14
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Medina E, R Latham D, Sanabria H. Unraveling protein's structural dynamics: from configurational dynamics to ensemble switching guides functional mesoscale assemblies. Curr Opin Struct Biol 2021; 66:129-138. [PMID: 33246199 PMCID: PMC7965259 DOI: 10.1016/j.sbi.2020.10.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 12/18/2022]
Abstract
Evidence regarding protein structure and function manifest the imperative role that dynamics play in proteins, underlining reconsideration of the unanimated sequence-to-structure-to-function paradigm. Structural dynamics portray a heterogeneous energy landscape described by conformational ensembles where each structural representation can be responsible for unique functions or enable macromolecular assemblies. Using the human p27/Cdk2/Cyclin A ternary complex as an example, we highlight the vital role of intramolecular and intermolecular dynamics for target recognition, binding, and inhibition as a critical modulator of cell division. Rapidly sampling configurations is critical for the population of different conformational ensembles encoding functional roles. To garner this knowledge, we present how the integration of (sub)ensemble and single-molecule fluorescence spectroscopy with molecular dynamic simulations can characterize structural dynamics linking the heterogeneous ensembles to function. The incorporation of dynamics into the sequence-to-structure-to-function paradigm promises to assist in tackling various challenges, including understanding the formation and regulation of mesoscale assemblies inside cells.
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Affiliation(s)
- Exequiel Medina
- Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Casilla 653, Santiago 7800003, Chile; Department of Physics and Astronomy, Clemson University, Clemson 29634, United States
| | - Danielle R Latham
- Department of Physics and Astronomy, Clemson University, Clemson 29634, United States
| | - Hugo Sanabria
- Department of Physics and Astronomy, Clemson University, Clemson 29634, United States.
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15
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The road less traveled in protein folding: evidence for multiple pathways. Curr Opin Struct Biol 2020; 66:83-88. [PMID: 33220553 DOI: 10.1016/j.sbi.2020.10.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 10/11/2020] [Indexed: 11/23/2022]
Abstract
Free Energy Landscape theory of Protein Folding, introduced over 20 years ago, implies that a protein has many paths to the folded conformation with the lowest free energy. Despite the knowledge in principle, it has been remarkably hard to detect such pathways. The lack of such observations is primarily due to the fact that no one experimental technique can detect many parts of the protein simultaneously with the time resolution necessary to see such differences in paths. However, recent technical developments and employment of multiple experimental probes and folding prompts have illuminated multiple folding pathways in a number of proteins that had all previously been described with a single path.
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16
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Durham RJ, Latham DR, Sanabria H, Jayaraman V. Structural Dynamics of Glutamate Signaling Systems by smFRET. Biophys J 2020; 119:1929-1936. [PMID: 33096078 PMCID: PMC7732771 DOI: 10.1016/j.bpj.2020.10.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/06/2020] [Accepted: 10/13/2020] [Indexed: 12/19/2022] Open
Abstract
Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique for investigating the structural dynamics of biological macromolecules. smFRET reveals the conformational landscape and dynamic changes of proteins by building on the static structures found using cryo-electron microscopy, x-ray crystallography, and other methods. Combining smFRET with static structures allows for a direct correlation between dynamic conformation and function. Here, we discuss the different experimental setups, fluorescence detection schemes, and data analysis strategies that enable the study of structural dynamics of glutamate signaling across various timescales. We illustrate the versatility of smFRET by highlighting studies of a wide range of questions, including the mechanism of activation and transport, the role of intrinsically disordered segments, and allostery and cooperativity between subunits in biological systems responsible for glutamate signaling.
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Affiliation(s)
- Ryan J Durham
- University of Texas Health Science Center at Houston, Houston, Texas
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17
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Girodat D, Pati AK, Terry DS, Blanchard SC, Sanbonmatsu KY. Quantitative comparison between sub-millisecond time resolution single-molecule FRET measurements and 10-second molecular simulations of a biosensor protein. PLoS Comput Biol 2020; 16:e1008293. [PMID: 33151943 PMCID: PMC7643941 DOI: 10.1371/journal.pcbi.1008293] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/27/2020] [Indexed: 12/15/2022] Open
Abstract
Molecular Dynamics (MD) simulations seek to provide atomic-level insights into conformationally dynamic biological systems at experimentally relevant time resolutions, such as those afforded by single-molecule fluorescence measurements. However, limitations in the time scales of MD simulations and the time resolution of single-molecule measurements have challenged efforts to obtain overlapping temporal regimes required for close quantitative comparisons. Achieving such overlap has the potential to provide novel theories, hypotheses, and interpretations that can inform idealized experimental designs that maximize the detection of the desired reaction coordinate. Here, we report MD simulations at time scales overlapping with in vitro single-molecule Förster (fluorescence) resonance energy transfer (smFRET) measurements of the amino acid binding protein LIV-BPSS at sub-millisecond resolution. Computationally efficient all-atom structure-based simulations, calibrated against explicit solvent simulations, were employed for sampling multiple cycles of LIV-BPSS clamshell-like conformational changes on the time scale of seconds, examining the relationship between these events and those observed by smFRET. The MD simulations agree with the smFRET measurements and provide valuable information on local dynamics of fluorophores at their sites of attachment on LIV-BPSS and the correlations between fluorophore motions and large-scale conformational changes between LIV-BPSS domains. We further utilize the MD simulations to inform the interpretation of smFRET data, including Förster radius (R0) and fluorophore orientation factor (κ2) determinations. The approach we describe can be readily extended to distinct biochemical systems, allowing for the interpretation of any FRET system conjugated to protein or ribonucleoprotein complexes, including those with more conformational processes, as well as those implementing multi-color smFRET. Förster (fluorescence) resonance energy transfer (FRET) has been used extensively by biophysicists as a molecular-scale ruler that yields fundamental structural and kinetic insights into transient processes including complex formation and conformational rearrangements required for biological function. FRET techniques require the identification of informative fluorophore labeling sites, spaced at defined distances to inform on a reaction coordinate of interest and consideration of noise sources that have the potential to obscure quantitative interpretations. Here, we describe an approach to leverage advancements in computationally efficient all-atom structure-based molecular dynamics simulations in which structural dynamics observed via FRET can be interpreted in full atomistic detail on commensurate time scales. We demonstrate the potential of this approach using a model FRET system, the amino acid binding protein LIV-BPSS conjugated to self-healing organic fluorophores. LIV-BPSS exhibits large scale, sub-millisecond clamshell-like conformational changes between open and closed conformations associated with ligand unbinding and binding, respectively. Our findings inform on the molecular basis of the dynamics observed by smFRET and on strategies to optimize fluorophore labeling sites, the manner of fluorophore attachment, and fluorophore composition.
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Affiliation(s)
- Dylan Girodat
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Avik K Pati
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Daniel S Terry
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Scott C Blanchard
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Karissa Y Sanbonmatsu
- Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.,New Mexico Consortium, Los Alamos, New Mexico, United States of America
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18
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Thomsen J, Sletfjerding MB, Jensen SB, Stella S, Paul B, Malle MG, Montoya G, Petersen TC, Hatzakis NS. DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife 2020; 9:e60404. [PMID: 33138911 PMCID: PMC7609065 DOI: 10.7554/elife.60404] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 10/02/2020] [Indexed: 12/20/2022] Open
Abstract
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.
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Affiliation(s)
- Johannes Thomsen
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | | | - Simon Bo Jensen
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | - Stefano Stella
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Bijoya Paul
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | - Mette Galsgaard Malle
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
| | - Guillermo Montoya
- Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
| | | | - Nikos S Hatzakis
- Department of Chemistry and Nanoscience Centre, University of CopenhagenCopenhagenDenmark
- Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of CopenhagenCopenhagenDenmark
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19
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Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020; 25:E4783. [PMID: 33081072 PMCID: PMC7594097 DOI: 10.3390/molecules25204783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 01/01/2023] Open
Abstract
Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.
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Affiliation(s)
| | | | - Carlos Amero
- Laboratorio de Bioquímica y Resonancia Magnética Nuclear, Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (R.C.); (J.M.-S.)
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20
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Fuchigami S, Niina T, Takada S. Particle Filter Method to Integrate High-Speed Atomic Force Microscopy Measurements with Biomolecular Simulations. J Chem Theory Comput 2020; 16:6609-6619. [PMID: 32805119 DOI: 10.1021/acs.jctc.0c00234] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
High-speed atomic force microscopy (HS-AFM) can be used to observe the structural dynamics of biomolecules at the single-molecule level in real time under near-physiological conditions; however, its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions, albeit with some artifacts. Here, to integrate HS-AFM data and coarse-grained molecular dynamics (CG-MD) simulations, we develop a particle filter method that implements a sequential Bayesian data assimilation approach. We test the method in a twin experiment. First, we generate a reference HS-AFM movie from the CG-MD trajectory of a test molecule, a nucleosome; this serves as the "experimental measurement". Then, we perform a particle filter simulation with 512 particles, which captures the large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing particle filter simulations with 8-8192 particles, we find that using greater numbers of particles consistently increases the likelihood of the whole AFM movie. By comparing the likelihoods for different ionic concentrations and time scale mappings, we find that the "true" concentration and time scale mapping can be inferred as the largest likelihood of the whole AFM movie but not that of each AFM image. The particle filter method provides a general approach for integrating HS-AFM data with MD simulations.
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Affiliation(s)
- Sotaro Fuchigami
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Toru Niina
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
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21
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Okazaki KI, Nakamura A, Iino R. Chemical-State-Dependent Free Energy Profile from Single-Molecule Trajectories of Biomolecular Motors: Application to Processive Chitinase. J Phys Chem B 2020; 124:6475-6487. [DOI: 10.1021/acs.jpcb.0c02698] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Kei-ichi Okazaki
- Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8585, Japan
| | - Akihiko Nakamura
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8787, Japan
- Department of Applied Life Sciences, Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Ryota Iino
- Department of Life and Coordination-Complex Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, 444-8787, Japan
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22
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Bacic L, Sabantsev A, Deindl S. Recent advances in single-molecule fluorescence microscopy render structural biology dynamic. Curr Opin Struct Biol 2020; 65:61-68. [PMID: 32634693 DOI: 10.1016/j.sbi.2020.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/21/2020] [Accepted: 05/21/2020] [Indexed: 01/30/2023]
Abstract
Single-molecule fluorescence microscopy has long been appreciated as a powerful tool to study the structural dynamics that enable biological function of macromolecules. Recent years have witnessed the development of more complex single-molecule fluorescence techniques as well as powerful combinations with structural approaches to obtain mechanistic insights into the workings of various molecular machines and protein complexes. In this review, we highlight these developments that together bring us one step closer to a dynamic understanding of biological processes in atomic details.
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Affiliation(s)
- Luka Bacic
- Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Anton Sabantsev
- Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Sebastian Deindl
- Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
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23
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Srivastava A, Tiwari SP, Miyashita O, Tama F. Integrative/Hybrid Modeling Approaches for Studying Biomolecules. J Mol Biol 2020; 432:2846-2860. [DOI: 10.1016/j.jmb.2020.01.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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24
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Matsunaga Y, Sugita Y. Use of single-molecule time-series data for refining conformational dynamics in molecular simulations. Curr Opin Struct Biol 2020; 61:153-159. [DOI: 10.1016/j.sbi.2019.12.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/24/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022]
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25
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McFarlane JMB, Krause KD, Paci I. Accelerated Structural Prediction of Flexible Protein–Ligand Complexes: The SLICE Method. J Chem Inf Model 2019; 59:5263-5275. [DOI: 10.1021/acs.jcim.9b00688] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- James M. B. McFarlane
- Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 3V6, Canada
| | - Katherine D. Krause
- Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 3V6, Canada
| | - Irina Paci
- Department of Chemistry, University of Victoria, Victoria, British Columbia V8W 3V6, Canada
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26
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Chen S, Wiewiora RP, Meng F, Babault N, Ma A, Yu W, Qian K, Hu H, Zou H, Wang J, Fan S, Blum G, Pittella-Silva F, Beauchamp KA, Tempel W, Jiang H, Chen K, Skene RJ, Zheng YG, Brown PJ, Jin J, Luo C, Chodera JD, Luo M. The dynamic conformational landscape of the protein methyltransferase SETD8. eLife 2019; 8:45403. [PMID: 31081496 PMCID: PMC6579520 DOI: 10.7554/elife.45403] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/08/2019] [Indexed: 12/27/2022] Open
Abstract
Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape. Our cells contain thousands of proteins that perform many different tasks. Such tasks often involve significant changes in the shape of a protein that allow it to interact with other proteins or ligands. Understanding these shape changes can be an essential step for predicting and manipulating how proteins work or designing new drugs. Some changes in protein shape happen quickly, whereas others take longer. Existing experimental approaches generally only capture some, but not all, of the different shapes an individual protein adopts. A family of proteins known as protein lysine methyltransferases (PKMTs) help to regulate the activities of other proteins by adding small tags called methyl groups to specific positions on their target proteins. PKMTs play important roles in many life processes including in activating genes, maintaining stem cells and controlling how organs develop. It is important for cells to properly control the activity of PKMTs because too much, or too little, activity can promote cancers and neurological diseases. For example, genetic mutations that increase the levels of a PKMT known as SETD8 appear to promote the progression of some breast cancers and childhood leukemia. There is a pressing need to develop new drugs that can inhibit SETD8 and other PKMTs in human patients. However, these efforts are hindered by the lack of understanding of exactly how the shape of PKMT proteins change as they operate in cells. Chen, Wiewiora et al. used a technique called X-ray crystallography to generate structural models of the human SETD8 protein in the presence or absence of native or foreign ligands. These models were used to develop computer simulations of how the shape of SETD8 changes as it operates. Further computational analysis and laboratory experiments revealed how slow changes in the shape of SETD8 contribute to the ability of the protein to attach methyl groups to other proteins. This work is a significant stepping-stone to developing a complete model of how the SETD8 protein works, as well as understanding how genetic mutations may affect the protein’s role in the body. The next step is to refine the model by integrating data from other approaches including biophysical models and mathematical calculations of the energy associated with the shape changes, with a long-term goal to better understand and then manipulate the function of SETD8.
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Affiliation(s)
- Shi Chen
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.,Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Rafal P Wiewiora
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States.,Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Fanwang Meng
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Nicolas Babault
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Anqi Ma
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Wenyu Yu
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Kun Qian
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Hao Hu
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Hua Zou
- Takeda California, Science Center Drive, San Diego, United States
| | - Junyi Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Shijie Fan
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Gil Blum
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Fabio Pittella-Silva
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Kyle A Beauchamp
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Wolfram Tempel
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Hualiang Jiang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Kaixian Chen
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Robert J Skene
- Takeda California, Science Center Drive, San Diego, United States
| | - Yujun George Zheng
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, United States
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto, Toronto, Canada
| | - Jian Jin
- Mount Sinai Center for Therapeutics Discovery, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, United States.,Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Cheng Luo
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - John D Chodera
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Minkui Luo
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States.,Program of Pharmacology, Weill Cornell Medical College of Cornell University, New York, United States
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27
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Croop B, Zhang C, Lim Y, Gelfand RM, Han KY. Recent advancement of light-based single-molecule approaches for studying biomolecules. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 11:e1445. [PMID: 30724484 DOI: 10.1002/wsbm.1445] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 12/01/2018] [Accepted: 01/08/2019] [Indexed: 12/27/2022]
Abstract
Recent advances in single-molecule techniques have led to new discoveries in analytical chemistry, biophysics, and medicine. Understanding the structure and behavior of single biomolecules provides a wealth of information compared to studying large ensembles. However, developing single-molecule techniques is challenging and requires advances in optics, engineering, biology, and chemistry. In this paper, we will review the state of the art in single-molecule applications with a focus over the last few years of development. The advancements covered will mainly include light-based in vitro methods, and we will discuss the fundamentals of each with a focus on the platforms themselves. We will also summarize their limitations and current and future applications to the wider biological and chemical fields. This article is categorized under: Laboratory Methods and Technologies > Imaging Laboratory Methods and Technologies > Macromolecular Interactions, Methods Analytical and Computational Methods > Analytical Methods.
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Affiliation(s)
- Benjamin Croop
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida
| | - Chenyi Zhang
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida
| | - Youngbin Lim
- Department of Bioengineering, Stanford University, Stanford, California
| | - Ryan M Gelfand
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida
| | - Kyu Young Han
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida
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28
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Buckle AM, Borg NA. Integrating Experiment and Theory to Understand TCR-pMHC Dynamics. Front Immunol 2018; 9:2898. [PMID: 30581442 PMCID: PMC6293202 DOI: 10.3389/fimmu.2018.02898] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 11/26/2018] [Indexed: 11/13/2022] Open
Abstract
The conformational dynamism of proteins is well established. Rather than having a single structure, proteins are more accurately described as a conformational ensemble that exists across a rugged energy landscape, where different conformational sub-states interconvert. The interaction between αβ T cell receptors (TCR) and cognate peptide-MHC (pMHC) is no exception, and is a dynamic process that involves substantial conformational change. This review focuses on technological advances that have begun to establish the role of conformational dynamics and dynamic allostery in TCR recognition of the pMHC and the early stages of signaling. We discuss how the marriage of molecular dynamics (MD) simulations with experimental techniques provides us with new ways to dissect and interpret the process of TCR ligation. Notably, application of simulation techniques lags behind other fields, but is predicted to make substantial contributions. Finally, we highlight integrated approaches that are being used to shed light on some of the key outstanding questions in the early events leading to TCR signaling.
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Affiliation(s)
- Ashley M Buckle
- Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia
| | - Natalie A Borg
- Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia
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29
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Mittal S, Shukla D. Maximizing Kinetic Information Gain of Markov State Models for Optimal Design of Spectroscopy Experiments. J Phys Chem B 2018; 122:10793-10805. [DOI: 10.1021/acs.jpcb.8b07076] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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