Post on 23-Jan-2016
description
Forces and Prediction of Protein Structure
Ming-Jing Hwang ( 黃明經 )Institute of Biomedical SciencesAcademia Sinica
http://gln.ibms.sinica.edu.tw/
Sequence - Structure - Function
MADWVTGKVTKVQNWTDALFSLTVHAPVLPFTAGQFTKLGLEIDGERVQRAYSYVNSPDNPDLEFYLVTVPDGKLSPRLAALKPGDEVQVVSEAAGFFVLDEVPHCETLWMLATGTAIGPYLSILR
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
Year
Num
ber of
ent
ries
Sequence
Structure
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
Science 2003
A computer-designed protein (93 aa) with 1.2 A resolution
Structure prediction servers
http://bioinfo.pl/cafasp/list.html
Thank You!