bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
PosterBeNeLux Bioinformatics Conference – Antwerp,
December 7-8 2015
Abstract 10th ID: Benelux 000 Bioinformatics Category: Conference Abstract template
bbc 2015
P41. RIGAPOLLO, A HMM-SVM BASED APPROACH TO SEQUENCE
ALIGNMENT
Gabriele Orlando 1,2,3,4 , Wim Vranken 1,2,3 and & Tom Lenaerts 1,4,5 .
1 Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan, CP 263 1 ; 2 Structural
Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2 2 ; 3 Structural Biology Research Center, VIB,1050 Brussels,
Belgium 3 ;. 4 Machine Learning group, Université Libre de Bruxelles, Brussels, 1050, Belgium 4 ;. 5 Artificial Intelligence
lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium 5 .
INTRODUCTION
Reliable protein alignments are a central problem for
many bioinformatics tools, such as homology modelling.
Over the years many different algorithms have been
developed and different kinds of information have been
used to align very divergent sequences [1]. Here we
present a pairwise alignment tool, called Rigapollo, based
on pairwise HMM-SVM, which includes backbone
dynamics predictions [2] in the alignment process: recent
work suggests that protein backbone dynamics is often
evolutionary conserved and contains information
orthogonal to the amino acid conservation..
METHODS
Rigapollo uses a pairwise HMM-SVM alignment
approach to infer the optimal alignment between two
proteins, taking into consideration both sequence and
dynamic information. The model (described in Figure 1) is
composed by 3 states: M (match), G1 (gap in the first
sequence) and G2 (gap in the second sequence). The
transition probabilities are defined in the same way as a
standard HMM. This new alignment tool is further
designed in the following manner:
Defining the N-dimensional feature vectors:
Each amino acid in the sequences is described by an N-
dimensional feature vector. That vector can be defined
using any kind of information, ranging from evolutionary
information (i.e. PSSM calculated with HHblits [3])) to
dynamics predictions (using the DynaMine predictor [2]).
While standard pairwise HMMs require the definition of a
finite and discrete alphabet of observable states, our model
works directly using these feature vectors (that can be both
orthonormal or not orthonormal), evaluating the emission
probability with a support vector machine (SVM).
Definition of the emisisonemission probability:
We define the emission probability using a SVM trained
to discriminate matches from mismatches. We define as
matches all the positions in the reference pairwise
alignments that do not contain gaps and we use the
concatenation of the previously defined feature vectors to
describe them. These matches are considered positive hits.
For what concerns the mismatches, we perform the same
procedure, but couple positions that, in the reference
alignment, are shifted a number of amino acids, varying
between 5 and 10. After the training, the predicted
emission probabilities for the M state, given the
concatenation of two feature vectors, will be a function of
the distance from the decision hyperplane of the SVM
(called f(D)). The corresponding emission probabilities for
the states G1 and G2 will be modeled as 1-f(D)
RESULTS & DISCUSSION
For the evaluation of the performances of Rigapollo, we
adopted two publicly available subsets of the Balibase and
SABmark alignmenta datasets, already used to evaluate
other pairwise alignment tools [1]; from the MSAs, allpair
pairwise alignments has been extracted, and all these
that shared a percentage of sequence equal to the median
of the one of the full database has been put in the subset.
The datasets consist respectively in 38 and 123 manually
curated, structure based pairwise alignments and they
share very low sequence identity. For the evaluation of the
performances we performed a 10 folds randomized crossvalidtion.
Rigapollo increases the quality of low sequence
identity pairwise alignment from 5 to 10% respect to the
state of the art methods and it seams appears that the
increase in the performancewse is more marked in very
Figure 1: Structure of the pairwise HMM-SVM model
divergent sequences, such as the onesthose in the
SABmark dataset , where the dynamics information seams
to significantly increase the quality of the alignment. This
is probably due to the fact that dynamics are often well
conserved in functional patterns, also when the sequence
is not preserved [2].
REFERENCES
[1] Do Chuong B.et al. Research in Computational Molecular Biology.
Springer Berlin Heidelberg, 2006
[2] Cilia, Elisa, et al. Nucleic acids research 42.W1 (2014): W264-W270
[3] Remmert, Michael, et al.Nature methods 9.2 (2012): 173-175.
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