Forces and prediction of protein structure
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Forces and Prediction of Protein Structure. Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica. http://gln.ibms.sinica.edu.tw/. Sequence - Structure - Function.

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Forces and Prediction of Protein Structure

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Forces and Prediction of Protein Structure

Ming-Jing Hwang (黃明經)

Institute of Biomedical Sciences

Academia 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

Sequence

Structure


Structure Prediction Methods

Homology modeling

Fold recognition

ab initio

0 10 20 30 40 50 60 70 80 90 100

% sequence identity


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 simulation

980ns

  • villin headpiece

  • 36 a.a.

  • 3000 H2O

  • 12,000 atoms

  • 256 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

  • [email protected]:

  • [email protected]

  • Distributed folding

  • Sengent’s drug design

  • [email protected]


Massively distributed computing

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 ms

  • [email protected]

  • 30,000 internet volunteers

  • several months, or ~a million CPU days of simulation


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

Homology modeling

Fold recognition

ab initio

0 10 20 30 40 50 60 70 80 90 100

% sequence identity


Flowchart of homology (comparative) modeling

From 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)


Reliability and uses of comparative models

Marti-Renom et al. (2000)


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

Primary

LGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAYVQSTNNCISGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC

seq. to str. mapping

Secondary(fragment)

Tertiary

fragment assembly


CASP Experiments


One group dominates the ab initio (knowledge-based) prediction

One lab dominated in CASP4


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!


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