Title: Reductionism and Classification Require Detailed Comparison Consider 3D Comparison
1Reductionism and Classification Require Detailed
ComparisonConsider 3D Comparison
- Pharm 201/Bioinformatics I
- Philip E. Bourne
- Department of Pharmacology, UCSD
- Reading Chapter 16, Structural Bioinformatics
-
2Consider this Course a Workflow
Understand the experiment to understand
the errors
Data In
Understand the scope and complexity of the data
Understand how to best represent (model) the data
Understand the methods to physically
instantiate the model
From initial analysis understand how to
control data in
Recognize redundancy In the data
Classify the data
Visualize the data
Analyze the data
3From Last Time
- We established the complex relationship between
- Sequence and Structure
- Structure and Structure
- Structure and Function
- Today we analyze how the relationships between
structure and structure are established
4Agenda
- Understand why structure comparison is important
- Understand why it is not a solved problem
- Understand the basics of the methods used to
address the problem - Understand one method (CE) in more detail
- Review an example where structure comparison has
revealed new biological insights
5Why Structure Comparison is Important
- Reductionism needed to classify protein
structures - Functional assignment and hopefully new biology
- Alignment of predicted structure against
structural templates - Establish improved sequence relationships not
possible from sequence alone - Protein engineering
6Distinctions - Structure Superposition and
Structure Comparison and Alignment are Different
- Structure superposition assumes you already know
which atoms to superimpose it merely optimizes
for the atoms chosen (relatively simple) - Structure alignment must first determine what
atoms to align (difficult). We are concerned with
alignment
7Distinctions Pair-wise Alignments are Different
from Multiple Structure Alignments
- Multiple structure alignment algorithms are rare
and of questionable quality (see for example
Nucleic Acids Research (2004), 32 W100-W103 - Multiple structure alignments should not be
confused with multiple pair-wise alignments - Here we focus on single pair-wise comparison and
alignment
8Why is it Not a Solved Problem?
9Current State of the Art
- There are many papers published on this, but
relatively few have code to download or Web sites
from which to perform comparisons - All methods can identify obvious similarities
between two structures - Remote similarities are detected by a subset of
methods different remote similarities are
recognized by different methods - Good alignments are much harder to come by
- Speed is a serious issue with some algorithms
10Desirables
- Biologically meaningful alignments not just
geometrically meaningful - Complete database of all alignments
- Ability to apply to structures not in the PDB
11Biological vs Geometric Alignments Plastocyanin
versus Azurin (from Godzik 1996)
Maintain 9 of 10 interactions RMSD 1.5 Å
Maintain 5 of 10 interactions RMSD 0.5 Å
12Literature Alignments - Flavodoxin vs Che Y
Protein From Godzik (1996) Protein Science, 5,
1325-1338.
13Understand the basics of the methods used to
address the problem
14See also http//en.wikipedia.org/wiki/Structural_a
lignment_software
15How to Compare Structures?
Structure 1
Structure 2
Feature extraction
1.
Structure description 1
Structure description 2
Comparison algorithm
2.
3.
Scores
Statistical significance
Similarity, classification
16Components of Structure Alignment
- Local geometry
- Side chain contacts
- Geometric hashing
- Distance matrix (Dali, 1993)
- Properties (secondary structure, hydrophobic
clusters (Comparer, 1990) - Secondary structure elements (VAST, 1996)
- Distances of inter intra aligned fragment
pairs (CE, 1998) - Contact map (Celera, 2004)
- Geometry invariants (Jia et al, 2004)
17Components of Structure Alignment
- 2. Alignment algorithms
- Monte Carlo (Dali, VAST)
- Heuristics (CE)
- Dynamic Programming (CE)
- Probabilistic
- Statistical significance
18Components of Structure Alignment
2. Alignment algorithms
- Input output of alignment algorithm
- Input two proteins and
- Output An alignment
- and scores
- Constraints
- min rmsd
- max L
- min Gaps
- Dynamic programming, Integer programming, Monte
Carlo
3. Statistical significance
- Levitt and Gerstein, PNAS, 1998
- Random Model and CE scoring function (Jia et al,
2004)
19Understand one method (CE) in more detail
- I.N. Shindyalov and P.E. Bourne Protein
Engineering 1998, 11(9) 739-747. Protein
Structure Alignment by Incremental Combinatorial
Extension of the Optimum Path. PDF File 793
citations!
20Basic Approach
- Compare octameric fragments an aligned fragment
pair (AFP) (local alignments) - Stitch together AFPs
- Find the optimal path through the AFPs
- Optimize the alignment through dynamic
programming - Measure the statistical significance of the
alignment
21Why This Approach?Alignment Space is Very Large
and Must be Constrained Without Loosing
Meaningful Alignments
- Similarity Matrix S where
- S(nA-m).(nB-m)
- m Length of AFP
- nA Length of protein A
- This is very large to compute constraints are
needed
22(No Transcript)
23Definition of the Alignment Path
- pAi AFPs starting residue position in protein A
at the ith position - of the alignment path
- m longest continual path set as 8
- One of the conditions (1)-(3) should be satisfied
for 2 consecutive AFPs i - and i1 in the path
- 2 consecutive AFPs aligned without gaps
- Two consecutive AFPs with a gap in protein A
- Two consecutive AFPs with a gap in protein B
24Extension of the Alignment Path
Gap sizes are limited to G heuristically set as
30 residues
25Evaluation based upon the following three
distance similarity measures
1. Distance calculated from independent set of
inter-residue distances where each
distance is used only once - used for
combinations of 2 AFPs
2. Full set of inter-residue distances - used for
a single AFP
3. RMSD from least squares superposition - used
to select few best fragments
26Evaluation Based Upon the Following Three
Distance Similarity Measures
1. Distance calculated from independent set of
inter-residue distances where each
distance is used only once
2. Full set of inter-residue distances
3. RMSD from least squares superposition
27How to Extend the Path?
1. Consider all possible AFPs that extend the path
2. Consider only the best AFP
3. Use some intermediate strategy
28How to Extend the Path?
1. Consider all possible AFPs that extend the
path Computationally expensive
2. Consider only the best AFP Works well
with the right heuristics
3. Use some intermediate strategy
29What Heuristics?
Candidate AFPs are based upon (9) D0 3Å The
best AFP is based upon (10) D1 4Å The
decision to extend or terminate the path is based
upon (11)
30Z-Score
- Evaluate the probability of finding an alignment
path of the same length or smaller gaps and
distance from a random set of non-redundant
structures
31Optimization of the Final Path
The 20 best alignments with a Z score above 3.5
are assessed based on RMSD and the best kept.
This produces approx. one error in 1000
structures
Each gap in this alignment is assessed for
relocation up to m/2
Iterative optimization using dynamic programming
is performed using residues for the superimposed
structures
32Test Case Phycocyanin versus Colicin A
33Cyclin-dependent kinases Open (purple) Closed
(blue) Pavelitch et al. (1997)
34Limitations
- Will not find non-topological alignments (outside
the bounds of the dotted lines) - What are the correct units to be comparing?
- CE works on chains as we shall see in future
weeks domains are the correct units, but
definition of the domains is not straightforward
35Computation of All x All
- Took 11,748 chain in the PDB (1/98)
- Computed for 1868 representatives
- 24,000 Cray T3E processor hours
- Loaded pairwise alignments into
- database
361-2 Years Ago
- 40,000 proteins 70,000 chains
- 70,0002/2 30 seconds 2330 yrs
- Options
- Use a redundant set of chains
- Use parallel architectures
- D. Pekurovsky, I.N. Shindyalov, P.E. Bourne 2004
High Throughput Biological Data Processing on
Massively Parallel Computers. A Case Study of
Pairwise Structure Comparison and Alignment Using
the Combinatorial Extension (CE) Algorithm.
Bioinformatics, 20(12) 1940-1947 PDF.
37Now
- Using egrid to compute all by all for CE and
FatCat
38One Criteria for Redundancy
- Remove highly homologous chains
- The RMSD between two chains is less than 2Å
- The length difference between two chains is less
than 10 - The number of gap positions in alignment between
two chains is less than 20 of aligned residue
positions - At least 2/3 of the residue positions in the
represented chain are aligned with the
representing chain.
39Review example where structure comparison has
revealed new biological insights
40Example
- CE revealed putative Ca binding domain in
acetylcholinesterase - Sequence similarity to neuroligins predicts Ca
binding too confirmed experimentally - Members of the a/b hydrolase family bind Ca
which may be important for heterologous cell
associations
Structural similarity between Acetylcholinesterase
and Calmodulin found using CE (Tsigelny et al,
Prot Sci, 2000, 9180)
41The Future(also a general rule)
- Gold standards are important
- For structure comparison a human generated
alignment standard is important - Algorithms are then challenged to meet the
standard - Eventually those algorithms highlight problems
with the standard - The cycle continues