From Computational Physics to Structural Bioinformatics Part 0 Fernando Luís BARROSO da Silva Department of Biomolecular Sciences, FCFRP/USP, Brazil [email protected] March 09, 2020 1 or Computational physics applied to biological science or where Physics, Biology and computer science meet [pictures from internet, unknown internet,from[pictures unknown author] Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 2 1 where Physics, Biology and computer science meet Pressure [pictures from internet, unknown internet,from[pictures unknown author] Temperature Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 3 Multi-scale & multi-approach modeling 4 2 Molecular base to understand diseases, pharmaceuticals, bioseparation processes, new functionalized materials, .... Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 5 General approach: to rationalize key applied systems From the REAL system to CG models + - + - + - - + - microencapsulation pH and salt Example of measurements Electrostatic interactions Electrostatic Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 6 3 From Computational Physics to Structural Bioinformatics 1. M.P.Allen e D.J. Tildesley, Computer Simulation of Liquids, (Oxford University Press, 1989). 2. D. Frenkel e B. Smid, Understanding Molecular Simulation, (Academic Press, 2001). 3. T. Schlick, Molecular Modeling and Simulation, (Springer, 2010). 4. J.M. Haille, Molecular Dynamics Simulation: Elementary Methods, (Wiley-Interscience, 1997). 5. D.C. Rappaport, The Art of Molecular Dynamics Simulation, (Cambridge University Press, 1997). + specific scientific papers Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 7 ….Structural Bioinformatics Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 8 4 Syllabus ✓ About us ✓ Bibliography ✓ From complex problems to key answers ✓ Early computer experiments ✓ What is Structural Bioinformatics? ✓ Molecular modeling ✓ Solving the model ✓ Electrostatic interactions & constant-pH simulation methods Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 9 Life is complex! [unknown author][unknown Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 10 5 Physicist΄s toolbox predict events 퐴 = −푘푇푙푛 푄 = 푈 − 푇푆 [unknown author][unknown Free energy 11 푤(푟) = −푘퐵푇 ln[ 푔(푟)] The radial distribution function hist(r) g(r) = nobsV [Barroso da Silva, da Silva, 2000] [Barroso Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 12 6 Intermolecular interactions and the radial distribution function [Skaf & Barroso da Silva, 1994] Water intermolecular interactions Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 13 [FB, LGD, SP & PD, 2016] Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 14 7 The radial distribution function How do I generate the configurations? … Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 15 hist(r) g(r) = nobsV Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 16 8 How does computer simulation enter in this picture? Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 17 Easier to be done in a computer experiment! [unknown author][unknown 18 9 Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 19 Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 20 10 rdf Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 21 Structural Bioinformatics Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 22 11 We need to create a common language! Computer simulation B.P.C. – DCBM/FCFRP - USP Fernando Barroso ([email protected]) SAIFR, March 9-15, 2020 23 “…is conceptualising biology in terms of molecules (in the sense of Physical chemistry) and applying “informatics techniques” (derived from disciplinessuch as applied maths, computer science and statistics) to understand and organise the information associated with these molecules, on a large scale.” 24 12 Homology modeling biomolecular “Omics” Molecular proteomics simulation data mining modeling Binding affinities alignments molecular dynamics structure prediction protein interactions genomics docking Statistics Physics 25 PSP Biological approach 1) Find a known structure with a similar sequence 2) Align the sequences 3) Model the unknown structure on the known structure using the alignment 26 13 Physical approach 1) Choose or make a model 2) Simulate f (x) Solution of the Modelling model What Force field? Molecular dynamics? Langevin dynamics? Genetic algorithm? simplification Monte Carlo? reduction in # sites accuracy practical relevance Poisson-Boltzmann? 27 Real world Physical approach Computer Experiments — Our main tools Solve • Modeling the model • Implementation • Setup • Runs (simulation) • Analyses Bioinformatics B.P.C. – DCBM/FCFRP - USP Fernando Barroso ([email protected]) SAIFR - March 9-15, 2020 28 14 Molecular modeling Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 29 Gwild ✓ Fundamental interactions General questions ✓ Each aa contribution G ✓ Molecular mechanisms mutant More specific questions CaM-smMLK • Complex? In what condition? Is it stable? • Contact residues? • Mean smMLK conformation? • What sites have changes on pKas? Where are they localized? • Effect of Ca2+ on the CaM-smMLK? • Effect of smMLK on the Ca2+-CaM? • Critical mutation at contact • Other critical mutations 30 15 Models (general idea) Mesoscopic models 31 1 1 푈 = ෍ 푘 푏 − 푏 2 + ෍ 푘 휃 − 휃 2 2 푏 0 2 휃 0 bonds angles 1 + ෍ 푘 1 + cos 푛휑 − 훿 2 휑 dihedrals 퐴 퐵 푞 푞 + ෍ − + 1 2 Direct approach 푟12 푟6 휀휀 푟 non−bonded 0 pairs B2 Compare with experiments 32 16 33 Examples of force fields Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 34 17 1 1 푈 = ෍ 푘 푏 − 푏 2 + ෍ 푘 휃 − 휃 2 2 푏 0 2 휃 0 bonds angles 1 + ෍ 푘 1 + cos 푛휑 − 훿 2 휑 dihedrals 퐴 퐵 푞 푞 + ෍ − + 1 2 푟12 푟6 휀휀 푟 non−bonded 0 pairs 35 36 18 * Bioinformatics B.P.C. – DCBM/FCFRP - USP Fernando Barroso ([email protected]) SAIFR - March 9-15, 2020 37 38 19 RMC aim 39 40 20 Solving the model f (x) Solution of the Modelling model Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 41 Molecular Dynamics Protein Protein Thermodynamics Protein Dynamics Structure Protein Function compute Forces 푑푟 퐹 Update 푣(푡) = = 푡 + 푣0 Analysis 푑푡 푚 F = ma Trajectory 퐹 푟(푡) = 푡2 + 푣 푡 + 푟 2푚 0 0 Time averages Update positions and velocities 42 21 ✓ Gromacs ✓ Charmm ✓ Amber ✓ Discover, Insight ✓ Sigma, Tripos, … ✓ NAMD ✓ Tinker ✓ Lammps... http://www.mdtutorials.com/gmx/lysozyme/index.html 43 Monte Carlo Trial move Ei Ej E j − Ei 0 → Accept move −E E j − Ei 0 → Accept move if e random number generated uniformly on (0,1) 44 22 Monte Carlo Protein Protein Thermodynamics Structure Protein Function Random displacement 106 steps Evaluate Update ensemble Analysis Energies −E j Pj e Accept or Ensemble averages reject 45 Monte Carlo 46 23 Simulation Methods for Protein Structure Fluctuations Scott H. Northrup and J. Andrew McCammon Biopolymers, Vol. 19, Issue 5, pp. 1001-1016 (1980) Three numerical techniques for generating thermally accessible configurations of globular proteins are considered; these techniques are the molecular dynamics method, the Metropolis Monte Carlo method, and a modified Monte Carlo method which takes account of the forces acting on the protein atoms. The molecular dynamics method is shown to be more efficient than either of the Monte Carlo methods. Because it may be necessary to use Monte Carlo methods in certain important types of sampling problems, the behavior of these methods is examined in some detail. It is found that an acceptance ratio close to 1/6 yields optimum efficiency for the Metropolis method, in contrast to what is often assumed. This result, together with the overall inefficiency of the Monte Carlo methods, appears to arise from the anisotropic forces acting on the protein atoms due to their covalent bonding. Possible ways of improving the Monte Carlo methods are suggested. 47 x Computational physics ✓ Rationalize processes ✓ Molecular design patients ✓ To deeply understand protein function ✓ To identify new targets ✓ To develop methods and algorithms to determine Next Generation Sequencers protein characteristics ✓ To analyze drug binding in silico drug discovery biophysical characterization whole genome sequencing 48 24 Fernando Barroso, FCFRP/USP SAIFR - March 9-15, 2020 49 From Molecular to Macroscopic Scale (Bio2020) Experiments, theory and computer simulation Macroscopic scale Biomolecular interactions Single molecule Electrolyte solutions 50 25 From Molecular to Macroscopic Scale (Bio2020) Experiments, theory and computer simulation Macroscopic scale Biomolecular interactions Single molecule Electrolyte solutions 51 26.