ProClust:

1
ProClust

• Improved clustering of protein sequences with an
  extended graph-based approach

Ying Jin, Jonathan Michael Nowacki Nov. 21, 2003
2
What in this presentation

• Papers
• SCOP a Structural Classification of Proteins
• database
• link
• Clustering protein sequences structure
  prediction by transitive homology
• link
• Improved clustering of protein sequences with an
  extended graph-based approach
• link

3
Part I

• SCOP a Structural Classification of Proteins
  database

4
The main idea

• A database that provides a detailed and
  comprehensive description of all known protein
  structures

5
The Novel

• The distinction between evolutionary
  relationships and those that arise from the
  physics and chemistry of proteins
• The classification of proteins in SCOP has been
  constructed by visual inspection and comparison
  of structures. Believed better than purely
  automatic methods.

6
The organizational Basics

• By three traits
• Family near evolutionary relationships
• Based on one of two criteria that imply having
  common evolutionary origin significant sequence
  similarity, and functional/structural similarity.
• Super Family far evolutionary relationships
• Low sequence identity, but whose structures and
  in many cases, functional features suggest that a
  common evolutionary origin is probable, i.e.
  variable and constant domains of immunoglobulins.
• Fold geometrical relationships
• If proteins have the same major secondary
  structures in the same arrangement and with the
  same topological connections.
• Others classes. Domain, PDB, literature reference

7
More on folds

• All-alpha essentially all alpha
• All-beta essentially all beta
• Alpha/beta mix of alpha and beta
• Alpha beta helices and strands are segregated
• Multi-domain no known homologues

8
PDB at a Glance

• The PDB structure entries, consisting of a
  collection of files having nondescript names,
  cannot be easily grasped in a biochemically
  meaningful context. Manually organizing the
  structures based on the descriptive information
  in the files is becoming less and less practical
  as the database expands. A chemically or
  biologically meaningful context can be provided
  by the user in the form of a search keyword (e.g.
  hemoglobin), but the range of available contexts
  cannot be predetermined from the database
  itself--users must know, in general, what they
  are looking for. Although searching is an
  extremely useful approach for locating specific
  PDB entries, the scope of the database is best
  ascertained by browsing a set of predetermined
  contexts. Useful contexts include molecular
  classes (e.g. "cytochrome"), secondary/tertiary
  structural classes (e.g. "globin fold")
  functional classes (e.g. "binding protein"),
  species of origin, and experimental determination
  method. The descriptive information in the PDB
  files is distributed between a set of fields
  (e.g. "HEADER").

9
Other advantages of PDB

• PDB entry viewer links PDB entries to various
  graphical view, external databases and SCOP
  itself.
• Links to
• images of structure
• Interactive molecular views
• Atomic co-ordinates
• Data on functional conformational changes
• Sequence data
• Homologues
• MEDLINE abstracts

10
Access Methods

• Main url
• http//scop.mrc-lmb.cam.ac.uk/scop/index.html
• Numerous mirrors
• Europe
• East Coast USA
• Japan
• Isreal
• Taiwan
• China
• Australia

11
The Root Down Method
12
Example Pic
13
Chime
14
Search Engine
15
3d Search
16
In Conclusion

• SCOP is an easy way to access data and images.
• SCOP has a powerful generic purpose interface to
  the PDB
• Excellent overview of the diversity of protein
  structures which can aid researchers and students
  alike.

17
Part II

• Clustering protein sequences structure
  prediction by transitive homology

18
Main Idea of the Paper

• A graph-based clustering approach using
  transitivity handling multi-domain proteins and
  cluster comparison algorithms.
• - determined all pair-wise similarities for
  the sequences in the SwissProt database using the
  Smith-Waterman local alignment algorithm
• - transformed the data into a directed graph
• vertices protein sequence
• directed edges sequence A to B if
• score(A, B)/ score(A, A) gt T
• - the clustering process using transitivity
• SCOP was used as an evaluation data set

19
Motivation

• Finding the three-dimensional structure of
  proteins is one of the fundamental problems in
  molecular biology.
• X-ray diffraction analysis cant keep up with the
  ever-increasing speed at which proteins are
  sequenced.
• Desirable method predict structure from the
  sequence data. The main idea
• The sequence similarity gt homology
• gt similar structure
• gt function virtue
• (Note same structure or function does not imply
  a common ancestor)

20
Motivation (cont.)

• The relation of sequence similarity obtained by
  pair-wise alignment.
• Rule-of-thumb is that 30 identity over aligned
  regions (T)
• A widely accepted approach
• Score(A, B) gt T, implies structural similarity of
  sequence A and B
• This is a sufficient, but not a necessary
  condition
• Example

21

• Histogram of pair-wise alignment scores for
  all pairs from the same super-family in the SCOP1
  data set

22

• Detecting those distant homologues, bringing
  light into the so-called twilight zone of low
  similarity.
• ? What other criteria can be used to identify
  remote homologues

23
Graph-based Approach

• A graph-based clustering approach using the
  transitivity concept.
• Transitivity
• In mathematics if AB and BC then AC
• In biology for given three sequences A, B and C,
  if A and B as well as B and C have a common
  ancestor, then A and C have a common ancestor

24
Use of Transitivity

• The concept of transitivity can be used to detect
  remote homologues.

However - It is not fully understood
if transitivity always holds and whether
transitivity can be extended ad infinitum. -
Multi-Domain Problem
25
Multi-Domain Problem
If use an undirected graph, then solid black
edges provide a path from 1-4. In the directed
case, the grey edges avoid this possible problem.
26
Algorithm (1)

• Computing pair-wise similarities
• A complete undirected graph G
• Given edge between sequence P and Q,
• the weight of the edge raw(P, Q)
• raw(P, Q) is the raw Smith-Waterman local
  alignment score
• As mentioned above, there is the multi-domain
  problem with this approach the unwanted
  bridges connecting clearly unrelated proteins

27
Algorithm (2)

• Directing the edges
• Aim to solve the multi-domain problem
• there has to be a difference in length between
  sequences if multi-domain proteins cause a
  problem.

G
Gd
Note Raw self similarity score raw(P, P) is
approximately proportional to the length of P
28
Algorithm (3)

• Clustering in a threshold graph
• Remove all the edges from Gd if w(P, Q) lt T,
  resulting graph Gd(t)
• Using SCCs as clusters
• Definition 1 of SCC In a directed graph G, a
  Strongly Connected Component (SCC) is a maximal
  set C of nodes of G, such that for every pair of
  nodes p and q in C there is one directed path in
  G from p and one from q to p.
• Complexity O(n e), while n is the number of
  nodes and e the number of edges

29
An example of a SCC in SwissProt

• The grey nodes are not part of the SCC, but are
  clearly related.
• No edge present between nodes P03480 and P03475.
  The transitivity applied.
• Threshold 32

30
Implementation and Evaluation

• The algorithm implemented in C
• Own implementation of the Simith-Waterman local
  alignment algorithm for computing sequence
  similarity.
• The substitution matrix BLOSUM80
• Gap opening (gop) 90
• Gap extension penalties (gep) 9

31
Data (1)

• SwissProt (SP) excluded all sequences with less
  than 40 amino acids (a.a.), resulting in a set of
  86494 protein sequences
• The total running time for the pair-wise
  Smith-Waterman alignment was on the order of
  14000 cpu-days
• The evaluation data SCOP database
• Three levels are used family, super-family and
  fold

32
Data (2)

• SCOP1 set of 2692 sequences
• Contains all non-identical sequences from SCOP
• No sequences shorter than 40 a.a.
• No sequences from classes 8, 9, 10
• 65464 pairs of homologue sequences i.e. pairs
  where both sequences are in the same super-family
  and 3556622 pairs where the sequences are in
  distinct super-families.
• SCOP1 SP 85961 sequences
• All sequences are from SCOP1 and SwissProt
• SCOP2 609 randomly chosen sequences from SCOP
• Including sequences shorter than 40 a.a.
• no sequences from classes 8, 9, 10.

33
Performance measure

• Sensitivity specifies the proportion of
  identified homologue pairs

• Specificity the proportion of errors among the
  pairs predicted to be homologues

Sens spec 1 means the most highly desired
performance
34
Discussion (1)
Threshold 32 sens 55.6 spec 100 TP
due to intermediate linking 8 noise floor
lifting off at threshold 23

• Sensitivity, specificity and the percentage of
  indirectly linked true positives versus
  clustering threshold for the SCOP1 data set

35
Discussion (2)
Threshold 32 sens 57.9 spec
99.8 Indirect TP 11.6 absolute increase in
sens 2.3 relative increase in sens
4.1 absolute increase in indirect TP 3.6
The noise floor is higher

• Sensitivity, specificity and the percentage of
  indirectly linked true positives versus
  clustering threshold for the SCOP1 SP data set

36
Discussion (3)
SCOP1
SCOP1 SP
of SCOP super-families

• Total number of SCC clusters, and SCC clusters
  of sizes 1, 2-5, 6-10 etc. for varying thresholds
  from 25 to 50.

37
Discussion (4)
Comparison with algorithm by Arvestad employs
only pair-wise sequence comparisons, their
approach uses a more involved scoring method,
optimized substitution matrices, and gap
penalties, to achieve a substantial improvement
over straight-forward pair-wise sequence
comparisons. 24 better sensitivity at
virtually equal specificity.

• Sensitivity versus specificity for the SCOP2
  data set on the fold, super-family and family
  level

38
Links

• http//promoter.mi.uni-koeln.de/proclust/

39
Part III

• Improved clustering of protein sequences with an
  extended graph-based approach

40
The goal

• To detect structural homology through sequence
  similarity, by increasing sensitivity through
  transitive homology heuristics.

41
Some Alternatives

• Altenatve approaches using the concept of
  transitivity for large scale analysis of protein
  sequences
• Iterated BLAST or FASTA search for computing
  clusters, which are subsequently merged and
  processed further. Does not explicitly deal with
  multi-domain problems.
• Protomap Graph based approach, uses a combination
  of BLAST, FASTA and Smith-Waterman E-Values to
  create a hierarchy of clusters. Has problems
  with multi-domain proteins which cause cluster
  splitting.
• All against All BLAST search and ignore all hits
  below a specified threshold yielding a (0,1)
  similarity matrix. Extensive post processing is
  required to symmetrize the matrix and to deal
  with multi-domain proteins.
• Build clusters of orthologous groups (COGs)
  starting with proteins from seven different
  species. Tries to compensate for multi-domain
  proteins with an iterative merging process.

42
A new solution

• Extended graph-based approach is designed to
  provide clustering as an aid in finding remote
  homologues the multi-domain problem is directly
  addressed, although is not fully solved.
  Sensitivity is increased without a significant
  loss of specificity.

43
Different Symmetries

• Symmetric similary
• Does not distinguish between two proteins being
  globally similar and one protein being similar to
  an individual domain of a multi-domain protein.
  Can lead to incorrect links
• Asymmetric similarity
• Can be employed to distinguish between global and
  non-global similarity.

44
Limiting factors

• Large random similarities can cause
  super-clusters, which will connect large parts of
  the sequence space. This can be countered by
  using more stringent criteria.
• Multi-domain proteins
• Domains are the compact semi-independent
  structural units of proteins, which often appear
  highly conserved in a number of multi-domain
  proteins.

45
An example run

• The dataset
• SCOP v1.53
• All sequences with less than 40 amino acids were
  removed
• Filtered for low complexity regions using seg
  with the parameters of 12, 1.8, 2.0 x
• Sequences containing masked amino acids as well
  as duplicate sequences were removed.
• SPROT
• Release 39
• Processed analogously to SCOP

46
The Filtering by Significance

• Extremal value distribution
• The maximum scores of a large number of
  alignments between random sequences of equal
  length tends to have an extreme value
  distribution. Used to estimate maximal scores
  observable with the Smith-Waterman alogrithm for
  random sequences of given lengths.
• Pruning consists of removing edges (P,Q) from
  graph if the significance of the score w(P,Q) was
  below the chosen significance threshold.

47
The Algorithm

• Compute a complete undirected graph
• Replace each undirected edge with two directed
  edges

• Proceed to threshold graph by removing all edges
  of weight less than the threshold
• Compute all strongly connected components SCCs

48
Post Processing Merging Clusters

• Clusters with at least 20 sequences were selected
• Multiple alignment was built for each set of
  sequences with the ClustalW.
• Profiles were built and calibrated with the HMMER
  package using default parameters.
• For each such cluster profile all sequences not
  contained in the cluster were scored using the
  profile and the E-value was recorded.
• If a profile of one cluster resulted in an
  E-value below threshold against another cluster,
  those clusters are merged.

49
Complexity

• Using C software on a Comaq ES40 running Tru64
  Unix V5.1
• Smith-Waterman computations needed 70 CPU days.
• Clustering needed 30 seconds.
• Cluster merging using HMMs needed 21 CPU days.

50
Psi-Blast Flexibility
51
Multi-Domain Problem
52
Path Length FP. vs. TP.
53
Abundance of multi-domain proteins
54
Multi-domain Problem
Present at 13.1 threshold but disappears if
threshold is raised above 15.4 since d1m1da1
vanishes
55
Larger Multi-Domain Problem
Threshold of 21.3 and no edges between P12715
and P33497
56
More Laddering of Proteins

• False positives caused by just the right increase
  in length of proteins. None of the edges are
  removed when going over to threshold graph.

57
Extended Graph vs. PSI-BLAST
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Conlcusion

• Sensitivity 63.5 _at_ 99.0 specificity
• Improvement of 34 upon PSI-Blasts performance
  of 47.5 sensitivity and 99.0 specificity.
• Performance is gained at the expense of a much
  larger computational effort.
• Performance can be further improved by taking
  length and position of conserved regions into
  account.