Engineering, 2013, 5, 149-153 Published Online October 2013 (
Copyright © 2013 SciRes. ENG
Using Chou’s Pseudo Amino Acid Composition for Protein
Remote Homology Detection
Bin Liu1,2,3, Xiaolong Wang1,2
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School,
Shenzhen, Guangdong, China
2Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology,
Shenzhen Graduate School, Shenzhen, Guangdong, China
3Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
Email: bl i,
Received April 2013
Protein remote homology detection is a key problem in bioinformatics. Currently, the discriminative methods, such as
Support Vector Ma chine (SVM), can achieve the best performance. The most efficient approach to improve the perfor-
mance of the SVM-based methods is to find a general protein representation method that is able to convert proteins with
different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination.
The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated
by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we appli e d this app roa ch for prot ein
remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated
into the PseAAC to improve the generalization ability of this method. Our experiments on a well-known benchmark
show this method achieves superior or comparable performance with current state-of-the-art methods.
Keywords: Protein Remote Homology; Support Vector Machine; Pseudo Amino Acid Composition;
Protein Representation
1. Introduction
Protein remote homology detection, referring to the de-
tection of evolutional homology in proteins with low
similarities, is a challenging problem in bioinformatics,
which has been intensively studied for a decade. Many
computational methods have been proposed to address
this problem, which can be split into three groups: pair-
wise comparison methods, generative models and dis-
criminative algorithms. Pairwise comparison methods
measure the pairwise similarities between protein se-
quences, such as pairwise method [1] and Smith-Water-
man dynamic programming algorithm [2]. Generative
models induce a probability distribution over the protein
family and try to generate the unknown proteins as new
member of the family from the stochastic model [3]. Re-
cent methods have applied the discriminative algorithms
for accurate remote homology detection. Different from
the generative methods, the discriminative methods lean
a combination of the features that can discriminate the
protein families. Among these methods, the top-per-
forming methods use the support vector machines (SVM)
[4] to build the discriminative framework. The core
component in the SVM is the calculation of the kernel
functions, which measure the difference between any two
pair of samples. For example, LA kernel [5] measures the
similarity between a pair of proteins by taking all the
optimal local alignment scores with gaps between all
possible subsequences into account. SVM-PDT [6] takes
the sequenc e orde r i nfor mation of the proteins i nto account
by combining the amino acid physicochemical distance
transformation and different amino acid indices derived
from the AAIndex database [7]. Some top-performing
methods employ the evolutional information extracted
from the profiles. These methods need an additional
alignment step to generate the profiles by searching
against a non-redundant database, which leads to higher
computational cost. For example, Top-n-grams extract the
profile-based patterns by considering the most frequent
elements in the profiles [8].
A key step to improve the performance of the SVM-
based methods is to find a fast and accurate representa-
tion of protein sequence. Previous studies show that the
sequence order effects are relevant for remote homology
detection [9]. The difficulty to include the sequence order
information into the prediction is that protein sequence
lengths vary widely. The pseudo amino acid composition
Copyright © 2013 SciRes. ENG
(PseAAC) was proposed by Chou [10]. Motivated by the
success of PseAAC, we applied this approach for protein
remote homology detection. Some new indices derived
from the AAIndex database are incorporated into the
PseAAC to improve the generalization ability of this
2. Methods
2.1. Benchmark Dataset
A common benchmark [1] was used to evaluate the per-
formance of our method for protein remote homology
detection, which is available at This
benchmark has been used by many studies of remote
homology detection methods [5,9,11], which can provide
good comparability with previous methods. The bench-
mark contains 54 families and 4352 proteins selected
from SCOP version 1.53. These proteins are extracted
from the Astral database [12] and include no pair with a
sequence similarity higher than an E-value of 1025. For
each family, the proteins within the family are taken as
positive test samples, and the proteins outside the family
but within the same superfamily are taken as positive
training samples. Negative samples are selected from
outside of the superfamily and are separated into training
and test sets.
2.2. Amino Acid Indices
The Amino Acid Index (AAIndex) [7] is a database of
numerical indices representing various physicochemical
and biochemical properties of amino acids and pairs of
amino acids ( There are
three sections in the latest version of the database (ver-
sion 9): AAIndex1, AAIndex2 and AAIndex3. AAIn-
dex1 contains 544 indices; AAIndex2 has 94 amino acid
mutation matrices; AAIndex3 has 47 statistical protein
contact potential matrices. Because AAIndex2 and
AAIndex3 are matrices, they are not suitable for PseAAC.
Therefore, the AAIndex1 is selected for protein trans-
formation step. After removing the incomplete data and
the indices with all zeros in AAIndex1, 531 indices are
selected for the physicochemical property distance trans-
2.3. Combining AA Indices with Pseudo Amino
Acid Composition
The pseudo amino acid composition (PseAAC) was pro-
posed by Chou [10], which takes the sequence order in-
formation into account. PseAAC has been applied to
successfully solve many important problems in computa-
tional biology, such as predicting enzymes and their fam-
ily/sub-family classification [13], protein subcellular
location prediction [14], predicting protein subnuclear
localization [15], predicting membrane proteins and their
types discrimination of outer membrane proteins [16,17],
identifying proteases and their types [18], predicting
protein quaternary structural attributes [19,20], fold pat-
tern prediction [21,22], and many other tasks.
In this study, we employ the concept of PseAAC for
protein remote homology detection. In the original Pse-
AAC, it only uses three indices, including the hydropho-
bicity index, hydrophilicity index, and side-chain mass
index. Because protein remote homology detection is a
more difficult problem, proteins in the dataset only have
very low sequence similarity. Only these three indices
are not enough to capture the different properties of var-
ious proteins. Therefore, we extend the PseAAC by using
all the meaningful 531 indices extracted from the AAIn-
dex database, which describe the properties of the 20
standard amino acids in different aspects. The proposed
method is called PseAACIndex.
The detailed process of the PseAACIndex is shown in
the following.
Given a protein sequence with L amino acids:
A1 A2 A3 A4 A5 A6......AL (1)
where A1 is the amino acid at protein chain position 1, A2
is the amino acid at protein chain position 2 and so forth.
Given an amino acid index j in AAIndex1, each protein
sequence is converted into a series of numbers by using
the amino acid index j.
All the 531 indices are subj ected to a st andard co nver-
sion by the following equation:
20 20
() 20
() ()
(() )
ji m
= =
(2 )
where I^(Ai) represents the raw physicochemical property
value of amino acid Ai in index j, Rm (m = 1, 2, 3, 4, …,
20) represents the 20 standard amino acids.
The sequence order information associated with index
j can be approximately reflected with the order-correlated
factor as defined below:
( ()())
ji ji
where λ is the distance between two amino acids along
the protein chain.
Let us use the concept of the PseAAC to formulate the
amino acid composition by using the order-correlated
factor calculated by Equation (3). Given an index j, the
protein sequence can be converted into a 20 + λ dimen-
sional vector:
Copyright © 2013 SciRes. ENG
Xj = [x1, x2, …, x20, …, x20+λ] (4)
,(1 u20)
= =
= =
+≤ ≤+
(5 )
where fm is the normalized occurrence frequency of the
20 standard amino acids in a protein,
is the i-tier
sequence correlation factor calculated by equation 3. The
first 20 elements represent the effect of the amino acid
composition, and the elements from 20 + 1 to 20 + λ
represent the effect of sequence order.
In this study, 531 indices are extracted from the
AAIndex database. Therefore, by using the above ap-
proach, a protein can be represented as following vector:
1 2531
[ ,,...,,...,]
=X xxxx
where Xj is calculated by Equations (4) and (5). There-
fore, the dimension of the final vector is 531*(20 + λ).
2.4. Support Vector Machi ne
Support vector machine (SVM) is a class of supervised
learning algorithms first introduced by Vapnik [4]. Given
a set of labelled training vectors (positive and negative
input samples), SVM can learn a linear decision boun-
dary to discriminate the two classes. The result is a linear
classification rule that can be used to classify new test
samples. When the samples are linearly non-separable,
the kernel function can be used to map the samples to a
high-order feature space in which the optimal decision
boundary can be found. SVM has exhibited excellent
performance in practice and has a strong theoretical
foundation of statistical learning.
In this stud y, the publicly available G ist SVM package
( is employed. The SVM
parameters are used by default of the Gist Package ex-
cept that the kernel function is set as radial basis func-
2.5. Evaluation Methodology
Because the test sets have more negative than positive
samples, simply measuring error-rates will not give a
good evaluation of performance. For the cases in which
the positive and negative samples are not evenly distri-
buted, the best way to evaluate the trade-off between the
specificity and sensitivity is to use a receiver operating
characteristics (ROC) score [23]. A ROC score is the
normalized area under a curve that plots true positives
against false positives for different classification thre-
sholds. A score of 1 denotes perfect separation of posi-
tive samples from negative ones, whereas a score of 0
indicates that none of the sequences selected by the algo-
rithm is positive. Another performance measure is
ROC50 score, which is the area under the ROC curve up
to the first 50 false positives.
3. Results and Discussion
3.1. λ Value Has Minior Impact on the
Performance of PseACC-AAIndex
In our method, there is a parameter λ, which would im-
pact on the performance of PseAACIndex (see method
section for more information). λ can be any integer be-
tween 1 and L-1, where L is the shortest protein se quence
in the dataset. The average ROC scores obtained by us-
ing different λ values are shown in Figure 1. As we can
see from the figure, the λ value has little impact on the
performance. PseAACIndex with different λ values show
similar results. Here, the λ value of 5 is used in this study,
because of this value, PseAACIndex can achieve the best
performance with shorter feature vectors and lower
comput a t ional cost .
3.2. Comparison with Other Sequence-Based
In order to compare the proposed PseAACIndex method
with other relevant protein remote homology detection
methods, the PseAACIndex was evaluated on the widely
used SCOP 1.53 dataset to give an unbiased comparison
with prior methods that are based on sequence composi-
tion information.
Although previous study tuned both the features and
SVM parameters for each protein family, in order to
evaluate the robustness and generalization of the
PseACC vectorization approach, no feature selection was
performed to select the best features for the proteins or
Figure 1. The impact of λ on the average ROC score ob-
tained by PseACCIndex.
Copyright © 2013 SciRes. ENG
the families. All the 531 amino acid indices were used
for predicting each family.
The predictive results of different sequence-based me-
thods are listed in Table 1. SVM-Ngram, SVM-Pattern,
and SVM-Motif are based on three different building
blocks of proteins. Mismatch method allows a given
number of mismatches between the substrings of the
proteins. SVM-LA is based on the pairwise similarity
scores. The performance of PseAACIndex is highly
comparable with SVM-LA and outperforms other me-
thods in terms of both ROC and ROC50 scores, indicat-
ing that the proposed PseAACIndex approach is an effi-
cient method for protein remote homology detection.
4. Conclusion
In this study, inspired by the success of PseAAC, we
combined the PseAAC with various amino acid indices
extracted from the AAIndex database for protein remote
homology detection. It took both the sequence-order in-
formation and the amino acid physicochemical proper-
ties extracted from the AAIndex database into considera-
tion. Experimental results demonstrated that this ap-
proach was useful for protein remote homology detection
and showed better predictive results than the compared
Table 1. Results of different methods for protein remote
homology detection.
Average ROC and ROC50 scores
Methods ROC ROC50 Source
PseAACIndex (λ = 5) 0.880 0.620 This study
SVM-Ngram 0.791 0.584 [24]
SVM-Pattern 0.835 0.589 [24]
SVM-LA(ß = 0.5) 0.925 0.649 [5]
Mismatch 0.872 0.400 [25]
SVM-Motif 0.814 0.616 [24]
5. Acknowledgements
We would like to thank Professor Kuo-Chen Chou at
Gordon Life Science Institute for his helpful suggestions
on this manuscript. This work was supported by the Na-
tional Natural Science Foundation of China (No.
61173075, 61003090 and 60973076), the Project HIT.
NSRIF.2013103 supported by Natural Scientific Re-
search Innovation Foundation in Harbin Institute of
Technology, Natural Science Foundation of Guangdong
province (No.S2012040007390), and Shanghai Key La-
boratory of Intelligent Information Processing, China
(Grant No.IIPL-2012-002).
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