reading report ce wang a segment alignment approach to protein comparison
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Reading Report
Ce WANG
A segment alignment approach to protein
comparison
AgendaAgenda
Motivation Previous works SEgment Alignment algorithm (SEA) Results and Discussion Answer Questions
Motivation
Local structure segments (LSSs) Predicted LSSs (PLSSs) predicted or real LSSs are rarely
exploited by protein sequence comparison programs that are based on position-by-position alignments.
Previous WorksPrevious Works
Nearest-neighbor methods
which typically produce a list of Predicted Local Structure Segments (PLSSs) for a given protein (Fig. 1, Rychlewski and Godzik, 1997; Yi and Lander, 1993; Bystroff and Baker, 1998).
ambiguous
Previous WorksPrevious Works
single position secondary structures averaged over the segments (Rychlewski and Godzik, 1997; Yi and Lander, 1993).
Baker and colleagues (Bystroff and Baker, 1998) who further combined the predicted segments for a compact tertiary structure in their de novo protein structure prediction program ROSETTA (Simons et al., 1999).
Previous WorksPrevious Works
most protein comparison methods are firmly based on the concept of residue-level alignments (Waterman, 1995)
similar proteinssimilar proteins
SEgment Alignment SEgment Alignment (SEA) (SEA)
compare proteins described as a compare proteins described as a collection of predicted local structure collection of predicted local structure segments (PLSSs), which is equivalent segments (PLSSs), which is equivalent to an unweighted graph (network). Any to an unweighted graph (network). Any specific structure, real or predicted specific structure, real or predicted corresponds to a specific path in this corresponds to a specific path in this network. network.
SEA then uses a network matching SEA then uses a network matching approach to find two most similar paths approach to find two most similar paths in networks representing two proteins. in networks representing two proteins.
AdvantageAdvantage
SEA explores the SEA explores the uncertainty and diversity of predicted local of predicted local structure information to search for structure information to search for a globally optimal solution. It a globally optimal solution. It simultaneously solves two related simultaneously solves two related problems: problems:
the alignment of two proteins and the the alignment of two proteins and the local structure prediction for each local structure prediction for each of them.of them.
SEA FORMULATION
network matching problem that can be solved by dynamic programming in polynomial time.
SEA
We define V(i, j ) as the maximum similarity score for transforming S1[1 . . . i] to S2[1 . . . j ], calculated by
V(i, j ) = maxall(α,β)combinations, α∈E(i ),
β∈E( j )V(iα, jβ)
substitution, deletion and insertion
IMPLEMENTATION
The prediction and representation of local structures
Scoring scheme(iα, jβ) = Wa × (Aai , Aaj ) + Ws × (α, β)
Fig. 3. Comparison of the alignments between λ-repressor from E.coli (1lliA) and 434 repressor (1r69) by CE (top) and SEA (bottom).
IMPLEMENTATION
The measures of alignment accuracy
The benchmark for SEA validation
RESULTS AND DISCUSSION
The general performance of SEA on the benchmark
Prediction ambiguity improves alignment quality
Alignment quality versus local structure prediction ambiguity
CONCLUSION
Any Questions?Any Questions?
Thanks!
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