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protein sturcture prediction and molecular modelling

This document discusses molecular modeling and protein structure prediction. It begins by introducing molecular modeling as a combination of computational chemistry and computer graphics that allows scientists to generate and present molecular data. It then discusses the two main computational methods for molecular modeling - molecular mechanics and quantum mechanics. The document goes on to discuss molecular mechanics in more detail and its applications. It also discusses protein structure and function, the challenges of protein structure prediction, and the goals of protein structure prediction.

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PRESENTED BY: P. DILEEPB.Pharmacy M.Pharmacy(2ND sem) SHIFT II, Roll no:30 Department of Pharmacology VAAGDEVI COLLEGE OF PHARMACY
Contents of Seminar 2 Introduction Molecular modeling Types of molecular modeling Applications of molecular modeling Proteins in brief Purpose of protein structure prediction Types of PSP Conclusion
3
Molecular Modeling 4  The science (or art) of representing molecular structures numerically and simulating their behavior with the equations of quantum and classical physics.  Combination of computational chemistry and computer graphics.  Allows scientists to generate and present molecular data including geometries (bond lengths, bond angles, torsion angles), energies (heat of formation, activation energy, etc.), electronic properties (moments, charges, ionization potential, electron affinity), spectroscopic properties (vibrational modes, chemical shifts) and bulk properties (volumes, surface areas, diffusion, viscosity, etc.). Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
Potential energy variation 5 Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
Molecular Modeling methods 6 The two most common computational methods  Molecular mechanics  Quantum mechanics Both these methods produce equations for the total energy(E) of the structure. MOLECULAR MECHANICS:  Calculation of energy of atoms, force on atoms and their resulting motion.  Used to model the geometry of the molecule, motion of molecule and to get the global minimum energy structure. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
Molecular mechanics 7 Consider a molecule as system of rigid balls connected via springs.  Depends strongly on concepts of bonding  Follows the Newtonian laws  Neglect the electronic degrees of freedom Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
Methods for Molecular mechanics study 8  Potential surface  Study of force field  Study of Electrostatics  Molecular dynamics  Conformational Analysis Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
Methods for Molecular mechanics study 9  Force field is used to describe the total potential energy of a molecule or system as a function of geometry. and the set of parameters required is called “force field parameters”. The total energy is a sum of Taylor series expansions for stretches for every pair of bonded atoms, and adds additional potential energy terms coming from bending, torsional energy, Vander wall energy, electrostatics and cross terms.  Study of Electrostatics involves the study of interaction between various dipoles. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
10 Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
Methods for Molecular mechanics study 11  Molecular dynamics program allow the model to how the natural motion of atoms in the structure. This is achieved by including the kinetic energy term of atoms in the force field equation by using equations based on Newton's law of motion.  Conformational Analysis involves the determination or analysis of the spatial arrangement of the functional group of the respective molecule. Strategies used to study the conformational analysis are Rigid geometry approximation, Rigid body rotation, Conformational clustering. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
QUANTUM MECHANICS 12  Provides information about both nuclear position and distribution.  Based on study of arrangement and interaction of electrons and nuclei of a molecular system.  It does not require the use of parameters similar to those used in molecular mechanics.  It is based on the wave properties of electrons and all material particles. Griffith S, David J(2004), Introduction to Quantum Mechanics, Prentice Hall press, 2nd edition, pp. 1-4.
QUANTUM MECHANICS 13 HΨ = EΨ = (U+K) Ψ Where, H = Hamiltonian for the system, Ψ(“p-sigh”) = wave function, E = energy. Simply put, the Hamiltonian is an “operator,” a mathematical construct that operates on the molecular orbital, Ψ, to determine the energy. U= Potential energy, K= Kinetic energy. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
ADVANTAGES 14  To calculate the value of potentials, electron affinities ,heat of formation, dipole moment and other physical properties  To find the electron density in a structure  To determine the points at which a structure will react with electrophiles and nucleophiles  To determine the shape and electron density of a molecule Rajkumar B, Branson K, Giddy J, Abramson D(2003), The Virtual Laboratory : A toolset to enable distributed molecular modeling for drug design on the World-Wide Grid, Concurrency and computation, 15, pp. 1–25.
15
Proteins…. 16 If there is a job to be done in the molecular world of our cells, usually that job is done by a protein. CATALASE An enzyme which removes Hydrogen peroxide from your body so it does not become toxic A protein hormone which helps to regulate your blood sugar levels http://courses.washington.edu/conj/protein/insulin2.gif http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
Proteins for cell motility 17 Myosin and actin filaments work in coordination for the proper muscle contraction http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
Cell structures 18 Microtubules Tubulin frame work for the exoskeleton Cellular coat Eukaryotic exoskeleton http://www.fz-juelich.de/ibi/ibi-1/Cellular_signaling/ http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html http://cpmcnet.columbia.edu/dept/gsas/anatomy/Faculty/Gundersen/main.html
Enzymes 2 2 + Energy Substrate Product Progress of reaction http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html 19
Harmones and channels 20 http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
Immune response 21 http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
22 Proteins can be fibrous or globular
Fibrous proteins 23 •Collagen is the most abundant protein in vertebrates. Collagen fibers are a major portion of tendons, bone and skin. Alpha helices of collagen make up a triple helix structure giving it tough and flexible properties. •Fibroin fibers make the silk spun by spiders and silk worms stronger weight for weight than steel! The soft and flexible properties come from the beta structure. •Keratin is a tough insoluble protein that makes up the quills of echidna, your hair and nails and the rattle of a rattle snake. The structure comes from alpha helices that are cross-linked by disulfide bonds. http://opbs.okstate.edu/~petracek/2002%20protein%20structure%20function/CH06.gif http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
Globular proteins 24 Cell motility – proteins link together to form filaments which make movement possible. Organic catalysts in biochemical reactions – enzymes Regulatory proteins – hormones, transcription factors Membrane proteins – protein channels Defense against pathogens – poisons/toxins, antibodies, complement Transport and storage – hemoglobin http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
Molecular Logic of Life is Same 25 English Genome  26-Letter alphabet  4-Letter alphabet  Only one grammar  Only one grammar  Extremely diverse  Extremely diverse literature organisms
Gene Expression The protein folds to form its working shape 26 Gene DNA Cell machinery CELL copies the code G T A C T A making an mRNA The order of bases in molecule. This NUCLEUS DNA is a code for moves into the Chromoso making proteins. The cytoplasm. me code is read in Ribosomes read groups of three the code and AUGAGUAAAGGAGAAGAACUUUUCACUGGAU accurately join A Amino acids M S E E L F T K together to make a protein http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
Hallmark of Proteins: Specificity 27 Know exactly which small molecule (ligand) they should bind to or interact with. Also know which part of a macromolecule they should bind to. One Aspect of Genome Sequence Analysis is to Assign Functions to Proteins (Reverse Genetics) Function is critically dependent on structure Schween G, Egener T, Fritzkowsky D, Granado J, Guitton M C(2005), Large-scale analysis of Physcomitrella plants transformed with different gene disruption libraries: Production parameters and mutant phenotypes, Plant Biology, 7 (3), pp. 228–237.
28
How Does Sequence Specify Structure? 29 Sequence Functional ? Genomics Structure Function The Protein Folding Problem (second half of the genetic code) Structure has to be determined experimentally
Protein Structure 30 • Levels of organization – Primary Sequence – Secondary Structure (Modular building blocks) • α-helices • β-sheets – Tertiary Structure – Quartenary Structure • Hydrophobic/Hydrophilic Organization. Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
Protein Structure 31 Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
Secondary Structure: -helix 32 Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
Secondary Structure: -sheets 33 Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
Definition of -turn 34 four consecutive residues i, i+1, i+2 and i+3 that do not form a helix and the turn lead to reversal in the protein chain. The conformation of -turn is defined in terms of two central residues, i+1 and i+2 and can be classified into different types on the basis of this conformation. i+1 i+2 i H- i+3 bond D <7Å Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
Biology/Chemistry of Protein Structure 35 Primary Assembly STRUCTURE PROCESS Secondary Folding Tertiary Packing Quaternary Interaction Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
3 main questions… 36 1. Why predict the structure? 2. Methods for structure prediction 3. What next?
Purpose of PSP 37 Explaining phenotype of existing mutations (experimental or patient-derived) Designing mutants to disrupt or alter specific functions (leaving others unaffected) Hints at function Drug design (at high sequence identity) Hypothesis generation Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
• Anfinsen’s (1973) thermodynamic hypothesis: 38 Proteins are not assembled into their native structures by a biological process, but folding is a purely physical process that depends only on the specific amino acid sequence of the protein. Anfinsen C B(1973), Principles that govern the folding of protein chains, Biological Science, 181, pp. 223–230
The Prediction Problem 39 Can we predict the final 3D protein structure knowing only its amino acid sequence? • Studied for 4 Decades • “Holy Grail” in Biological Sciences • Primary Motivation for Bioinformatics • Based on this 1-to-1 Mapping of Sequence to Structure • Still very much an OPEN PROBLEM Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
PSP: Major Hurdles 40  Energetics  We don‟t know all the forces involved in detail  Too computationally expensive BY FAR!  Conformational search impossibly large  100 AA. protein, 2 moving dihedrals, 2 possible positions for each diheral: 2200 conformations!  Levinthal‟s Paradox  Longer than time of universe to search  Proteins fold in a couple of seconds?? Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
PSP: Goals 41  Accurate 3D structures. But not there yet.  Good “guesses”  Working models for researchers  Understand the FOLDING PROCESS  Get into the Black Box  Only hope for some proteins  25% won‟t crystallize, too big for NMR  Best hope for novel protein engineering  Drug design, etc. Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
Comparative Modeling--Basic Protocol 42 1. Identification of homologue for target sequence 2. Alignment of target sequence to template sequence and structure 3. Side-chain modeling, copy the backbone of the template and model the new side chains onto this backbone 4. Loop modeling, for insertions and deletions in the alignment 5. Refinement of model -- moving template closer to target 6. Assessment of (predicted) model quality 7. Using the model to explain experiments and guide new ones David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60.
Experimental techniques for structure determination 43  X-ray Crystallography  Nuclear Magnetic Resonance spectroscopy (NMR)  Electron Microscopy/Diffraction  Free electron lasers. David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60.
X-ray Crystallography 44  From small molecules to viruses  Information about the positions of individual atoms  Limited information about dynamics  Requires crystals Saville W B, Shearer G (1925), An X-ray Investigation of Saturated Aliphatic Ketones, Journal of the Chemical Society, 127, pp. 591.
NMR 45  Limited to molecules up to ~50kDa (good quality up to 30 kDa)  Distances between pairs of hydrogen atoms  Lots of information about dynamics  Requires soluble, non-aggregating material  Assignment problem Addess M, Kenneth J, Feigon J(1996). Introduction to 1H NMR Spectroscopy of DNA. Bioorganic Chemistry: Nucleic Acids, Oxford University Press, 8th edition, pp. 238.
Electron Microscopy/ Diffraction 46  Low to medium resolution  Limited information about dynamics  Can use very small crystals (nm range)  Can be used for very large molecules and complexes
Tertiary Structure Prediction 47  Template Modeling  Homology Modeling  Threading  Template-Free Modeling  ab initio Methods  Physics-Based  Knowledge-Based Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288.
HOMOLOGY MODELING 48  Constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental 3d structure of a related homologous protein (the "template").  Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence  This approach can be complicated by the presence of alignment gaps (commonly called indels) that arise from poor resolution in the experimental procedure (usually X-ray crystallography). Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
HOMOLOGY MODELING 49  Homology modeling can produce high-quality structural models when the target and template are closely related, which has inspired the formation of a structural genomics consortium.  The analysis and prediction of loop structures for small and medium sized loops and the positioning of side chains, given the conformation of the protein's backbone. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
Threading or Fold Recognition Method 50  Computational protein structure prediction  Distinction between two fold recognition scenarios.  “Threading" (i.e. placing, aligning) each amino acid in the target sequence to a position in the template structure, and evaluating how well the target fits the template. After the best-fit template is selected, the structural model of the sequence is built based on the alignment with the chosen template.  Homologous folds share the Same structure through divergent evolution from a common ancestor.  Analogous folds, on the other hand, share the same structure, but give insufficient evidence for an evolutionary relationship. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
Threading or Fold Recognition Method 51  One popular model for protein folding assumes a sequence of events:  Hydrophobic collapse  Local interactions stabilize secondary structures  Secondary structures interact to form motifs  Motifs aggregate to form tertiary structure Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
Ab-initio method 52  It calculates energetics involved in the process of folding  Finding the structure with lowest free energy  It is based on the „thermodynamic hypothesis‟, which states that the native structure of a protein is the one for which the free energy achieves the global minimum.  2 components to ab initio prediction: 1. devising a scoring (ie, energy) function that can distinguish between correct (native or native-like) structures from incorrect ones. 2. a search method to explore the conformational space.  The most difficult, but most useful approach. Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88.
Ab-initio method 53 Sequence Prediction Secondary structure Low Tertiary Validation Predicted energy structure Energy Mean field structure structures Minimization potentials Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88.
Secondary Structure Prediction 54 • Existing SSP Methods • Statistical Methods (Chou,GOR) • Physio-chemical Methods • A.I. (Neural Network Approach) • Consensus and Multiple Alignment • Our Method APSSP of SSP • Neural Network • Example Based Learnning • Multiple Alignment • Steps involved in APSSP • Blast search against protein sequence (NR) • Multiple Alignment (ClustalW) • Profile by HMMER, Result by Email • Recogntion: CASP,CAFASP,LiveBench, MetaServer Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
Web servers for structure prediction 55 JPRED:-http://www.compbio.dundee.ac.uk/~www-jpred/ PHD:-http://cubic.bioc.columbia.edu/predictprotein/ PSIPRED:-http://bioinf.cs.ucl.ac.uk/psipred/ Chou and Fassman:- http://fasta.bioch.virginia.edu/fasta_www/chofas.htm
Future technologies 56 Modeling of biologically relevant states of proteins using all available templates Homooligomers Heterooligomers Amino acid modifications Bound ligands (small molecules, nucleic acids) Modeling of specific classes of proteins Antibodies Repeat proteins (ARM/HEAT, WD repeats) Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
Available databases, software, and services 57 Rotamer library ProtBuD -- biological units database across families PISCES -- non-redundant sequences in PDB MolIDE 1.5 ArboDraw -- drawing phylogenetic trees BioDownloader -- automatic updating of biological databases Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
Assessment of accuracy of PSP 58 P = (N – total incorrect) N total incorrect = total number of residues whose conformations are predicted incorrectly N = the number of residues in the protein. Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
Applications of PSP: 59  Drug targetting.  Pharmacogenetics.  Pharmacogenomics.  MOA.  Dosage regimen.
Conclusion 60  Pharmacist  Biotechnologis t Molecular modeling
Conclusion 61  Pharmacist  Biotechnologis t Protein structure prediction
References: 62 1. Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230. 2. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63. 3. Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science+Business Media, 12th edition, pp. 21-32. 4. Zhan Y Z, Tom L B(1996), The Use of Amino Acid Patterns of Classified Helices and Strands in Secondary Structure Prediction, Journal of Molecular biology, 260, pp. 61–76. 5. Schween G, Egener T, Fritzkowsky D, Granado J, Guitton M C(2005), Large-scale analysis of Physcomitrella plants transformed with different gene disruption libraries: Production parameters and mutant phenotypes, Plant Biology, 7 (3), pp. 228–237. 6. David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60. 7. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288. 8. Anfinsen C B(1973), Principles that govern the folding of protein chains, Biological Science, 181, pp. 223–230.
References: 63 9. Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88. 10. Saville W B, Shearer G (1925), An X-ray Investigation of Saturated Aliphatic Ketones, Journal of the Chemical Society, 127, pp. 591. 11. Addess M, Kenneth J, Feigon J(1996). Introduction to 1H NMR Spectroscopy of DNA. Bioorganic Chemistry: Nucleic Acids, Oxford University Press, 8th edition, pp. 238. 12. Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198. 13. Rajkumar B, Branson K, Giddy J, Abramson D(2003), The Virtual Laboratory : A toolset to enable distributed molecular modeling for drug design on the World-Wide Grid, Concurrency and computation, 15, pp. 1–25. 14. Griffith S, David J(2004), Introduction to Quantum Mechanics, Prentice Hall press, 2nd edition, 1, pp. 1-4. 15. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3. 16. LEONOR C H, PAULO A S(2001), Protein folding : thermodynamic versus kinetic control, Journal of biological physics, 27, pp. 6-8.
Useful links: 64 Date: 28/10/2012. 1. http://dunbrack.fccc.edu 2. http://courses.washington.edu/conj/protein/insulin2.gif 3. http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html 4. http://opbs.okstate.edu/~petracek/2002%20protein%20structure%20function/CH06.gif 5. http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true 6. http://www.grin.com/object/external_document.274822/5fbac5ddfea3cb2dd3dde8ad8ee98 1f9_LARGE.png
Thank you….. 65

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protein sturcture prediction and molecular modelling

  • 1.
    PRESENTED BY: P. DILEEPB.Pharmacy M.Pharmacy(2NDsem) SHIFT II, Roll no:30 Department of Pharmacology VAAGDEVI COLLEGE OF PHARMACY
  • 2.
    Contents of Seminar 2 Introduction Molecular modeling Types of molecular modeling Applications of molecular modeling Proteins in brief Purpose of protein structure prediction Types of PSP Conclusion
  • 3.
  • 4.
    Molecular Modeling 4  The science (or art) of representing molecular structures numerically and simulating their behavior with the equations of quantum and classical physics.  Combination of computational chemistry and computer graphics.  Allows scientists to generate and present molecular data including geometries (bond lengths, bond angles, torsion angles), energies (heat of formation, activation energy, etc.), electronic properties (moments, charges, ionization potential, electron affinity), spectroscopic properties (vibrational modes, chemical shifts) and bulk properties (volumes, surface areas, diffusion, viscosity, etc.). Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
  • 5.
    Potential energy variation 5 Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
  • 6.
    Molecular Modeling methods 6 The two most common computational methods  Molecular mechanics  Quantum mechanics Both these methods produce equations for the total energy(E) of the structure. MOLECULAR MECHANICS:  Calculation of energy of atoms, force on atoms and their resulting motion.  Used to model the geometry of the molecule, motion of molecule and to get the global minimum energy structure. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
  • 7.
    Molecular mechanics 7 Consider a molecule as system of rigid balls connected via springs.  Depends strongly on concepts of bonding  Follows the Newtonian laws  Neglect the electronic degrees of freedom Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
  • 8.
    Methods for Molecularmechanics study 8  Potential surface  Study of force field  Study of Electrostatics  Molecular dynamics  Conformational Analysis Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
  • 9.
    Methods for Molecularmechanics study 9  Force field is used to describe the total potential energy of a molecule or system as a function of geometry. and the set of parameters required is called “force field parameters”. The total energy is a sum of Taylor series expansions for stretches for every pair of bonded atoms, and adds additional potential energy terms coming from bending, torsional energy, Vander wall energy, electrostatics and cross terms.  Study of Electrostatics involves the study of interaction between various dipoles. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
  • 10.
    10 Leach A R(2001),Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
  • 11.
    Methods for Molecularmechanics study 11  Molecular dynamics program allow the model to how the natural motion of atoms in the structure. This is achieved by including the kinetic energy term of atoms in the force field equation by using equations based on Newton's law of motion.  Conformational Analysis involves the determination or analysis of the spatial arrangement of the functional group of the respective molecule. Strategies used to study the conformational analysis are Rigid geometry approximation, Rigid body rotation, Conformational clustering. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3.
  • 12.
    QUANTUM MECHANICS 12  Provides information about both nuclear position and distribution.  Based on study of arrangement and interaction of electrons and nuclei of a molecular system.  It does not require the use of parameters similar to those used in molecular mechanics.  It is based on the wave properties of electrons and all material particles. Griffith S, David J(2004), Introduction to Quantum Mechanics, Prentice Hall press, 2nd edition, pp. 1-4.
  • 13.
    QUANTUM MECHANICS 13 HΨ = EΨ = (U+K) Ψ Where, H = Hamiltonian for the system, Ψ(“p-sigh”) = wave function, E = energy. Simply put, the Hamiltonian is an “operator,” a mathematical construct that operates on the molecular orbital, Ψ, to determine the energy. U= Potential energy, K= Kinetic energy. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63.
  • 14.
    ADVANTAGES 14  To calculate the value of potentials, electron affinities ,heat of formation, dipole moment and other physical properties  To find the electron density in a structure  To determine the points at which a structure will react with electrophiles and nucleophiles  To determine the shape and electron density of a molecule Rajkumar B, Branson K, Giddy J, Abramson D(2003), The Virtual Laboratory : A toolset to enable distributed molecular modeling for drug design on the World-Wide Grid, Concurrency and computation, 15, pp. 1–25.
  • 15.
  • 16.
    Proteins…. 16 If there is a job to be done in the molecular world of our cells, usually that job is done by a protein. CATALASE An enzyme which removes Hydrogen peroxide from your body so it does not become toxic A protein hormone which helps to regulate your blood sugar levels http://courses.washington.edu/conj/protein/insulin2.gif http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
  • 17.
    Proteins for cellmotility 17 Myosin and actin filaments work in coordination for the proper muscle contraction http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
  • 18.
    Cell structures 18 Microtubules Tubulin frame work for the exoskeleton Cellular coat Eukaryotic exoskeleton http://www.fz-juelich.de/ibi/ibi-1/Cellular_signaling/ http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html http://cpmcnet.columbia.edu/dept/gsas/anatomy/Faculty/Gundersen/main.html
  • 19.
    Enzymes 2 2 + Energy Substrate Product Progress of reaction http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html 19
  • 20.
    Harmones and channels 20 http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
  • 21.
    Immune response 21 http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html
  • 22.
    22 Proteins can be fibrous or globular
  • 23.
    Fibrous proteins 23 •Collagen is the most abundant protein in vertebrates. Collagen fibers are a major portion of tendons, bone and skin. Alpha helices of collagen make up a triple helix structure giving it tough and flexible properties. •Fibroin fibers make the silk spun by spiders and silk worms stronger weight for weight than steel! The soft and flexible properties come from the beta structure. •Keratin is a tough insoluble protein that makes up the quills of echidna, your hair and nails and the rattle of a rattle snake. The structure comes from alpha helices that are cross-linked by disulfide bonds. http://opbs.okstate.edu/~petracek/2002%20protein%20structure%20function/CH06.gif http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
  • 24.
    Globular proteins 24 Cell motility – proteins link together to form filaments which make movement possible. Organic catalysts in biochemical reactions – enzymes Regulatory proteins – hormones, transcription factors Membrane proteins – protein channels Defense against pathogens – poisons/toxins, antibodies, complement Transport and storage – hemoglobin http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
  • 25.
    Molecular Logic ofLife is Same 25 English Genome  26-Letter alphabet  4-Letter alphabet  Only one grammar  Only one grammar  Extremely diverse  Extremely diverse literature organisms
  • 26.
    Gene Expression The protein folds to form its working shape 26 Gene DNA Cell machinery CELL copies the code G T A C T A making an mRNA The order of bases in molecule. This NUCLEUS DNA is a code for moves into the Chromoso making proteins. The cytoplasm. me code is read in Ribosomes read groups of three the code and AUGAGUAAAGGAGAAGAACUUUUCACUGGAU accurately join A Amino acids M S E E L F T K together to make a protein http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true
  • 27.
    Hallmark of Proteins:Specificity 27 Know exactly which small molecule (ligand) they should bind to or interact with. Also know which part of a macromolecule they should bind to. One Aspect of Genome Sequence Analysis is to Assign Functions to Proteins (Reverse Genetics) Function is critically dependent on structure Schween G, Egener T, Fritzkowsky D, Granado J, Guitton M C(2005), Large-scale analysis of Physcomitrella plants transformed with different gene disruption libraries: Production parameters and mutant phenotypes, Plant Biology, 7 (3), pp. 228–237.
  • 28.
  • 29.
    How Does SequenceSpecify Structure? 29 Sequence Functional ? Genomics Structure Function The Protein Folding Problem (second half of the genetic code) Structure has to be determined experimentally
  • 30.
    Protein Structure 30 • Levels of organization – Primary Sequence – Secondary Structure (Modular building blocks) • α-helices • β-sheets – Tertiary Structure – Quartenary Structure • Hydrophobic/Hydrophilic Organization. Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 31.
    Protein Structure 31 Lubert S,Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 32.
    Secondary Structure: -helix 32 LubertS, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 33.
    Secondary Structure: -sheets 33 LubertS, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 34.
    Definition of -turn 34 four consecutive residues i, i+1, i+2 and i+3 that do not form a helix and the turn lead to reversal in the protein chain. The conformation of -turn is defined in terms of two central residues, i+1 and i+2 and can be classified into different types on the basis of this conformation. i+1 i+2 i H- i+3 bond D <7Å Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 35.
    Biology/Chemistry of ProteinStructure 35 Primary Assembly STRUCTURE PROCESS Secondary Folding Tertiary Packing Quaternary Interaction Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230.
  • 36.
    3 main questions… 36 1. Why predict the structure? 2. Methods for structure prediction 3. What next?
  • 37.
    Purpose of PSP 37 Explaining phenotype of existing mutations (experimental or patient-derived) Designing mutants to disrupt or alter specific functions (leaving others unaffected) Hints at function Drug design (at high sequence identity) Hypothesis generation Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
  • 38.
    • Anfinsen’s (1973)thermodynamic hypothesis: 38 Proteins are not assembled into their native structures by a biological process, but folding is a purely physical process that depends only on the specific amino acid sequence of the protein. Anfinsen C B(1973), Principles that govern the folding of protein chains, Biological Science, 181, pp. 223–230
  • 39.
    The Prediction Problem 39 Can we predict the final 3D protein structure knowing only its amino acid sequence? • Studied for 4 Decades • “Holy Grail” in Biological Sciences • Primary Motivation for Bioinformatics • Based on this 1-to-1 Mapping of Sequence to Structure • Still very much an OPEN PROBLEM Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
  • 40.
    PSP: Major Hurdles 40  Energetics  We don‟t know all the forces involved in detail  Too computationally expensive BY FAR!  Conformational search impossibly large  100 AA. protein, 2 moving dihedrals, 2 possible positions for each diheral: 2200 conformations!  Levinthal‟s Paradox  Longer than time of universe to search  Proteins fold in a couple of seconds?? Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
  • 41.
    PSP: Goals 41  Accurate 3D structures. But not there yet.  Good “guesses”  Working models for researchers  Understand the FOLDING PROCESS  Get into the Black Box  Only hope for some proteins  25% won‟t crystallize, too big for NMR  Best hope for novel protein engineering  Drug design, etc. Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science Business Media, 12th edition, pp. 21-32
  • 42.
    Comparative Modeling--Basic Protocol 42 1. Identification of homologue for target sequence 2. Alignment of target sequence to template sequence and structure 3. Side-chain modeling, copy the backbone of the template and model the new side chains onto this backbone 4. Loop modeling, for insertions and deletions in the alignment 5. Refinement of model -- moving template closer to target 6. Assessment of (predicted) model quality 7. Using the model to explain experiments and guide new ones David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60.
  • 43.
    Experimental techniques forstructure determination 43  X-ray Crystallography  Nuclear Magnetic Resonance spectroscopy (NMR)  Electron Microscopy/Diffraction  Free electron lasers. David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60.
  • 44.
    X-ray Crystallography 44  From small molecules to viruses  Information about the positions of individual atoms  Limited information about dynamics  Requires crystals Saville W B, Shearer G (1925), An X-ray Investigation of Saturated Aliphatic Ketones, Journal of the Chemical Society, 127, pp. 591.
  • 45.
    NMR 45  Limited to molecules up to ~50kDa (good quality up to 30 kDa)  Distances between pairs of hydrogen atoms  Lots of information about dynamics  Requires soluble, non-aggregating material  Assignment problem Addess M, Kenneth J, Feigon J(1996). Introduction to 1H NMR Spectroscopy of DNA. Bioorganic Chemistry: Nucleic Acids, Oxford University Press, 8th edition, pp. 238.
  • 46.
    Electron Microscopy/ Diffraction 46  Low to medium resolution  Limited information about dynamics  Can use very small crystals (nm range)  Can be used for very large molecules and complexes
  • 47.
    Tertiary Structure Prediction 47  Template Modeling  Homology Modeling  Threading  Template-Free Modeling  ab initio Methods  Physics-Based  Knowledge-Based Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288.
  • 48.
    HOMOLOGY MODELING 48  Constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental 3d structure of a related homologous protein (the "template").  Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence  This approach can be complicated by the presence of alignment gaps (commonly called indels) that arise from poor resolution in the experimental procedure (usually X-ray crystallography). Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
  • 49.
    HOMOLOGY MODELING 49  Homology modeling can produce high-quality structural models when the target and template are closely related, which has inspired the formation of a structural genomics consortium.  The analysis and prediction of loop structures for small and medium sized loops and the positioning of side chains, given the conformation of the protein's backbone. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
  • 50.
    Threading or FoldRecognition Method 50  Computational protein structure prediction  Distinction between two fold recognition scenarios.  “Threading" (i.e. placing, aligning) each amino acid in the target sequence to a position in the template structure, and evaluating how well the target fits the template. After the best-fit template is selected, the structural model of the sequence is built based on the alignment with the chosen template.  Homologous folds share the Same structure through divergent evolution from a common ancestor.  Analogous folds, on the other hand, share the same structure, but give insufficient evidence for an evolutionary relationship. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
  • 51.
    Threading or FoldRecognition Method 51  One popular model for protein folding assumes a sequence of events:  Hydrophobic collapse  Local interactions stabilize secondary structures  Secondary structures interact to form motifs  Motifs aggregate to form tertiary structure Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
  • 52.
    Ab-initio method 52  It calculates energetics involved in the process of folding  Finding the structure with lowest free energy  It is based on the „thermodynamic hypothesis‟, which states that the native structure of a protein is the one for which the free energy achieves the global minimum.  2 components to ab initio prediction: 1. devising a scoring (ie, energy) function that can distinguish between correct (native or native-like) structures from incorrect ones. 2. a search method to explore the conformational space.  The most difficult, but most useful approach. Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88.
  • 53.
    Ab-initio method 53 Sequence Prediction Secondary structure Low Tertiary Validation Predicted energy structure Energy Mean field structure structures Minimization potentials Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88.
  • 54.
    Secondary Structure Prediction 54 • Existing SSP Methods • Statistical Methods (Chou,GOR) • Physio-chemical Methods • A.I. (Neural Network Approach) • Consensus and Multiple Alignment • Our Method APSSP of SSP • Neural Network • Example Based Learnning • Multiple Alignment • Steps involved in APSSP • Blast search against protein sequence (NR) • Multiple Alignment (ClustalW) • Profile by HMMER, Result by Email • Recogntion: CASP,CAFASP,LiveBench, MetaServer Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288
  • 55.
    Web servers forstructure prediction 55 JPRED:-http://www.compbio.dundee.ac.uk/~www-jpred/ PHD:-http://cubic.bioc.columbia.edu/predictprotein/ PSIPRED:-http://bioinf.cs.ucl.ac.uk/psipred/ Chou and Fassman:- http://fasta.bioch.virginia.edu/fasta_www/chofas.htm
  • 56.
    Future technologies 56 Modeling of biologically relevant states of proteins using all available templates Homooligomers Heterooligomers Amino acid modifications Bound ligands (small molecules, nucleic acids) Modeling of specific classes of proteins Antibodies Repeat proteins (ARM/HEAT, WD repeats) Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
  • 57.
    Available databases, software,and services 57 Rotamer library ProtBuD -- biological units database across families PISCES -- non-redundant sequences in PDB MolIDE 1.5 ArboDraw -- drawing phylogenetic trees BioDownloader -- automatic updating of biological databases Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
  • 58.
    Assessment of accuracyof PSP 58 P = (N – total incorrect) N total incorrect = total number of residues whose conformations are predicted incorrectly N = the number of residues in the protein. Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198.
  • 59.
    Applications of PSP: 59  Drug targetting.  Pharmacogenetics.  Pharmacogenomics.  MOA.  Dosage regimen.
  • 60.
    Conclusion 60  Pharmacist  Biotechnologis t Molecular modeling
  • 61.
    Conclusion 61  Pharmacist  Biotechnologis t Protein structure prediction
  • 62.
    References: 62 1. Lubert S, Tymoczko J L, Jeremy M B(2003). Text book of biochemistry, W. H. Freeman and company press, 5th edition, pp. 198-230. 2. Thomas L L, David A W, Victoria K(1999), Foye‟s principles of Medicinal Chemistry, Lippincott Williams & Wilkins publications, 6th edition, 3, pp. 55-63. 3. Mateusz K, Michał J, Andrzej K(2011), Multiscale Approaches to Protein Modeling, Springer Science+Business Media, 12th edition, pp. 21-32. 4. Zhan Y Z, Tom L B(1996), The Use of Amino Acid Patterns of Classified Helices and Strands in Secondary Structure Prediction, Journal of Molecular biology, 260, pp. 61–76. 5. Schween G, Egener T, Fritzkowsky D, Granado J, Guitton M C(2005), Large-scale analysis of Physcomitrella plants transformed with different gene disruption libraries: Production parameters and mutant phenotypes, Plant Biology, 7 (3), pp. 228–237. 6. David F B, Charlotte M D, Hampapathalu A N, Nuria C, An Iterative Structure-Assisted Approach to Sequence Alignment and Comparative Modeling, PROTEINS: Structure, Function, and Genetics Supplementations, 3, pp. 55-60. 7. Thomas L, Ralf Z(2000), Protein structure prediction methods for drug design, Briefings in Bioinformatics, 3, pp. 275-288. 8. Anfinsen C B(1973), Principles that govern the folding of protein chains, Biological Science, 181, pp. 223–230.
  • 63.
    References: 63 9. Richard B, David B(2001), AB INITIO PROTEIN STRUCTURE PREDICTION: Progress and Prospects, Annual review of biophysical and biomolecular structures, 30, pp. 73-88. 10. Saville W B, Shearer G (1925), An X-ray Investigation of Saturated Aliphatic Ketones, Journal of the Chemical Society, 127, pp. 591. 11. Addess M, Kenneth J, Feigon J(1996). Introduction to 1H NMR Spectroscopy of DNA. Bioorganic Chemistry: Nucleic Acids, Oxford University Press, 8th edition, pp. 238. 12. Ram S, Yu Xia, Enoch H, Michael L(1999), Ab Initio Protein Structure Prediction Using a Combined Hierarchical Approach, PROTEINS: Structure, Function, and Genetics, 3, pp. 194–198. 13. Rajkumar B, Branson K, Giddy J, Abramson D(2003), The Virtual Laboratory : A toolset to enable distributed molecular modeling for drug design on the World-Wide Grid, Concurrency and computation, 15, pp. 1–25. 14. Griffith S, David J(2004), Introduction to Quantum Mechanics, Prentice Hall press, 2nd edition, 1, pp. 1-4. 15. Leach A R(2001), Molecular Modelling: Principles and Applications, Oxford press, 4th edition, 1, pp. 1-3. 16. LEONOR C H, PAULO A S(2001), Protein folding : thermodynamic versus kinetic control, Journal of biological physics, 27, pp. 6-8.
  • 64.
    Useful links: 64 Date: 28/10/2012. 1. http://dunbrack.fccc.edu 2. http://courses.washington.edu/conj/protein/insulin2.gif 3. http://www.biochem.ucl.ac.uk/bsm/pdbsum/1gwf/main.html 4. http://opbs.okstate.edu/~petracek/2002%20protein%20structure%20function/CH06.gif 5. http://my.webmd.com/hw/health_guide_atoz/zm2662.asp?printing=true 6. http://www.grin.com/object/external_document.274822/5fbac5ddfea3cb2dd3dde8ad8ee98 1f9_LARGE.png
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