Abstract
Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure–dynamics–function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.
Key words
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dror RO, Dirks RM, Grossman JP et al (2012) Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys 41:429–452
Orozco M (2014) A theoretical view of protein dynamics. Chem Soc Rev 43:5051–5066
Bonomi M, Heller GT, Camilloni C et al (2017) Principles of protein structural ensemble determination. Curr Opin Struct Biol 42:106–116
Piana S, Klepeis JL, Shaw DE (2014) Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr Opin Struct Biol 24:98–105
Karplus M, Kuriyan J (2005) Molecular dynamics and protein function. Proc Natl Acad Sci U S A 102:6679–6685
Henzler-Wildman K, Kern D (2007) Dynamic personalities of proteins. Nature 450:964–972
Bernadó P, Blackledge M (2010) Proteins in dynamic equilibrium. Nature 468:1046–1048
Papaleo E (2015) Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity. Front Mol Biosci 2:28
Grant BJ, Gorfe AA, McCammon JA (2010) Large conformational changes in proteins: signaling and other functions. Curr Opin Struct Biol 20:142–147
Kay LE (2016) New views of functionally dynamic proteins by solution NMR spectroscopy. J Mol Biol 428:323–331
Papaleo E, Saladino G, Lambrughi M et al (2016) The role of protein loops and linkers in conformational dynamics and allostery. Chem Rev 116:6391–6423
Villali J, Kern D (2011) Choreographing an enzyme’s dance. Curr Opin Chem Biol 14:636–643
Tzeng S-R, Kalodimos CG (2011) Protein dynamics and allostery: an NMR view. Curr Opin Struct Biol 21:62–67
Fujimoto A, Okada Y, Boroevich KA et al (2016) Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes. Sci Rep 6:26483
Reimand J, Wagih O, Bader GD (2015) Evolutionary constraint and disease associations of post-translational modification sites in human genomes. PLoS Genet 11:e1004919
Reimand J, Wagih O, Bader GD (2013) The mutational landscape of phosphorylation signaling in cancer. Sci Rep 3:2651
Pon JR, a Marra M (2015) Driver and passenger mutations in cancer. Annu Rev Pathol Mech Dis 10:25–50
Allison JR (2017) Using simulation to interpret experimental data in terms of protein conformational ensembles. Curr Opin Struct Biol 43:79–87
Spiwok V, Sucur Z, Hosek P (2015) Enhanced sampling techniques in biomolecular simulations. Biotechnol Adv 33:1130–1140
Abrams C, Bussi G (2013) Enhanced sampling in molecular dynamics Using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163–199
Eguchi T, Prince T, Wegiel B et al (2015) Role and regulation of myeloid zinc finger protein 1 in cancer. J Cell Biochem 116:2146–2154
Nygaard M, Terkelsen T, Olsen AV et al (2016) The mutational landscape of the oncogenic MZF1 SCAN domain in cancer. Front Mol Biosci 3:1–18
Rafn B, Nielsen CF, Andersen SH et al (2012) ErbB2-driven breast cancer cell invasion depends on a complex signaling network activating myeloid zinc finger-1-dependent cathepsin B expression. Mol Cell 45:764–776
Gaboli M, Kotsi PA, Gurrieri C et al (2001) Mzf1 controls cell proliferation and tumorigenesis service Mzf1 controls cell proliferation and tumorigenesis. Genes Dev 15:1625–1630
Mudduluru G, Vajkoczy P, Allgayer H (2010) Myeloid zinc finger 1 induces migration, invasion, and in vivo metastasis through Axl gene expression in solid cancer. In: Molecular cancer research : MCR, vol 8, pp 159–169
Sander TL, Stringer KF, Maki JL et al (2003) The SCAN domain defines a large family of zinc finger transcription factors. Gene 310:29–38
Peterson FC, Hayes PL, Waltner JK et al (2006) Structure of the SCAN domain from the tumor suppressor protein MZF1. J Mol Biol 363:137–147
Nam K, Honer C, Schumacher C (2004) Structural components of SCAN-domain dimerizations. Proteins 56:685–692
Liang Y, Huimei Hong F, Ganesan P et al (2012) Structural analysis and dimerization profile of the SCAN domain of the pluripotency factor Zfp206. Nucleic Acids Res 40:8721–8732
Noll L, Peterson FC, Hayes PL et al (2008) Heterodimer formation of the myeloid zinc finger 1 SCAN domain and association with promyelocytic leukemia nuclear bodies. Leuk Res 32:1582–1592
Sander TL, Haas AL, Peterson MJ et al (2000) Identification of a novel SCAN box-related protein that interacts with MZF1B. J Biol Chem 275:12857–12867
Lindorff-Larsen K, Best RB, Depristo MA et al (2005) Simultaneous determination of protein structure and dynamics. Nature 433:128–132
Shaw DE, Maragakis P, Lindorff-Larsen K et al (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330:341–346
Lindorff-Larsen K, Maragakis P, Piana S et al (2012) Systematic validation of protein force fields against experimental data. PLoS One 7:e32131
Papaleo E, Sutto L, Gervasio FL et al (2014) Conformational changes and free energies in a proline isomerase. J Chem Theory Comput 10:4169–4174
Piana S, Lindorff-Larsen K, Shaw DE (2011) How robust are protein folding simulations with respect to force field parameterization? Biophys J 100:L47–L49
Mackerell AD, Feig M, Brooks CL (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 25:1400–1415
Bjelkmar P, Larsson P, Cuendet MA et al (2010) Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction Maps, virtual interaction sites, and water models. J Chem Theory Comput 6:459–466
Best RB, Hummer G (2009) Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides. J Phys Chem B 113:9004–9015
Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958
Li DW, Brüschweiler R (2010) NMR-based protein potentials. Angew Chem Int Ed 49:6778–6780
Jiang F, Zhou C-Y, Wu Y-D (2014) Residue-specific force field based on the protein coil library. RSFF1: modification of OPLS-AA/L. J Phys Chem B 118:6983–6998
Lange OF, van der Spoel D, de Groot BL (2010) Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data. Biophys J 99:647–655
Unan H, Yildirim A, Tekpinar M (2015) Opening mechanism of adenylate kinase can vary according to selected molecular dynamics force field. J Comput Aided Mol Des 29:655–665
Tiberti M, Papaleo E, Bengtsen T et al (2015) ENCORE: software for quantitative ensemble comparison. PLoS Comput Biol 11:e1004415
Martín-García F, Papaleo E, Gomez-Puertas P et al (2015) Comparing molecular dynamics force fields in the essential subspace. PLoS One 10:e0121114
Costantini S, Paladino A, Facchiano AM (2008) CALCOM: a software for calculating the center of mass of proteins. Bioinformation 2:271–272
Daidone I, Amadei A (2012) Essential dynamics: foundation and applications. Comput Mol Sci 2:762–770
Amadei A, Linssen AB, Berendsen HJ (1993) Essential dynamics of proteins. Proteins 17:412–425
Hess B (2000) Similarities between principal components of protein dynamics and random diffusion. Phys Rev E 62:8438–8448
Papaleo E, Mereghetti P, Fantucci P et al (2009) Free-energy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: The myoglobin case. J Mol Graph Model 27:889–899
Maisuradze G, Liwo A, Scheraga H (2009) Principal component analysis for protein folding dynamics. J Mol Biol 385:312–329
Maisuradze GG, Leitner DM (2007) Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima. Proteins 67:569–578
Hess B (2002) Convergence of sampling in protein simulations. Phys Rev E Stat Nonlin Soft Matter Phys 65:031910
Mereghetti P, Riccardi L, Brandsdal BO et al (2010) Near native-state conformational landscape of psychrophilic and mesophilic enzymes: probing the folding funnel model. J Phys Chem B 114:7609–7619
Yao X, Scarabelli G, Skjaerven L et al (2015) Protein structure networks with Bio3D. Grantlab, Manassas, VA, pp 1–22
Skjærven L, Yao X-Q, Scarabelli G et al (2014) Integrating protein structural dynamics and evolutionary analysis with Bio3D. BMC Bioinformatics 15:399
Lindorff-Larsen K, Ferkinghoff-Borg J (2009) Similarity measures for protein ensembles. PLoS One 4:e4203
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315:972–976
Agrafiotis DK, Xu H (2002) A self-organizing principle for learning nonlinear manifolds. Proc Natl Acad Sci U S A 99:15869–15872
Michaud-Agrawal N, Denning EJ, Woolf TB et al (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem 32:2319–2327
Ichiye T, Karplus M (1991) Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins 11:205–217
Hünenberger PH, Mark AE, van Gunsteren WF (1995) Fluctuation and cross-correlation analysis of protein motions observed in nanosecond molecular dynamics simulations. J Mol Biol 252:492–503
Lange OF, Grubmüller H (2008) Full correlation analysis of conformational protein dynamics. Proteins 70:1294–1312
Tiberti M, Invernizzi G, Papaleo E (2015) (Dis)similarity index to compare correlated motions in molecular simulations. J Chem Theory Comput 11:4404–4414
Seeber M, Felline A, Raimondi F et al (2011) Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J Comput Chem 32:1183–1194
Invernizzi G, Tiberti M, Lambrughi M et al (2014) Communication routes in ARID domains between distal residues in helix 5 and the DNA-binding loops. PLoS Comput Biol 10:e1003744
Berjanskii M, Zhou J, Liang Y et al (2012) Resolution-by-proxy: a simple measure for assessing and comparing the overall quality of NMR protein structures. J Biomol NMR 53:167–180
Li D, Brüschweiler R (2015) PPM_One: a static protein structure based chemical shift predictor. J Biomol NMR 62:403–409
Guo J, Zhou HX (2016) Protein Allostery and Conformational Dynamics. Chem Rev 116:6503–6515
Ribeiro AAST, Ortiz V (2016) A Chemical Perspective on Allostery. Chem Rev 116:6488–6502
Vuillon L, Lesieur C (2015) From local to global changes in proteins: a network view. Curr Opin Struct Biol 31:1–8
Di Paola L, Giuliani A (2015) Protein contact network topology: a natural language for allostery. Curr Opin Struct Biol 31:43–48
Vishveshwara S, Ghosh A, Hansia P (2009) Intra and inter-molecular communications through protein structure network. Curr Protein Pept Sci 10:146–160
Csermely P, Nussinov R, Szilágyi A (2013) From allosteric drugs to allo-network drugs: state of the art and trends of design, synthesis and computational methods. Curr Top Med Chem 13:2–4
Tiberti M, Invernizzi G, Lambrughi M et al (2014) PyInteraph : a framework for the analysis of interaction networks in structural ensembles of proteins. J Chem Inf Model 54:1537–1551
Brown DK, Penkler DL, Sheik Amamuddy O et al (2017) MD-TASK: a software suite for analyzing molecular dynamics trajectories. Bioinformatics 33:2768–2771
Salamanca Viloria J, Allega MF, Lambrughi M et al (2016) An optimal distance cutoff for contact-based protein structure networks using side chain center of masses. Sci Rep 7:2838
Lovell SC, Word JM, Richardson JS et al (2000) The penultimate rotamer library. Proteins 40:389–408
Lange OF, Grubmüller H (2006) Can principal components yield a dimension reduced description of protein dynamics on long time scales? J Phys Chem B 110:22842–22852
Wriggers W, Stafford KA, Shan Y et al (2009) Automated event detection and activity monitoring in long molecular dynamics simulations. J Chem Theory Comput 5:2595–2605
Savol AJ, Burger VM, Agarwal PK et al (2011) QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. Bioinformatics (Oxford) 27:i52–i60
Ramanathan A, Savol AJ, Agarwal PK et al (2012) Event detection and sub-state discovery from biomolecular simulations using higher-order statistics: application to enzyme adenylate kinase. Proteins 80:2536–2551
Fan Z, Dror RO, Mildorf TJ et al (2015) Identifying localized changes in large systems: change-point detection for biomolecular simulations. Proc Natl Acad Sci U S A 112:7454–7459
Kovacs JA, Wriggers W (2016) Spatial heat maps from fast information matching of fast and slow degrees of freedom: application to molecular dynamics simulations. J Phys Chem B 120:8473–8484
Brinda KV, Vishveshwara S (2005) A network representation of protein structures: implications for protein stability. Biophys J 89:4159–4170
Papaleo E, Renzetti G, Tiberti M (2012) Mechanisms of intramolecular communication in a hyperthermophilic acylaminoacyl peptidase: a molecular dynamics investigation. PLoS One 7:e35686
Papaleo E, Pasi M, Tiberti M et al (2011) Molecular dynamics of mesophilic-like mutants of a cold-adapted enzyme: insights into distal effects induced by the mutations. PLoS One 6:e24214
Pasi M, Tiberti M, Arrigoni A et al (2012) xPyder: a PyMOL plugin to analyze coupled residues and their networks in protein structures. J Chem Inf Model 279:1–6
Laio A, Gervasio FL (2008) Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep Prog Phys 71:126601
Camilloni C, Cavalli A, Vendruscolo M (2013) Replica-averaged metadynamics. J Chem Theory Comput 9:5610–5617
Bonomi M, Camilloni C, Vendruscolo M (2016) Metadynamic metainference: enhanced sampling of the metainference ensemble using metadynamics. Sci Rep 6:31232
Lambrughi M, De Gioia L, Gervasio FL et al (2016) DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions. Nucleic Acids Res 44:9096–9109
Acknowledgments
This work was supported by two ISCRA-CINECA HPC Grants (NetDyn-HP10C2TOOC and ALLO-PCM-HP10CWP9KW) and two EU-PRACE DECI projects DECI-13th and DECI-14th and DeiC Pilot Project in 2016–2017 on a Danish Infrastructure Computerome. EP group is supported by LEO Foundation Grant 2017–2019 (grant number LF17006), Alfred Benzon Investigator Fellowships 2017–2019, a DFF-FNU grant from the Danish Council of Independent Research (grant number 7014-00272B), and Carlsberg Foundation Distinguished Fellowship (grant number CF18-0314). EP group is also part of the Center of Excellence for Autophagy, Recycling and Disease funded by Danmarks Grundforskningsfond (grant number DNRF125).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Lambrughi, M. et al. (2019). Analyzing Biomolecular Ensembles. In: Bonomi, M., Camilloni, C. (eds) Biomolecular Simulations. Methods in Molecular Biology, vol 2022. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9608-7_18
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9608-7_18
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9607-0
Online ISBN: 978-1-4939-9608-7
eBook Packages: Springer Protocols