forces and prediction of protein structure ming-jing hwang ( 黃明經 ) institute of biomedical...

Forces and Prediction of Protein Structure

Ming-Jing Hwang ( 黃明經 )Institute of Biomedical SciencesAcademia Sinica

http://gln.ibms.sinica.edu.tw/

Science 2005

Sequence - Structure - Function

MADWVTGKVTKVQNWTDALFSLTVHAPVLPFTAGQFTKLGLEIDGERVQRAYSYVNSPDNPDLEFYLVTVPDGKLSPRLAALKPGDEVQVVSEAAGFFVLDEVPHCETLWMLATGTAIGPYLSILR

Sequence/Structure Gap Current (May 15, 2007) entries in protein sequence and structure

database:

SWISS-PROT/TREMBL : 267,354/4,361,897 PDB : 43,459

Year

Num

ber of

ent

ries

Sequence

Structure

Structural Bioinformatics: Sequence/Structure Relationship

All possible sequences of amino acids

Protein sequences observed in nature

Protein structures observed in nature

100

90

80

70

60

50

40

30

20

10

0

Percent Identity

Twilight zoneMidnight zone

Structure Prediction Methods

0 10 20 30 40 50 60 70 80 90 100

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 3200. Even an incredibly fast computational or physical sampling in 10-15 s would mean that a complete sampling would take 1080 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*

Levitt

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

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Levitt

Molecular Mechanics (Force Field)

Levitt

1-microsecond MD simulation980ns

- villin headpiece - 36 a.a.- 3000 H2O- 12,000 atoms- 256 CPUs (CRAY)-~4 months- single trajectory

Duan & Kollman, 1998

Protein folding by MDPROTEIN 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 SETI@home: Folding@home Distributed folding Sengent’s drug design FightAIDS@home …

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- Folding@home- 30,000 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 techniqueCGU: 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

0 10 20 30 40 50 60 70 80 90 100

ab initio

Fold recognition

% sequence identity

Homology modeling

Flowchart of homology (comparative) modeling

From Marti-Renom et al.

Fold recognitionFind, from a library of folds, the 3D templatethat 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 3DLGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAYSISAYVQVQSTNNCISGTEACRHLTNLVNHGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC

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

The prediction of protein structure from amino acid sequence is a grand challenge of computational molecular biology. By using a combination of improved low- and high-resolution conformational sampling methods, improved atomically detailed potential functions that capture the jigsaw puzzle–like packing of protein cores, and high-performance computing, high-resolution structure prediction (<1.5 angstroms) can be achieved for small protein domains (<85 residues). The primary bottleneck to consistent high-resolution prediction appears to be conformational sampling.

Toward High-Resolution de Novo Structure Prediction for Small Proteins --Philip Bradley, Kira M. S. Misura, David Baker (Science 2005)

Science 2003

3D to 1D?

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

Structure prediction servers

http://bioinfo.pl/cafasp/list.html

Hybrid approach for solving macromolecular complex structures

Thank You!