Computational method on biochemistry

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Computational method on biochemistry

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• Protein Structure and Dynamics
• Bioinformatics
• Comparative modeling
• Other method

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Protein structure and dynamics

• Time scale in biological phenomena
• Newtonian mechanics
• Force field
• CHARMM
• AMBER
• Energy minimization
• Molecular Dynamics
• Example

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Time scale in biological phenomena
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ns
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ps
fs
hr
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Force field

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• ? ?? ??? ??? ???? ?? ???? ???? ?? ????? ?????
  ???? ??? ??.

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Newtonian mechanics

• Fma
• vv0atf(t)
• sv0tat2/2g(t)
• Emv2/2

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Energy minimization
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Energy minimization
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Molecular Dynamics
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Molecular Dynamics

• EtotEpotEkin

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CHemistry at HARvard Macromolecular Mechanics

• CHARMm forcefields
• CHARMm, which derives from CHARMM (CHemistry at
  HARvard Macromolecular Mechanics), is a highly
  flexible molecular mechanics and dynamics program
  originally developed in the laboratory of Dr.
  Martin Karplus at Harvard University. It was
  parameterized on the basis of ab initio energies
  and geometries of small organic models.
• Applicability
• CHARMm performs well over a broad range of
  calculations and simulations, including
  calculation of geometries, interaction and
  conformation energies, local minima, barriers to
  rotation, time-dependent dynamic behavior, free
  energy, and vibrational frequencies (Momany
  Rone, 1992). CHARMm is designed to give good (but
  not necessarily "the best") results for a wide
  variety of modelled systems, from isolated small
  molecules to solvated complexes of large
  biological macromolecules however, it is not
  applicable to organometallic complexes.

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Assisted Model Building with Energy Refinement

• AMBER forcefield
• The standard AMBER forcefield (Weiner et al.
  1984, 1986) is parameterized to small organic
  constituents of proteins and nucleic acids. Only
  experimental data were used in parameterization.
• However, AMBER has been widely used not only for
  proteins and DNA, but also for many other classes
  of models, such as polymers and small molecules.
  For the latter classes of models, various authors
  have added parameters and extended AMBER in other
  ways to suit their calculations. The AMBER
  forcefield has also been made specifically
  applicable to polysaccharides (Homans 1990, and
  see Homans' carbohydrate forcefield).
• AMBER is used mainly for modeling proteins and
  nucleic acids. It is generally lower in accuracy
  and has a limited range of applicability. The use
  of AMBER is recommended mainly for those
  customers who are familiar with AMBER and have
  developed their own AMBER-specific parameters. It
  generally gives reasonable results for gas-phase
  model geometries, conformational energies,
  vibrational frequencies, and solvation free
  energies.

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Application

• protein motion
• protein folding
• enzyme mechanism
• model optimization

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In silico protein folding
1us1,000,000,000 fs(or step) 644 step/sec on 256
CPUs CRAY machine
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Simulation of the travel of potassium
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Bioinformatics

• Introduction
• Sequence alignment
• Pairwise sequence alignment
• BLAST
• Multiple sequence alignment
• CLUSTALW
• T-COFFEE
• Scoring matrix
• Structure Alignment
• Example

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Pairwise alignment

• Smith-Waterman Algorithm
• BLAST local alignment
• FASTA global alignment

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Smith-Waterman Algorithm
Align S1ATCTCGTATGATG S2GTCTATCAC
0
0
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?1, ?1
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BLAST

• Basic Local Alignment Search Tool
• Altschul, S.F., Gish, W., Miller, W.,
• Myers, E.W. Lipman, D.J.
• Journal of Molecular Biology
• v. 215, 1990, pp. 403-410
• Used to search sequence databases for local
  alignments to a query

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BLAST algorithm

• Keyword search of all words of length w from the
  in the query of length n in database of length m
  with score above threshold
• w 11 for nucleotide queries, 3 for proteins
• Do local alignment extension for each found
  keyword
• Extend result until longest match above threshold
  is achieved
• Running time O(nm)

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BLAST algorithm (contd)
keyword
Query KRHRKVLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVL
KIFLENVIRD
GVK 18 GAK 16 GIK 16 GGK 14 GLK 13 GNK 12 GRK
11 GEK 11 GDK 11
Neighborhood words
neighborhood score threshold (T 13)
extension
Query 22 VLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLK
60 DN G IR L GK I L E
RGK Sbjct 226 IIKDNGRGFSGKQIRNLNYGIGLKVIADLV-EK
HRGIIK 263
High-scoring Pair (HSP)
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Original BLAST

• Dictionary
• All words of length w
• Alignment
• Ungapped extensions until score falls below some
  threshold
• Output
• All local alignments with score gt statistical
  threshold

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Original BLAST Example
A C G A A G T A A G G T C
C A G T

• w 4
• Exact keyword match of GGTC
• Extend diagonals with mismatches until score is
  under 50
• Output result
• GTAAGGTCC
• GTTAGGTCC

C T G A T C C T G G A T T
G C G A
From lectures by Serafim Batzoglou (Stanford)
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ClustalW

• Popular multiple alignment tool today
• Several heuristics to improve accuracy
• Sequences are weighted by relatedness
• Scoring matrix can be chosen on the fly
• Position-specific gap penalties

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ClustalW (contd)

• Often used for protein alignment
• W stands for weighted
• Different parts of alignment are weighted.
• Position/residue specific gap penalties.
• Three-step process
• 1.) Pairwise alignment
• 2.) Build Guide Tree
• 3.) Progressive Alignment

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Step 1 Pairwise Alignment

• Aligns each sequence again each other giving a
  distance matrix
• Distance exact matches / sequence length
  (percent identity)

(.17 means 17 identical)
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Step 2 Guide Tree

• Create Guide Tree using the distance matrix
• ClustalW uses the neighbor-joining method
• Guide tree roughly reflects evolutionary relations

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Step 2 Guide Tree (contd)
S1 S3 S4 S2
Calculates1,3 consensus(s1, s3)s1,3,4
consensus((s1,3),s4)s1,2,3,4
consensus((s1,3,4),s2)
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Step 3 Progressive Alignment

• Align the two most similar sequences
• Following the guide tree, add in the next
  sequences, aligning to the existing alignment
• Insert gaps as necessary

Sample output FOS_RAT
PEEMSVTS-LDLTGGLPEATTPESEEAFTLPLLNDPEPK-PSLEPVKNIS
NMELKAEPFD FOS_MOUSE PEEMSVAS-LDLTGGLPEASTPE
SEEAFTLPLLNDPEPK-PSLEPVKSISNVELKAEPFD FOS_CHICK
SEELAAATALDLG----APSPAAAEEAFALPLMTEAPPAVPPKEPS
G--SGLELKAEPFD FOSB_MOUSE PGPGPLAEVRDLPG-----
STSAKEDGFGWLLPPPPPPP-----------------LPFQ FOSB_HUM
AN PGPGPLAEVRDLPG-----SAPAKEDGFSWLLPPPPPPP---
--------------LPFQ . . .
.. . .
Dots and stars show how well-conserved a column
is.
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Scoring Matrix

• BLOSUM
• PAM
• PSSM

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PAM

• Percentage of Acceptable point Mutations per 108
  years
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• matrices are based on global alignments of
  closely related proteins. The PAM 1 is the matrix
  calculated from comparisons of sequences with no
  more than 1 divergence. Scores are derived from
  a mutation probability matrix where each element
  gives the probability of the amino acid in column
  X mutating to the amino acid in row Y after a
  particular evolutionary time, for example after 1
  PAM, or 1 divergence. A PAM matrix is specific
  for a particular evolutionary distance, but may
  be used to generate matrices for greater
  evolutionary distances by multiplying it
  repeatedly by itself. However, at large
  evolutionary distances the information present in
  the matrix is essentially degenerated. It is rare
  that a PAM matrix would be used for an
  evolutionary distance any greater than 256 PAMs.

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BLOSUM

• Local alingment? ???? ?? ??
• BLOcks SUbstitution Matrix
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  scoring matrix??
• BLOSUM 62? ??? 62 ??? ???? ??? ??? ?

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Position Specific Scoring Matrix

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  ??? ???
• PSI-BLAST?? ???? ??
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Homology/Comparative modeling

• Introduction
• Method
• Example

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Introduction

• ??? ??? ?? ???? ??? ??? ??? ??.
• Ex) hemoglobin/myoglobin, ubiquitin/ubiquitin
  like proteins. Serine proteases,
  thioredoxin/glutaredoxin

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Method

1. 30 ??? homology? ?? ??? ? ??? ?? ? ??
2. Pairwise or multiple sequence alignment
3. Alignment? ???? ??? ???? distance constraint??.
4. Model ???

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Example Modeling of malonly-CoA synthetase
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Firefly luciferase
Malonyl-CoA synthetase
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Other Methods

• Simulated Annealing
• Monte Carlos method
• Docking

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