| Protein
& Peptide Letters
ISSN: 0929-8665
Protein & Peptide Letters
Volume 15, Number 5, 2008
Contents
Special Issue on Advanced Intelligent Computing
Theory and Methodology in Protein Science
Guest Editor: De-Shuang Huang
Editorial: Pp. 427
D.-S. Huang
Protein Classification Based on Propagation of
Unrooted Binary Trees Pp. 428-434
A. Kocsor, R. Busa-Fekete and S. Pongor
[Abstract]
Combining Classifiers for HIV-1 Drug-Resistance
Prediction Pp. 435-442
A. Srisawat and B. Kijsirikul
[Abstract]
Protein Fold Recognition Based on Error-Correcting
Output Codes and SVM Pp. 443-447
Y. Chen, Q. Chen, F. Chen and Y. Zhao
[Abstract]
Analysis of Protein Surface Patterns by Pocket
Similarity Network Pp. 448-455
Z.-P. Liu, L.-Y. Wu, Y. Wang, X.-S. Zhang and
L. Chen
[Abstract]
Protein Domain Annotation with Predicted Domain-Domain
Interaction Networks Pp. 456-462
X.-M. Zhao, Y. Wang, L. Chen and K.
Aihara
[Abstract]
Function Prediction for DNA-/RNA-Binding Proteins,
GPCRs, and Drug ADME-Associated Proteins by SVM Pp.
463-468
C. Cai, H. Xiao, Q. Yuan, X. Liu and
Y. Wen
[Abstract]
Protein Folding in Hydrophobic-Polar Lattice Model:
A Flexible Ant-Colony Optimization Approach Pp. 469-477
X.-m. Hu, J. Zhang, J. Xiao and Y. Li
[Abstract]
Predicting Key Long-Range Interaction Sites by
B-Factors Pp. 478-483
P. Chen, K. Han, X. Li and D.-S. Huang
[Abstract]
An Artificial Immune Network-Based Algorithm for
Diabetes Diagnosis Pp. 484-487
L. Peng, T. Li, X. Liu, C. Liu, J. Zeng and
J. Zhang
[Abstract]
Efficient Ensemble Schemes for Protein Secondary
Structure Prediction Pp. 488-493
K.-H. Liu, J.-F. Xia and X. Li
[Abstract]
Prediction of Binding Motifs in Hepatitis C Virus
NS5A and Human Proteins Pp. 494-504
G.-Z. Zhang and K. Han
[Abstract]
Computational Analyses of the TBC Protein Family
in Eukaryotes Pp. 505-509
X. Gao, C. Jin, Y. Xue and X. Yao
[Abstract]
General Articles
Regular Papers
The Complex Structure Transition of Humanin Peptides by Sodium
Dodecylsulfate and Trifluoroethanol Pp. 510-515
Y. Kita, T. Niikura, F. Arisaka and T.
Arakawa
[Abstract]
Hysteresis on Heating and Cooling of
E. coli Alkaline Phosphatase Pp. 516-520
I.S. Uto and J.M. Brewer
[Abstract]
Structural Changes and Aggregation Process of
Cu/Containing Amine Oxidase in the Presence of 2,2,2'-Trifluoroethanol
Pp. 521-527
M. Amani, R. Yousefi, A.A. Moosavi-Movahedi,
F. Pintus, A. Mura, G. Floris, B.I. Kurganov and
A.A. Saboury
[Abstract]
Purification and Some Kinetic Properties of Carbonic
Anhydrase from Rainbow Trout (Oncorhynchus mykiss)
Liver and Metal Inhibition Pp. 528-535
H. Soyut and S. Beydemir
[Abstract]
Computational Analysis Suggests Beta-Defensins
Are Processed to Mature Peptides by Signal Peptidase
Pp. 536-540
N. Beckloff and G. Diamond
[Abstract]
Crystallization Report
Protein Preparation, Crystallization, and Preliminary X-Ray
Crystallographic Analysis of N-Acetylglutamate Kinase from
Streptococcus mutans Pp. 541-543
C.-W. Wu, L.-F. Li, X. Liu, X.-Z. Gao, J. Lei,
X.-D. Su, X. Zhao and Y.
H. Liang
[Abstract]
Abstracts
[Back to top]
Editorial
D.-S. Huang
We are very pleased to offer this special issue to the
readers of Protein and Peptide Letters by selecting
the candidate papers from the 2007 International Conference
on Intelligent Computing (ICIC), held in Qingdao, Shandong
Province, China, 21-24 August 2007. Twelve papers (representing
less than one half of one percent of all eligible papers accepted
at the 2007 ICIC) were selected for inclusion in this special
issue.
In recent years, we have witnessed intelligent computing techniques,
such as artificial intelligence, machine learning, colony
intelligence, and others being dedicated to various research
aspects of bioinformatics, neuroinformatics, chemoinformatics,
computational biology, system biology, etc. Meanwhile, intelligent
computing knowledge has been enriched by the development of
more solid mathematical frameworks, elaborating more efficient
and powerful algorithms as well as structures. More importantly,
it has been driven by its application to many amazing research
fields, such as bioinformatics.
Currently, intelligent computing techniques for protein bioinformatics
are being used to encode biological information, extract biological
features, recognize biological patterns, mine and comprehend
biological data, build models of biological systems and processes,
and thus automatically form theories from the unprecedentedly
vast experimental biological data. Its main objective is to
find the rule and useful biological information from limited
observation examples that cannot be obtained using classical
biological methods and theories. It extends the rule to predict
and infer protein’s structure, function, structurefunction
relationship, interaction networks, and other significant
aspects of protein and peptide science. Hence, the intelligent
computing technique, an in silico method, is supplementary
to conventional experimental methods and makes it possible
to use computers to extract knowledge from large amounts of
biological and biomedical information. This special issue
includes several papers on how to use intelligent computing
techniques to solve problems in protein bioinformatics.
Three papers in this issue focus on exploring computational
algorithms and applications in protein bioinformatics. BusaFekete
et al. discuss using propagation on unrooted binary
trees to perform protein classification. Zhang et al.
address an antcolony algorithm for solving protein-folding
problems by placing pheromones on the arcs connecting adjacent
squares in the lattice. Peng et al. present an artificial
immune network-based algorithm for diabetes diagnosis.
In the next two papers, Srisawat et al. use combined
classifiers for HIV drug-resistance prediction, and Liu, Xia
et al. adopt ensemble schemes for protein secondary
structure prediction.
The next four papers present network or sequence multiple
alignment-based protein structure and function predictions.
Liu et al. discuss using a pocket similarity network
to analyze protein surface patterns. Zhao et al.
propose a framework of protein domain function annotation
with predicted domain-domain interaction networks. Zhang et
al. predict the binding motifs in hepatitis protein C
virus NS5A and human proteins using sequence multiple alignment.
Gao et al. present their computational phylogenetic
analysis for the functional annotation of TBC (Tre-2/Bub2/Cdc16)
domain-containing proteins.
The last three papers focus on applying special coding methods,
support vector machine (SVM) and its modified techniques to
protein folding recognition, ligand binding, and long-range
interaction site predictions, respectively. More specifically,
Chen et al. combine error-correcting output codes
and SVM methods for protein fold recognition. Cai et al.
apply SVM to the function prediction of DNA-/RNA- binding
proteins, GPCR, and drug ADME-associated proteins. Chen
et al. present a bounded SVM method for locating key
long-range interaction sites by predicted, local B-factors.
It should be stressed that recommendations for this special
issue were made by the ICIC's International Program Committee,
and the final selections were made on the basis of quality,
novelty, and theoretical or practical importance. All papers
were subjected to two rounds of review with a minimum of three
reviewers, reflecting the demand for improving the quality
of ICIC papers. We hope that you find this special issue both
useful and enjoyable.
As guest editor, I would like to take this opportunity to
thank all the authors for their contributions to this special
issue, the reviewers for their valuable input, insight, and
expert comments, and the Editor-in-Chief, Distinguished Professor
Ben M. Dunn, for his valuable advice and strong support during
the preparation of this special issue.
De-Shuang Huang
Guest Editor
Protein & Peptide Letters
Intelligent Computing Lab
Hefei Institute of Intelligent Machines
Chinese Academy of Sciences
P.O. Box 1130, Hefei Anhui 230031
China
[Back to top]
Protein Classification Based on Propagation of
Unrooted Binary Trees
A. Kocsor, R. Busa-Fekete and S. Pongor
We present two efficient network propagation algorithms
that operate on a binary tree, i.e., a sparse-edged substitute
of an entire similarity network. TreeProp-N is based on passing
increments between nodes while TreeProp-E employs propagation
to the edges of the tree. Both algorithms improve protein
classification efficiency.
[Back to top]
Combining Classifiers for HIV-1 Drug-Resistance Prediction
A. Srisawat and B. Kijsirikul
This paper applies and studies the behavior of three
learning algorithms, i.e. the Support Vector machine (SVM),
the Radial Basis Function Network (the RBF network), and k-Nearest
Neighbor (k-NN) for predicting HIV-1 drug resistance
from genotype data. In addition, a new algorithm for classifier
combination is proposed. The results of comparing the predictive
performance of three learning algorithms show that, SVM yields
the highest average accuracy, the RBF network gives the highest
sensitivity, and k-NN yields the best in specificity.
Finally, the comparison of the predictive performance of the
composite classifier with three learning algorithms demonstrates
that the proposed composite classifier provides the highest
average accuracy.
[Back to top]
Protein Fold Recognition Based on Error-Correcting
Output Codes and SVM
Y. Chen, Q. Chen, F. Chen and Y. Zhao
A new approach based on the implementation of support
vector machine (SVM) with the error correcting output codes
(ECOC) is presented for recognition of multi-class protein
folds. The experimental show that the proposed method can
improve prediction accuracy by 4%-10% on two datasets containing
27 SCOP folds.
[Back to top]
Analysis of Protein Surface Patterns by Pocket Similarity
Network
Z.-P. Liu, L.-Y. Wu, Y. Wang, X.-S. Zhang and
L. Chen
In this work, in order to reveal protein surface patterns
in a systems biology framework, we analyze the similarity
among the surface cavities by investigating the features of
the pocket similarity network such as the community structure,
the small-world property, the scale-free characteristic, and
the hubs.
[Back to top]
Protein Domain Annotation with Predicted Domain-Domain
Interaction Networks
X.-M. Zhao, Y. Wang, L. Chen and K.
Aihara
This paper presents a framework for annotating protein
domains with predicted domain-domain interaction networks.
Specially, domain annotation is formalized as a multi-class
classification problem in this work. The numerical experiments
on InterPro domains show promising results, which proves the
efficiency of our proposed methods.
[Back to top]
Function Prediction for DNA-/RNA-Binding Proteins,
GPCRs, and Drug ADME-Associated Proteins by SVM
C. Cai, H. Xiao, Q. Yuan, X. Liu and
Y. Wen
This paper explores the use of support vector machine
(SVM) for protein function prediction. Studies are conducted
on several groups of proteins with different functions including
DNA-binding proteins, RNA-binding proteins, G-protein coupled
receptors, drug absorption proteins, drug metabolizing enzymes,
drug distribution and excretion proteins. The computed accuracy
for the prediction of these proteins is found to be in the
range of 82.32% to 99.7%, which illustrates the potential
of SVM in facilitating protein function prediction.
[Back to top]
Protein Folding in Hydrophobic-Polar Lattice Model:
A Flexible Ant-Colony Optimization Approach
X.-m. Hu, J. Zhang, J. Xiao and Y. Li
This paper proposes a flexible ant colony (FAC) algorithm
for solving protein folding problems based on the hydrophobic-polar
square lattice model. Collaborations of novel pheromone and
heuristic strategies in the proposed algorithm make it more
effective in predicting structures of proteins compared with
other state-of-the-art algorithms.
[Back to top]
Predicting Key Long-Range Interaction Sites by B-Factors
P. Chen, K. Han, X. Li and D.-S. Huang
In this paper, we adopted the bounded support vector
machine to locate the key long-range interaction sites by
the use of predicted local lowest B-factors. As a result,
the key long-range interaction residues can be located based
on information of local lowest B-factor sites.
[Back to top]
An Artificial Immune Network-Based Algorithm for Diabetes
Diagnosis
L. Peng, T. Li, X. Liu, C. Liu, J. Zeng and
J. Zhang
A novel artificial immune network based algorithm for
the diagnosis of diabetes is presented. The algorithm’s
implementation includes: (1) creating the initial immune antibody
network; (2) the network is evolved with the learning from
foreign antigens; (3) diagnosis process is accomplished by
majority vote of the k nearest neighbor antibodies.
[Back to top]
Efficient Ensemble Schemes for Protein Secondary Structure
Prediction
K.-H. Liu, J.-F. Xia and X. Li
This paper proposes an efficient ensemble system to tackle
the protein secondary structure prediction problem with neural
networks as base classifiers. The experimental results show
that the multi-layer system can lead to better results. When
deploying more accurate classifiers, the higher accuracy of
the ensemble system can be obtained.
[Back to top]
Prediction of Binding Motifs in Hepatitis C Virus
NS5A and Human Proteins
G.-Z. Zhang and K. Han
From the extensive analysis, we identified three highly
conserved sequence segments in HCV NS5A proteins and one binding
motif in human proteins. The binding motif of human proteins
often forms a full helix or an extended strand-loop structure,
and is in good agreement with the experimental findings of
previous studies.
[Back to top]
Computational Analyses of the TBC Protein Family in
Eukaryotes
X. Gao, C. Jin, Y. Xue and X. Yao
Tre-2/Bub2/Cdc16 domain-containing proteins (TBC proteins)
participate in wide range cellular processes. With computational
approaches, 137 non-redundant TBC proteins from five model
organisms were identified and classified into 13 subfamilies
base on molecular evolutionary tree. This phylogenetic analysis
provides useful functional annotation of newly-identified
TBC proteins and guides for further experimentation.
[Back to top]
The Complex Structure Transition of Humanin Peptides by Sodium
Dodecylsulfate and Trifluoroethanol
Y. Kita, T. Niikura, F. Arisaka and T.
Arakawa
We have examined the structure of two Humanin (HN) analog
peptides, HNG and AGA-(C8R)HNG17, in the presence of sodium
dodecylsulfate (SDS) and trifluoroethanol (TFE) using CD and
sedimentation velocity. Both HNG and AGA-(C8R)HNG17 underwent
complex conformational changes with increasing concentrations
of SDS and TFE, in contrast to general trend of increasing
α-helix
with their concentration. To our surprise, both peptides appear
to converge into a similar structure in SDS and TFE at higher
concentrations; e.g., above 0.05 % SDS or 30-40 % TFE. Sedimentation
velocity analysis showed extensive aggregation of HNG at 0.1
mg/ml in PBS in the absence of SDS, but a highly homogeneous
solution in 0.1 % SDS, indicating formation of a uniform structure
by SDS. These two peptides also formed an intermediate structure
both in SDS and TFE at lower concentrations, which appeared
to be associated with extensive aggregation. It is interesting
that the structure changes of these peptides occur well below
the critical micelle concentration of SDS, suggesting that
conformational changes are mediated through molecular, not
micellar, interactions with SDS.
[Back to top]
Hysteresis on Heating and Cooling of E.
coli Alkaline Phosphatase
I.S. Uto and J.M. Brewer
Measurements of [θ]222
of E. coli phosphatase on heating from 20°
to 90°
and subsequent cooling to 20°
shows a gradual increase in [θ]222
on heating, while cooling shows a symmetric transition centered
at 45°.
Reheating and cooling shows the same phenomenon. Enzyme heated
and cooled once is fully active. The activity of the enzyme
depends on its storage conditions (buffer and pH for example),
but such changes are least to some extent reversible, especially
by heating in different solvents. We conclude the enzyme exists
in several forms which are in slow equilibrium with each other,
so that the enzyme responds slowly when heated and hence is
not at equilibrium during heating/cooling experiments.
[Back to top]
Structural Changes and Aggregation Process of Cu/Containing
Amine Oxidase in the Presence of 2,2,2'-Trifluoroethanol
M. Amani, R. Yousefi, A.A. Moosavi-Movahedi,
F. Pintus, A. Mura, G. Floris, B.I. Kurganov and
A.A. Saboury
Conformational and structural changes of lentil seedlings
amine oxidase (LSAO) were studied in the presence of trifluoroethanol
(TFE) by spectroscopic and analytical techniques. At TFE concentrations
up to 5%, the induction of a structural transition from β-sheet
to α-helix
and up to 10% TFE a structural transition from α-helix
to β-sheet
as well as inactivation of the enzyme are observed. At TFE
concentrations between 10-35%, LSAO proves to be prone to
aggregation and beyond 35% TFE leads to a non–native
protein structure with a high α-helix
content. The obtained results revealed that the aggregation
of LSAO is strongly linked to the nature of secondary structures.
[Back to top]
Purification and Some Kinetic Properties of Carbonic
Anhydrase from Rainbow Trout (Oncorhynchus mykiss)
Liver and Metal Inhibition
H. Soyut and S. Beydemir
In the present study, carbonic anhydrase (CA) enzyme was purified
from rainbow trout (RT) liver with a specific activity of
4318 EUxmg-1 and a yield of 38% using Sepharose-4B-L
tyrosine-sulfanilamide affinity gel chromatography. The overall
purification was approximately 2260-fold. To check the purity
and determine subunit molecular weight of enzyme, SDS-polyacrylamide
gel electrophoresis was performed, which showed a single band
and MW of approx. 29.4 kDa. The molecular weight of native
enzyme was estimated to be approx. 31 kDa by Sephadex-G 200
gel filtration chromatography. Optimum and stable pH were
determined as 9.0 in 1 M Tris-SO4
buffer and 8.5 in 1 M Tris-SO4
buffer at 4oC, respectively. The optimum temperature,
activation energy (Ea),
activation enthalpy (ΔH
) and Q10 from Arrhenius
plot for the RT liver CA were 40°C, 2.88 kcal/mol, 2.288
kcal/mol and 1.53, respectively. The purified enzyme had an
apparent Km and
Vmax of 0.66 mM and 0.126
μmol
x min-1 for 4-nitrophenylacetate, respectively.
Kcat
of the CA was found to be 32.8 s-1. The
inhibitory effects of low concentrations of different metals
(Co(II), Cu(II), Zn(II) and Ag(I)) on CA activity were determined
using the esterase method under in vitro conditions.
The obtained C50
values, 50% inhibition of in vitro enzyme
activity, were 0.03 mM for cobalt, 30 mM for copper, 47.1
mM for zinc and 0.01 mM for silver. Ki
values for these substances were also calculated from Linewaever-Burk
plots as 0.050 mM for cobalt, 1.950 mM for copper, 7.035 mM
for zinc and 2.190 mM for silver respectively and determined
that cobalt and zinc inhibit the enzyme a competitive manner
and copper and silver inhibit the enzyme in an uncompetitive
manner.
[Back to top]
Computational Analysis Suggests Beta-Defensins Are
Processed to Mature Peptides by Signal Peptidase
N. Beckloff and G. Diamond
Antimicrobial peptides (AMPs) are generally produced
as precursor peptides containing a signal sequence, a pro-region
and the mature peptide. A computational analysis of β-defensin
precursors predicts cleavage solely by signal peptidase to
release the mature peptide, with no pro-region. This supports
the extensive transcriptional control of β-defensin
expression compared with other AMP genes.
[Back to top]
Protein Preparation, Crystallization, and Preliminary X-Ray
Crystallographic Analysis of N-Acetylglutamate Kinase from
Streptococcus mutans
C.-W. Wu, L.-F. Li, X. Liu, X.-Z. Gao, J.
Lei, X.-D. Su, X. Zhao and Y.
H. Liang
The N-acetylglutamate kinase from Streptococcus mutans
was expressed in Escherichia coli in soluble form
and purified to homogeneity. Crystals suitable for X-ray diffraction
were obtained by hanging-drop vapor diffusion method and diffracted
to 2.06 Å.
The crystal belonged to space group P21
21 2, with unit cell parameters
α
= 57.19 Å,
b =94.76 Å,
c =47.58 Å.
The gel filtration and initial phasing results showed that
the enzyme exists as a monomer, which is different from previously
reported N-acetylglutamate kinases. |
