1. Ab-initio protein structure prediction ? Chen Keasar BGU Any educational usage of these slides is welcomed. Please acknowledge. keasar@cs.bgu.ac.il

2. ……TVFAIYDYDFK….. …… TEDDAGSFHEK …… …… TLUNSGDGDWW …… …… TGYVGSSYV …… The problem: Predict the three dimensional structure of a protein based on its sequence. ? ? ? ? ? ? Chen Keasar BGU

3. How can we predict protein structures? Are we lucky? yes A V C W K A G K C AC WKA VGKC C + A V C W K A G K C C homology no ab initio a bit fold recognition Chen Keasar BGU

4. Why is ab-initio prediction hard? Chen Keasar BGU

5. Ab-initio is hard, why do it? Wait until enough proteins are solved and use homology modeling/fold-recognition Chen Keasar BGU

6. Because it’s there Chen Keasar BGU

7. Because homology modeling tells us nothing about the physical nature of the protein folding and stability. Because ab-initio methods can augment fold- recognition and homology (refinement, large loops, side chains). Because of ORFans (orphan ORFs). Because it can ease experimental structure determination. Because prediction is the basis of design. Chen Keasar BGU

8. ab-initio protein structure prediction Simulation of the actual folding process 1.Build an accurate initial model (including energy and forces). 2.Accurately simulate the dynamics of the system. 3.The native structure will emerge. 4. Optimization problem 1.Define some initial model. 2.Define a function mapping structures to numerical values (the lower the better). 3.Solve the computational problem of finding the global minimum. 4. Chen Keasar BGU

9. Simulating the actual folding process dimer a CHOOH Model I – quantum description of the system Chen Keasar BGU

10. Model II Semi-empirical energy functions – forcefields  Classic world no quantum effects (that is no chemistry).  Parameterized to reproduce experimental results for small molecules. Their use for proteins is an extrapolation.  The basic element is an atom: Unbreakable. Represented by the X,Y,Z coordinates of its center. Its attributes (volume, charge, mass etc.) are the basic parameters of the energy function. Chen Keasar BGU

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15. The good news The model is rather accurate and correctly describe many natural phenomena. The bad news Each time step is hard to compute. An order of 10 12 steps are needed to simulate protein folding.  Chen Keasar BGU

16. conformation energy Ab-initio protein structure prediction as an optimization problem 2.Solve the computational problem of finding an optimal structure. 3. 1.Define a function that map protein structures to some quality measure. Chen Keasar BGU

17. A dream function Has a clear minimum in the native structure. Has a clear path towards the minimum. Global optimization algorithm should find the native structure. Chen Keasar BGU

18. An approximate function Easier to design and compute.  Native structure not always the global minimum.  Global optimization methods do not converge. Many alternative models (decoys) should be generated. Chen Keasar BGU

19. An approximate function Easier to design and compute.  Native structure not always the global minimum.  Global optimization methods do not converge. Many alternative models (decoys) should be generated.  No clear way of choosing among them. Decoy set Chen Keasar BGU

20. Energy functions:  Typically include terms for hydrophobicity, hydrogen bonds etc.  Typically based on the distribution of structural features (say contacts between alanine residues and arginine residues) in the PDB. The more frequent is the feature the lower is the energy associated with it. A small problems – these assumptions are wrong. A brilliant solution – ignore it. Assumptions:  These features are independent.  The proteins in the PDB are a representative sample of conformation space. Chen Keasar BGU

21. diamond lattice fine square lattice fragments continuous Some residues Basic element residue heavy atom atom half a residue Not really Ab-initio torsion angle lattice electrons & protons Hinds & Levitt Skolnik 2000 Skolnik 1998 Scheraga 1998 Baker (Rosetta) Levitt & Keasar AMBR ECEP CHARM OPLS ENCAD GROMOS Levitt 1976 Osguthorpe Jones Park & Levitt Chen Keasar BGU

22. diamond lattice fine square lattice fragments continuous Some residues Basic element residue extended atom atom half a residue torsion angle lattice electrons & protons Hinds & Levitt Chen Keasar BGU

23. diamond lattice fine square lattice fragments continuous Some residues Basic element residue extended atom atom half a residue torsion angle lattice electrons & protons Park & Levitt Chen Keasar BGU

24. diamond lattice fine square lattice fragments continuous Some residues Basic element residue extended atom atom half a residue torsion angle lattice electrons & protons Skolnik 2000 Chen Keasar BGU

25. diamond lattice fine square lattice fragments continuous Some residues Basic element residue extended atom atom half a residue torsion angle lattice electrons & protons Scheraga 1998 Chen Keasar BGU

26. diamond lattice fine square lattice fragments continuous Some residues Basic element residue extended atom atom half a residue torsion angle lattice electrons & protons Hinds & Levitt Skolnik 2000 Skolnik 1998 Scheraga 1998 Baker (Rosetta) Levitt, Keasar AMBR ECEP CHARM OPLS ENCAD GROMOS Levitt 1976 Osguthorpe Jones Park & Levitt Chen Keasar BGU Apparently the best current method