Forces and Prediction of Protein Structure Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica

Sequence - Structure - Function MADWVTGKVTKVQ NWTDALFSLTVHAP VLPFTAGQFTKLGLE IDGERVQRAYSYVN SPDNPDLEFYLVTVP DGKLSPRLAALKPG DEVQVVSEAAGFFV LDEVPHCETLWMLA TGTAIGPYLSILR

Sequence/Structure Gap Current (May 26, 2005) entries in protein sequence and structure database:  SWISS-PROT/TREMBL : 181,821/1,748,002  PDB : 31,059 Sequence Structure

Structure Prediction Methods ab initio Fold recognition % sequence identity Homology modeling

Levinthal’s paradox (1969) If we assume three possible states for every flexible dihedral angle in the backbone of a 100-residue protein, the number of possible backbone configurations is Even an incredibly fast computational or physical sampling in s would mean that a complete sampling would take s, which exceeds the age of the universe by more than 60 orders of magnitude. Yet proteins fold in seconds or less! Berendsen

Energy landscapes of protein folding Borman, C&E News, 1998

Levitt ’ s lecture for S*S*

Levitt

Other factors Formation of 2nd elements Packing of 2nd elements Topologies of fold Metal/co-factor binding Disulfide bond …

Ab initio/new fold prediction Physics-based (laws of physics) Knowledge-based (rules of evolution)

Levitt

Molecular Mechanics (Force Field)

Levitt

1-microsecond MD simulation 980ns - villin headpiece - 36 a.a H2O - 12,000 atoms CPUs (CRAY) -~4 months - single trajectory Duan & Kollman, 1998

Protein folding by MD PROTEIN FOLDING: A Glimpse of the Holy Grail? Herman J. C. Berendsen * * "The Grail had many different manifestations throughout its long history, and many have claimed to possess it or its like". We might have seen a glimpse of it, but the brave knights must prepare for a long pursuit.

Massively distributed computing Distributed folding Sengent’s drug design …

Letters to nature (2002) - engineered protein (BBA5) - zinc finger fold (w/o metal) - 23 a.a. - solvation model - thousands of trajectories each of 5-20 ns, totaling 700  s ,000 internet volunteers - several months, or ~a million CPU days of simulation Massively distributed computing

Energy landscapes of protein folding Borman, C&E News, 1998

Protein-folding prediction technique CGU: Convex Global Underestimation - K. Dill ’ s group

Challenges of physics-based methods Simulation time scale Computing power Sampling Accuracy of energy functions

Structure Prediction Methods ab initio Fold recognition % sequence identity Homology modeling

Flowchart of homology (comparative) modeling From Marti-Renom et al.Marti-Renom et al.

Fold recognition Find, from a library of folds, the 3D template that accommodates the target sequence best. Also known as “ threading ” or “ inverse folding ” Useful for twilight-zone sequences

Fold recognition (aligning sequence to structure) (David Shortle, 2000)

3D->1D score

On X-ray, NMR, and computed models

(Rost, 1996)

Marti-Renom et al. (2000) Reliability and uses of comparative models

Pitfalls of comparative modeling Cannot correct alignment errors More similar to template than to true structure Cannot predict novel folds

Ab initio/new fold prediction Physics-based (laws of physics) Knowledge-based (rules of evolution)

From 1D  2D  3D SISAY VQGTEACRHLTNLVNH LGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAY VQSTNNCISGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC Tertiary Primary Secondary (fragment) fragment assembly seq. to str. mapping

CASP Experiments

One lab dominated in CASP4 One group dominates the ab initio (knowledge-based) prediction

Some CASP4 successes Baker ’ s group

Ab initio structure prediction server

Science 2003

A computer-designed protein (93 aa) with 1.2 A resolution

Structure prediction servers

Thank You!