Introduction to the structure of macromolecules ❑ 11 lectures ≈ 22 h 1. Introduction to the structure of macromolecules 2. Structure of biomolecules 3. Bioinformatics databases 4. Structure prediction 5. Models of structures 6. Stability and dynamics of macromolecules 7. Analysis of protein structures 8. Protein-ligand complexes 9. Macromolecular complexes and interactions 10. Engineering of protein structures 11. Applications of structural biology and bioinformatics Course information 2Course information ❑ Joan Planas, PhD → Lectures 3-5 ❑ Anthony Legrand, PhD → Lecture 9 ❑ Lecturers ❑ Sérgio Marques, PhD → Main lecturer ❑ David Bednář, PhD → Lecture 6 Course information 3Course information ❑ Examination ❑ Written exam, multiple choices, 25 questions, 25 points ▪ A: 25-22 ▪ B: 21-19 ▪ C: 18-16 ▪ D: 15-13 ▪ E: 12-10 ▪ F (fail): < 10 ❑ 3 exam dates; you can attend them all ❑ 10/17 Dec. 2024 (to be voted) ❑ Jan. 2025 ❑ Feb. 2025 ❑ Slides with essential information have the sign: Course information 4Course information ❑ Literature (provided) ❑ Petsko, G. A. & Ringe, D. (2004). Protein Structure and Function, New Science Press, London. ❑ Gu, J. & Bourne, P. E. (2009). Structural Bioinformatics, 2nd Edition, WileyBlackwell, Hoboken. ❑ Widłak, W. (2013). Molecular Biology - Not Only for Bioinformaticians. Springer Berlin, Heidelberg ❑ Lecture slides (uploaded every week) ❑ Journal articles (not essential) ❑ Alternative literature (not provided) ❑ Claverie, J-M., & Notredame, C. (2006), Bioinformatics for Dummies. Wiley Publishing, Hoboken ❑ Xiong, J. (2006), Essential Bioinformatics, Cambridge University Press, New York. ❑ T. Schwede & M. C. Peitsch (2008), Computational Structural Biology: Methods and Applications, World Scientific Publishing Company ❑ Liljas, L. Liljas, J. Piskur, G. Lindblom, P. Nissen, M. Kjeldgaard (2009), Textbook Of Structural Biology, World Scientific Publishing Company Course information 5Course information ▪ Semester: autumn ▪ Exercises: 2 hours/week ▪ Tutors: MUDr. J. Mičan, Mgr. J. Horáčková, Dr. S. Eyrilmez, Dr. A. Legrand ▪ Outline: ▪ Visualize 3D structure of biomolecules ▪ Obtain structures and relevant information from databases ▪ Analyze function, stability and dynamics of biomolecules ▪ Predict the structures of proteins and their complexes ▪ Predict the effects of mutations and engineer protein properties Structural biology - practice - Bi9410cen 6Other courses by Loschmidt Laboratories EN ▪ Semester: autumn ▪ Lectures: 2 hours/week; exercises: 2 hours/week ▪ Lecturers: Dr. Z. Prokop, Dr. M. Marek, Dr. P. Dvořák, Dr. Š. Nevolová ▪ Outline: ▪ Protein and metabolic engineering ▪ Molecular diagnostics and modern vaccines ▪ Cell and gene therapy and regenerative medicine ▪ Molecular biotechnology in industry and agriculture Molecular biotechnology - Bi7430 7Other courses by Loschmidt Laboratories CZ Synthetic biology - S2015 8Other courses by Loschmidt Laboratories CZ▪ Semester: autumn ▪ Lectures: 2 hours/week ▪ Lecturers: Dr. M. Marek, Dr. K. Říha ▪ Outline: ▪ Engineering concepts in synthetic biology ▪ From genetic engineering to synthetic genomes ▪ Protein engineering and design, from proteins to nanomachines ▪ Metabolic engineering, artificial organelles ❑ Motivation ❑ What is Structural Biology and Bioinformatics ❑ Visualization of structure ❑ Energetics of structures ❑ Molecular interactions ❑ Determination of structure Outline 9Introduction to structural biology and bioinformatics Motivation 10Introduction to structural biology and bioinformatics ❑ Sequence-structure-function paradigm ❑ 3D structure  biological function Motivation 11 The inner life of the cell - XVIVO & Harvard University: https://youtu.be/XOaiWl-nW1k Introduction to structural biology and bioinformatics What is Structural Biology 12Introduction to structural biology and bioinformatics ❑ Structural biology is the study of the molecular structure and dynamics of biological macromolecules, particularly proteins and nucleic acids, and how alterations in their structures affect their function What is Structural Biology 13Introduction to structural biology and bioinformatics ❑ Focused on the three-dimensional arrangement of biomolecules – the 3D structure – and their mutual interactions to understand their functions in the cell. ❑ Makes biological objects visible and understood ▪ “Seeing is believing” ▪ To understand, we need to see What is Structural Biology 14 ❑ “Unfortunately, we cannot accurately describe at the chemical level how a molecule functions unless we first know its structure” James Watson, 1964 ❑ Important milestones ▪ 1838 – Protein discovery - Gerardus Mulder ▪ 1869 – DNA discovery - Friedrich Miescher ▪ 1953 – DNA structure - James Watson and Francis Crick ▪ 1958 – Myoglobin crystal structure - John Kendrew ▪ 1959 – Hemoglobin crystal structure - Max Perutz Introduction to structural biology and bioinformatics What is Structural Biology 15Introduction to structural biology and bioinformatics What is Structural Biology 16Introduction to structural biology and bioinformatics ❑ Several different scales 10-10 m 10-9 m 10-6 m What is Structural Biology 17Introduction to structural biology and bioinformatics ❑ Several different scales What is Bioinformatics 18 ❑ Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. ❑ Sequence analysis, genomics, proteomics, systems biology, structural bioinformatics Introduction to structural biology and bioinformatics 19 The students Structure visualization 20Structure visualization ❑ Some widespread-used programs ▪ PyMOL – http://www.pymol.org/ ▪ Chimera – http://www.cgl.ucsf.edu/chimera/ ▪ VMD – http://www.ks.uiuc.edu/Research/vmd/ ❑ Various representation ▪ Bond-based ▪ Backbone-based ▪ Surface-based ❑ Seeing is believing, but … ▪ Beware of misinterpretations and over-interpretations! Structure visualization 21Structure visualization ❑ Bonds-based representation ▪ Fast, little resource-demanding ▪ Suitable for detailed analysis ▪ Incorrect impression about atom packing (empty space) and interatomic distances ❑ Hydrogen atoms are often omitted for simplicity Wire Stick Ball and stick ❑ Backbone-based representation ▪ Moderately fast, not very resource-demanding ▪ Suitable to investigate secondary structure and protein folds ▪ Shows main landmarks; good for overall orientation in the structure Structure visualization 22Structure visualization Ribbon Cartoon Helices Strands Loops Etc. Structure visualization 23Structure visualization ❑ Surface-based representation ▪ Very slow, very resource-demanding ▪ Suitable to study shapes, volume, cavities and molecular contacts CPK/ spheres Surface Energetics of structures 24Energetics of structures ❑ Energy ❑ Entropy ❑ Free energy ❑ Energy landscape Energetics of structures 25Energetics of structures ❑ Energy ▪ Internal energy U (const. V); enthalpy H (constant P), … ▪ Total energy often inaccessible -> differences in energy ▪ Convention: negative energy is favorable, positive is unfavorable ▪ Potential energy Ep – interactions of atoms in a system ▪ Kinetic energy Ek – movement of atoms U = Ep + Ek H = U + P.V Energetics of structures 26Energetics of structures ❑ Entropy ▪ Related to the thermal disorder or conformational availability (degrees of freedom) ▪ Total entropy S > 0 ▪ Higher entropy is more favorable Energetics of structures 27Energetics of structures ❑ Free energy ▪ Helmholtz A or F (const. V), Gibbs G (const. P) ▪ Combination of internal energy or enthalpy and entropy S A = U – TS ; G = H – TS → G = H - TS ▪ Negative change of free energy (ΔG < 0) is favorable H↓ S↑ H↑ S↓ (T = temperature) Energy landscape 28Energetics of structures ❑ Relationship between structure and its potential energy ▪ Structure dictates potential energy – how strong are the individual interactions ▪ Potential energy reflects probability of finding the different structures – lower energy ➔ more frequently occurrence ❑ Potential/free energy surface ▪ Minima – stable structures ▪ Saddle points – transient ▪ Maxima – unstable structures ▪ Energy barriers State 1 Intermediate State 2 Transition states Energy landscape 29Energetics of structures ❑ Relationship between structure and its potential energy ▪ Structure dictates potential energy – how strong are the individual interactions ▪ Potential energy reflects probability of finding the different structures – lower energy ➔ more frequently occurrence ❑ Potential/free energy surface ▪ Minima – stable structures ▪ Saddle points – transient ▪ Maxima – unstable structures ▪ Multidimensional surface Energy landscape 30Energetics of structures Global minimum Local minima Global maximum Local maximum Saddle point ❑ Relationship between structure and its potential energy ▪ Structure dictates potential energy – how strong are the individual interactions ▪ Potential energy reflects probability of finding the different structures – lower energy ➔ more frequently occurrence ❑ Potential/free energy surface ▪ Minima – stable structures ▪ Saddle points – transient ▪ Maxima – unstable structures ▪ Multidimensional surface 31 Molecular interactions Molecular interactions 32Molecular interactions ❑ Covalent interactions (chemical bonds) ▪ Between two atoms sharing electrons ▪ Very stable under standard condition ❑ Non-covalent interactions ▪ Much weaker than covalent bonds ▪ Electrostatic interactions ▪ Polar interactions ▪ Non-polar interactions Electrostatic interactions 33Molecular interactions – electrostatics ❑ Charge-charge or ionic interactions ▪ Coulomb’s law – between any two charges ▪ Attractive (opposite signs) or repulsive (same sign) ▪ Long-range interactions (up to 10 Å) – decrease with r2 2 21 4 r qq F   =  r = distance  = permittivity Electrostatic interactions 34Molecular interactions – electrostatics ❑ Charge-charge or ionic interactions ▪ Environment-dependent ▪ Permittivity ε = ε0·εr ▪ Relative permittivity (εr) = dielectric constant ε0 = vacuum permittivity Stronger force Weaker force Non-polar Highly polar 2 21 4 r qq F   =  Electrostatic interactions 35Molecular interactions – electrostatics ❑ Charge-charge or ionic interactions ▪ Environment dependent ▪ Salt concentration – presence of counter-ions (Na+, K+, Cl-, etc.) ▪ pH – may induce a change of charge Low pH (<4) High pH (>8) Very high pH (>10) Polar interactions 36Molecular interactions – polar ❑ Hydrogen bonds (H-bonds) ▪ Only between highly electronegative atoms: fluorine, oxygen, nitrogen (F, O, N) ▪ Donor and acceptor atoms sharing hydrogen ▪ H-bond distance: 2.8 – 3.4 Å ❑ Aromatic (π-π) interactions ▪ Attractive interaction between aromatic rings ▪ Distance between the center of mass of rings: ~ 5 Å acceptor (-) donor (+) parallel displaced T-shaped sandwich  orbitals Polar interactions 37Molecular interactions – polar ❑ Van der Waals (vdW) interactions ▪ Between any two atoms ▪ Permanent dipole-dipole (in polar molecules) Non-polar interactions 38Molecular interactions – non-polar ❑ Van der Waals (vdW) interactions ▪ Between any two atoms ▪ London dispersion forces, or temporary dipole-induced dipole (in non-polar molecules) ▪ Short-range interactions – up to 5 Å R1, R2 – van der Waals radii r - distance Non-polar interactions 39Molecular interactions – non-polar ❑ Hydrophobic interactions ▪ Entropic origin – water molecules ordered around hydrophobic moiety -> unfavorable ▪ Hydrophobic packing -> favorable release of some ordered water molecules Protein folding game 40FOLDIT ❑ FOLD.IT – https://fold.it/ ▪ Crowdsourcing computer game ▪ Prediction of protein structures ▪ You can contribute to help scientific research 41 Structure determination Structure determination 42Structure determination ❑ Established methods ▪ X-ray crystallography ▪ NMR spectroscopy ▪ Electron microscopy ▪ Bioinformatics predictions – theoretical ▪ Crystallization procedures ▪ Slow (days-weeks) ▪ High risk of failure X-ray crystallography 43Structure determination – X-ray crystallography X-ray crystallography 44Structure determination – X-ray crystallography European Synchrotron Radiation Facility, Grenoble X-ray sources: X-ray tubes, rotating anodes and synchrotrons. Synchrotrons produce the brightest X-rays (~70 worldwide) X-ray crystallography 45Structure determination – X-ray crystallography X-ray crystallography 46Structure determination – X-ray crystallography ❑ Crystallization ▪ Hanging drop, sitting drop, microbatch ❑ Data collection ▪ Diffractometers, synchrotrons ❑ Analysis of diffraction data ▪ Solving phase problem ▪ Molecular replacement ▪ Isomorphous replacement ▪ Anomalous scattering ❑ Iterative model building Parameters of an X-ray structure 47Structure determination – X-ray crystallography ❑ Resolution ▪ Measure of the level of detail present in the diffraction pattern ❑ R-factor (residual factor; R-value) ▪ Measure of a model quality – i.e. the agreement between the crystallographic model and the diffraction data ▪ Varies from 0 (ideal) to 0.63 (random structure), typically about 0.2 3 Å (bad) 2 Å (acceptable) 1 Å (exceptional) Parameters of an X-ray structure 48Structure determination – X-ray crystallography ❑ B-factors (thermal factors) ▪ Measure of how much an atom oscillates or vibrates around the position specified in the model ▪ Considered a measure of flexibility X-ray crystallography 49Structure determination – X-ray crystallography ❑ Advantages ▪ No limitations in size ▪ Possibility to obtain an atomic resolution ❑ Disadvantages ▪ Requirement of a crystal ▪ Structure in a crystalline state (non-native) ▪ Static picture of macromolecule ▪ Position of hydrogen atoms (usually) are not detected NMR spectroscopy 50Structure determination – NMR spectroscopy ❑ Nuclear magnetic resonance (NMR) ▪ Detects energy transitions in the magnetic moments of nuclei with non-zero nuclear spins ▪ Common isotopes: 1H, 13C, 15N, 31P, 35Cl 900 MHz NMR spectrometer NMR spectroscopy 51Structure determination – NMR spectroscopy Parameters of an NMR structure 52Structure determination – NMR spectroscopy ❑ RMSD ▪ Root-mean-squared deviation of atomic positions across the ensemble of solutions ▪ Reveals the mean differences between individual conformations ▪ Important parameter to compare different structures of the same molecule ▪ `  = atom displacement N = total No. atoms NMR spectroscopy 53Structure determination – NMR spectroscopy ❑ Advantages ▪ Structure in solution state (native) ▪ Possibility to investigate dynamics of macromolecules ▪ Position of hydrogen atoms detected ❑ Disadvantages ▪ Size limited to approximately 40 kDa (~ 400 amino acid proteins) ▪ Requirement of isotopically labeled sample Electron microscopy FEI Tecnai T12 Cryotransmission Electron Microscope 54Structure determination – electron microscopy Electron microscopy 55Structure determination – electron microscopy ❑ Wavelength of an electron is much shorter than the wavelength of light ❑ → so it can reveal much smaller things ❑ Samples are flash-frozen in their natural environments (cryo-EM) ❑ Can generate 3D images of large molecules at nearly atomic resolution Electron microscopy 56Structure determination – electron microscopy ❑ Advantages ▪ Applicable to extremely large systems ▪ Complements other methods e. g. X-ray, NMR ❑ Disadvantages ▪ Lower resolution (2-3 Å at best) Bioinformatics predictions 57Structure determination – bioinformatics predictions ❑ Homology modeling ❑ Machine learning ❑ Ab initio prediction Bioinformatics predictions 58Structure determination – bioinformatics predictions Comparative modelling Amino acid sequence Find similar proteins in 3D structure databases Homology search Profile method Threading Machine learning 3D structure database Ab initio predictions Predicted structure Amino acid sequence Energy minimization, Molecular dynamics Predicted structure Bioinformatics predictions 59Structure determination – bioinformatics predictions ❑ Homology modeling Comparison of sequences in databases: Multiple sequence alignment (MSA) Bioinformatics predictions 60Structure determination – bioinformatics predictions ❑ Machine learning ❑ Training on sequence and 3D databases ❑ Ex.: AlphaFold 2 Bioinformatics predictions 61Structure determination – bioinformatics predictions ❑ Ab initio prediction Bioinformatics predictions 62Structure determination – bioinformatics predictions ❑ Advantages ▪ Very fast (except ab initio) ▪ Low cost ❑ Disadvantages ▪ Ab initio is very demanding ▪ Theoretical model – experimental validation is needed References ❑ Petsko, G. A. & Ringe, D. (2004). Protein Structure and Function, New Science Press, London. ❑ Gu, J. & Bourne, P. E. (2009). Structural Bioinformatics, 2nd Edition, WileyBlackwell, Hoboken. ❑ Liljas, A. et al. (2009). Textbook Of Structural Biology, World Scientific Publishing Company, Singapore. ❑ Schwede, T. & Peitsch, M. C. (2008). Computational Structural Biology: Methods and Applications, World Scientific Publishing Company, Singapore. ❑ O’Donoghue, S. et al. (2010) Visualization of macromolecular structures. Nature Methods 7: S42–S55. ❑ Zhou, H-X. & Pang, X. (2018) Electrostatic interactions in protein structure, folding, binding, and condensation. Chemical Reviews. 118: 1691–1741 References 63