Reading Report Ce WANG A segment alignment approach to protein comparison

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!