Population-based Ant Colony algorithm is stochastic local search algorithm that mimics the behavior of
real ants, simulating pheromone trails to search for solutions to combinatorial optimization problems. This
paper introduces population-based Ant Colony algorithm to solve 3D Hydrophobic Polar Protein structure
Prediction Problem then introduces a new enhanced approach of population-based Ant Colony algorithm
called Enhanced Population-based Ant Colony algorithm (EP-ACO) to avoid stagnation problem in
population-based Ant Colony algorithm and increase exploration in the search space escaping from local
optima, The experiments show that our approach appears more efficient results than state of art method.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Tree Physiology Optimization in Constrained Optimization ProblemTELKOMNIKA JOURNAL
Metaheuristic algorithms are proven to be more effective on finding global optimum in numerous
problems including the constrained optimization area. The algorithms have the capacity to prevail over
many deficiencies in conventional algorithms. Besides of good quality of performance, some metaheuristic
algorithms have limitations that may deteriorate by certain degree of difficulties especially in real-world
application. Most of the real-world problems consist of constrained problem that is significantly important in
modern engineering design and must be considered in order to perform any optimization task. Therefore, it
is essential to compare the performance of the algorithm in diverse level of difficulties in constrained
region. This paper introduces Tree Physiology Optimization (TPO) algorithm for solving constrained
optimization problem and compares the performance with other existing metaheuristic algorithms. The
constrained problems that are included in the comparison are three engineering design and nonlinear
mathematic problems. The difficulties of each proposed problem are the function complexity, number of
constraints, and dimension of variables. The performance measure of each algorithm is the statistical
results of finding the global optimum and the convergence towards global optimum.
AN IMPROVED MULTIMODAL PSO METHOD BASED ON ELECTROSTATIC INTERACTION USING NN...ijaia
In this paper, an improved multimodal optimization (MMO) algorithm,calledLSEPSO,has been proposed. LSEPSO combinedElectrostatic Particle Swarm Optimization (EPSO) algorithm and a local search method and then madesome modification onthem. It has been shown to improve global and local optima finding ability of the algorithm. This algorithm useda modified local search to improve particle's personal best, which usedn-nearest-neighbour instead of nearest-neighbour. Then, by creating n new points among each particle and n nearest particles, it triedto find a point which could be the alternative of particle's personal best. This methodprevented particle's attenuation and following a specific particle by its neighbours. The performed tests on a number of benchmark functions clearly demonstratedthat the improved algorithm is able to solve MMO problems and outperform other tested algorithms in this article.
The optimization of running queries in relational databases using ant colony ...ijdms
The issue of optimizing queries is a cost-sensitive
process and with respect to the number of associat
ed
tables in a query, its number of permutations grows
exponentially. On one hand, in comparison with oth
er
operators in relational database, join operator is
the most difficult and complicated one in terms of
optimization for reducing its runtime. Accordingly,
various algorithms have so far been proposed to so
lve
this problem. On the other hand, the success of any
database management system (DBMS) means
exploiting the query model. In the current paper, t
he heuristic ant algorithm has been proposed to sol
ve this
problem and improve the runtime of join operation.
Experiments and observed results reveal the efficie
ncy
of this algorithm compared to its similar algorithm
s.
Tree Physiology Optimization in Constrained Optimization ProblemTELKOMNIKA JOURNAL
Metaheuristic algorithms are proven to be more effective on finding global optimum in numerous
problems including the constrained optimization area. The algorithms have the capacity to prevail over
many deficiencies in conventional algorithms. Besides of good quality of performance, some metaheuristic
algorithms have limitations that may deteriorate by certain degree of difficulties especially in real-world
application. Most of the real-world problems consist of constrained problem that is significantly important in
modern engineering design and must be considered in order to perform any optimization task. Therefore, it
is essential to compare the performance of the algorithm in diverse level of difficulties in constrained
region. This paper introduces Tree Physiology Optimization (TPO) algorithm for solving constrained
optimization problem and compares the performance with other existing metaheuristic algorithms. The
constrained problems that are included in the comparison are three engineering design and nonlinear
mathematic problems. The difficulties of each proposed problem are the function complexity, number of
constraints, and dimension of variables. The performance measure of each algorithm is the statistical
results of finding the global optimum and the convergence towards global optimum.
DISCOVERING DIFFERENCES IN GENDER-RELATED SKELETAL MUSCLE AGING THROUGH THE M...ijbbjournal
Understanding gene function (GF) is still a significant challenge in system biology. Previously, several
machine learning and computational techniques have been used to understand GF. However, these previous
attempts have not produced a comprehensive interpretation of the relationship between genes and
differences in both age and gender. Although there are several thousand of genes, very few differentially
expressed genes play an active role in understanding the age and gender differences. The core aim of this
study is to uncover new biomarkers that can contribute towards distinguishing between male and female
according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system
(MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and Receiver
Operating Characteristic (ROC)). Later, important genes are acquired using majority voting based on the
principle that combining multiple models can improve the generalization of the system. Experiments were
conducted on Micro Array gene expression dataset and results have indicated a significant increase in
classification accuracy when compared with existing system.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
HMM’S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSISijcseit
HMM has found its application in almost every field. Applying Hmm to biological sequences has its own
advantages. HMM’s being more systematic and specific, yield a result better than consensus techniques.
Profile HMMs use position specific scoring for the matching & substitution of a residue and for the
opening or extension of a gap. HMMs apply a statistical method to estimate the true frequency of a residue
at a given position in the alignment from its observed frequency while standard profiles use the observed
frequency itself to assign the score for that residue. This means that a profile HMM derived from only 10 to
20 aligned sequences can be of equivalent quality to a standard profile created from 40 to 50 aligned
sequences.
DISCOVERING DIFFERENCES IN GENDER-RELATED SKELETAL MUSCLE AGING THROUGH THE M...ijbbjournal
Understanding gene function (GF) is still a significant challenge in system biology. Previously, several
machine learning and computational techniques have been used to understand GF. However, these previous
attempts have not produced a comprehensive interpretation of the relationship between genes and
differences in both age and gender. Although there are several thousand of genes, very few differentially
expressed genes play an active role in understanding the age and gender differences. The core aim of this
study is to uncover new biomarkers that can contribute towards distinguishing between male and female
according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system
(MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and Receiver
Operating Characteristic (ROC)). Later, important genes are acquired using majority voting based on the
principle that combining multiple models can improve the generalization of the system. Experiments were
conducted on Micro Array gene expression dataset and results have indicated a significant increase in
classification accuracy when compared with existing system.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
HMM’S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSISijcseit
HMM has found its application in almost every field. Applying Hmm to biological sequences has its own
advantages. HMM’s being more systematic and specific, yield a result better than consensus techniques.
Profile HMMs use position specific scoring for the matching & substitution of a residue and for the
opening or extension of a gap. HMMs apply a statistical method to estimate the true frequency of a residue
at a given position in the alignment from its observed frequency while standard profiles use the observed
frequency itself to assign the score for that residue. This means that a profile HMM derived from only 10 to
20 aligned sequences can be of equivalent quality to a standard profile created from 40 to 50 aligned
sequences.
AMINO ACID INTERACTION NETWORK PREDICTION USING MULTI-OBJECTIVE OPTIMIZATIONcscpconf
Protein can be represented by amino acid interaction network. This network is a graph whose
vertices are the proteins amino acids and whose edges are the interactions between them. This
interaction network is the first step of proteins three-dimensional structure prediction. In this
paper we present a multi-objective evolutionary algorithm for interaction prediction and ant
colony probabilistic optimization algorithm is used to confirm the interaction.
Exploiting tertiary structure through local folds for crystallographic phasingxrbiotech
Exploiting tertiary structure through local folds for crystallographic phasing
Massimo Sammito, Claudia Millán, Dayté D Rodríguez, Iñaki M de Ilarduya, Kathrin Meindl, Ivan De Marino, Giovanna Petrillo, Rubén M Buey, José M de Pereda, Kornelius Zeth, George M Sheldrick and Isabel Usón
Journal:Nature Methods 10, 1099-1101 (2013); doi:10.1038/nmeth.2644
Protein can be represented by amino acid interaction network. This network is a graph whose vertices are
the proteins amino acids and whose edges are the interactions between them. In this paper we have
formalized amino acid interaction network prediction as a multi-objective evolutionary optimization
problem. This formalism is biologically plausible because interactions among amino acids do not depend
only on a single factor like atomic distance but also other factors like torsion angle, hydrophobicity and
hydrophilicity etc. This problem is then solved and implemented using multi-objective genetic algorithm
and subsequently optimized using ant colony optimization technique. The result shows that our algorithm
performs better than recent amino acid interaction network prediction algorithms that are based on single
factor
IOSR Journal of Applied Chemistry (IOSR-JAC) is an open access international journal that provides rapid publication (within a month) of articles in all areas of applied chemistry and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Chemical Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Fit for fitting: Bionext-DSS, a dataset of similars sitesMarie-Julie Denelle
“Bionext-DSS” is a dataset of similar binding sites which contains 35 test cases connected to a pharmaceutical application. Each case includes a reference site, as well as several positive sites further classified in 6 bins according to their similarity level to the reference. We moreover provide a list of noise structures carefully selected so as to be used
as negative controls in algorithms evaluation.
Amino acid interaction network prediction using multi objective optimizationcsandit
Protein can be represented by amino acid interaction network. This network is a graph whose
vertices are the proteins amino acids and whose edges are the interactions between them. This
interaction network is the first step of proteins three-dimensional structure prediction. In this
paper we present a multi-objective evolutionary algorithm for interaction prediction and ant
colony probabilistic optimization algorithm is used to confirm the interaction.
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
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Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Gopinath Rebala
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10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
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11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
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Paper: https://eprint.iacr.org/2023/1886
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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ENHANCED POPULATION BASED ANT COLONY FOR THE 3D HYDROPHOBIC POLAR PROTEIN STRUCTURE PREDICTION PROBLEM
1. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
DOI: 10.5121/ijbb.2013.3304 41
ENHANCED POPULATION BASED ANT COLONY FOR
THE 3D HYDROPHOBIC POLAR PROTEIN
STRUCTURE PREDICTION PROBLEM
Sara Salah1
, Abdel-Rahman Hedar2
and Marghny. H. Mohammed3
1
Computer Science Department, Faculty of Science, Assiut University
2
Computer Science Department, Faculty of Computers and Information, Assiut
University
3
Computer Science Department, Faculty of Computers and Information, Assiut
University
ABSTRACT
Population-based Ant Colony algorithm is stochastic local search algorithm that mimics the behavior of
real ants, simulating pheromone trails to search for solutions to combinatorial optimization problems. This
paper introduces population-based Ant Colony algorithm to solve 3D Hydrophobic Polar Protein structure
Prediction Problem then introduces a new enhanced approach of population-based Ant Colony algorithm
called Enhanced Population-based Ant Colony algorithm (EP-ACO) to avoid stagnation problem in
population-based Ant Colony algorithm and increase exploration in the search space escaping from local
optima, The experiments show that our approach appears more efficient results than state of art method.
KEYWORDS
Population based ACO, Ant Colony Optimization, HP Model, Protein Structure Prediction
1. INTRODUCTION
Recent breakthroughs in DNA and protein sequencing have unlocked many secrets of molecular
biology. A complete understanding of gene function, however, requires a protein structure in
addition to its sequence. Accordingly, the better we understand how proteins are built, the better
we can deal with many common diseases. In particular, information on structural properties of
proteins can give insight into the way they work and therefore help and influence modern
medicine and drug development [1].
The protein structure prediction problem (PSP) is that of computationally predicting the
three dimensional structure of protein from the sequence of amino acids alone. This has
been an open problem for more than 30 years and developing a practical solution is
widely considered the 'holy grail' of computational biology. The various approaches to the
problem can be classified into two categories: knowledge based methods building the structure
based on knowledge of a good template structure [16]; Ab initio methods building the structure
from scratch using primary principles. Ab initio do not rely on known structures in the PDB [2] as
2. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
42
knowledge based methods, instead, they predict the 3D structure of proteins given their primary
sequences only. The underlying strategy is to find the best stable structure based on a chosen
energy function. According to Anfinsen famous hypothesis, a protein native structure is
determined by its sequence which corresponds to minimum Gibbs energy [3]. The main challenge
of this approach is to search for the most stable structure in a huge search space. In general, Ab
initio PSP can be reduced to the following three steps:
1) Design a simple model with a desired level of accuracy to represents the protein structure,
when we approach PSP problem, probably the first thing is to represent protein structure in
the problem space we can represent protein structures using two categories: All-atom Model
and Simplified Models.
All-atom model: protein structures are represented by lists of 3D coordinates of all
atoms in a protein. Although an accurate all-atom model is desired in the structure
prediction, it causes too huge a computation overhead even for very small proteins.
Simplified Models: simplified models can be classified into lattice models and off-
lattice models. Lattice models adopted lattice environment which is a grid and
structural elements are positioned only at grid intersections; whereas off-lattice
models use off-lattice environment in which structural elements are positioned in a
continuous space
In this paper we concern on lattice models, perhaps the simplest lattice protein model is
Hydrophobic Polar (HP Model). It was proposed by Dill [4] and is widely studied for Ab
initio prediction.
2) Define an energy function that can effectively discriminate native states from nonnative
states. The HP model is based on the observation that the Hydrophobic Force is the main
force for protein folding more about energy function we will discuss in section 2.
3) Design an efficient algorithm to find minimal energy conformations easily.
Ant Colony Optimization (ACO) [5] [6], a non-deterministic algorithm, aims to mimic the
behaviors of real ant colonies to solve real-world optimization problems. ACO algorithms are a
class of constructive heuristic algorithms, which build solutions to a given optimization problem,
one solution component at a time, according to a defined set of rules (heuristics), starting with an
empty solution add solution components until a complete solution is built. One of the main
characteristics of an ACO algorithm is the pheromone information which stores information on
good solutions that have been found by ants of former iterations. The pheromone information is
what is transferred from one iteration of the algorithm to the next. An alternative scheme was
introduced called Population based ACO (P-ACO) instead of pheromone information as in ACO,
in P-ACO a population of solutions is transferred from one iteration of the algorithm to the next.
In this paper we provide a description of P-ACO algorithm and applying it for the first time to
solve 3D HP lattice protein structure prediction problem then introduce new approach of
population based ant colony algorithm called Enhanced Population Ant Colony (EP-ACO) to
avoid stagnation problem in P-ACO algorithm. The experimental results based on different test
cases of the PSP show that our algorithm enhances the performance of P- ACO.
3. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
43
The paper is organized as follows: Section 2 describes the HP model and mentions some
heuristics algorithms form the literature for the protein structure prediction problem in the 3D HP
model. An introduction to Population based ACO is given in Section 3. Our algorithm EP-ACO
and application in the 3D HP Protein structure prediction is described in Section 4. The
experiments and the results are presented in section 5. Conclusions are given in Section 6.
2. The HP model
The HP model is considered as the simplest abstraction of the PSP problem. This model divides
the 20 standard amino acids into only two classes, according to their affinity to water:
Hydrophobic amino acid is represented by (H) and Polar amino acid is represented by (P) as
shown in Table 1.
Table 1. The used Hydrophobic-Polar classification of amino acids takes from [7].
The folding of amino acid sequences is represented in a lattice, usually used in either square
lattice (for the bi-dimensional model- 2D HP) or cubic lattice (for the three-dimensional model-
3D HP). Thus, each amino acid is occupies one lattice site, connected to its chain neighbors. After
each amino acid takes one site on lattice, then it will form a shape that is considered to be the
conformation (structure), this confirmation must be self avoiding walk to be valid. An example
for a protein conformation under the 3D HP model is shown in Figure 1. There are several
common ways to represent protein sequence ∈ { , } on lattice, like Cartesian Coordinates,
Internal Coordinate and Distance Geometry [8]. We concern on Internal Coordinate where a
conformation is represented as a string of moving steps on the lattice from one amino acid to next
one. There are two types of Internal Coordinate relative encoding and absolute encoding, in our
study we use absolute encoding where the protein sequence is encoded as a string of character of
absolute direction. The coordination number of the 3D lattice model is six, (each point has six
neighbors). Thus there are six possible absolute moves from a given location. When we use
absolute encoding the candidate solutions are represented as a string of characters
{ , , , , , } representing the six directions: Right, Left, Backward, Forward, Up and
Down, where n is the length of the protein sequence. The example in Figure 1(a) shows a
confirmation of protein sequence S1 in Table 2. Its string representation would be
BUBRFRDLDFURULULDFD.
In the HP model, the energy of a conformation is defined as a number of topological contacts
between H amino acids that are neighbors in the conformation but not successive on the protein
4. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
44
sequence, more specifically, a confirmation that has number of H-H contacts has free energy
E( ) = . (−1) as shown in Figure 1(b).
Figure 1. (a) A sample protein conformation on the 3D HP model which corresponds to protein
sequence S1 in Table 2, green circle represents H amino acids while white circle symbolizes P amino
acids, (b)The energy of this conformation is the number of H-H contacts that are neighbors on
conformation and not successive on sequence, indicted in the Figure by dashed lines so the energy will
be -11
The PSP problem can be formally defined as follows given an amino acid sequence =
{ 1, 2, … , }, where each amino acid in sequence is one of two classes H or P, find an energy
minimizing conformation of , i.e. find ∗
∈ ( ) such that E∗
= E( ∗) =
min{E(c)|c ∈ }, where ( ) is a set of all valid conformations for , It was recently proved that
this problem and several variations of it are NP-hard combinatorial optimization problem [9] [10].
A number of well known heuristic optimization methods have been applied to solve PSP in 3D
HP lattice model. These include: Cutello and Nicosia [8] introduce an Immune Algorithm (IA)
based on the clonal selection principle; they employ a new aging operator and specific mutation
operators. Shmygelska and Hoos [11] use Ant Colony Optimization (ACO) with Local Search
which consists of long-range mutation moves to improve diversity on the solutions. Lin [12]
introduces a hybrid of Genetic Algorithm and Particle Swarm Optimization in order to solve PSP
on 3D HP lattice. Lin [15] presents a modified artificial bee colony algorithm for protein structure
prediction on lattice models.
3. Population Based ACO (P-ACO)
P-ACO has been proposed by Guntsch and Middendorf [13] and it is introduce a new way for
updating pheromone matrix, (where as genetic algorithm) a population of solutions is
directly transferred to next iteration, these solutions are then used to compute pheromone
information for ants of new iteration where for every solution in the population some
amount of pheromone added to corresponding edges. In more detail the first generation of
ants works in the same way as in standard ant colony algorithm i.e. the ants search
solutions using the initial pheromone matrix. But no pheromone evaporation is done. The
best solution is then put in the (initially empty) population Q After k generations there
exactly k solutions in the population. From generation k+1 on:
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45
One solution Qout must leave population, the solution leaves the population is decided by
update strategy and when ever solution Qout leave population the corresponding amount
of pheromone is subtracted from the elements of the pheromone matrix which called
negative update (i.e. it correspond to evaporation ).
rs rs rsa Qout . (1)
One solution Qin is entering population and some amounts of pheromone are added to the
edges presented that solution which called positive update.
rs rs rsa Qin
. (2)
The amount which added or subtracted from pheromone matrix is defined as numerical
number.
The algorithm representation of P-ACO is provided in Figure 2.
Figure 2. P-ACO algorithm
A subtle difference between ACO and P-ACO is the introduction of the solution storage and the
pheromone update process. There are many update strategies to decide which solution will
be deleted from the population and which solution will be remain in P-ACO algorithm, in
quality update strategy if a new solution is better (in terms of quality) than the worst
quality member of the population, the new solution replaces the worst solution, otherwise
there is no change to the population. The aim of this strategy is that the population will retain
good solutions which may have been found earlier in the search process. A possible weakness of
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46
this strategy is that there is no way to ensure that the population does not end up with what are
essentially multiple copies of the same solution.
4. Our Algorithm Enhanced Population Ant Colony (EP-ACO)
P-ACO is developed especially for dynamic problem such as DTSPs, but the stagnation behavior
remains unsolved since identical ants may be stored in the population memory and generate high
intensity of pheromone to a single trail. In our algorithm we try to avoid early stagnation by
maintain a certain level of diversity in the population by adding two main aspects:
1) P-ACO has a strong exploitation capability that allows a fast convergence to a good
quality solution. However, its exploration during the search may be insufficient. We add
procedure to enhance the exploration of new area of search space called Segmentation
where we select best solution in the population and cut it into segments and refold random
segments of this solution trying to find new solution on new area of search space.
2) As in Max-Min Ant System [14] to avoid stagnation, we restart the P-ACO by re-
initializing the pheromone values after r iterations without improvement.
The basic idea of our algorithm, as follow: after creating the initial population, the main loop
repeated until termination condition reached, where m solution is constructed, if the cost of the
best of m solution less than the cost of the worst in the population the P-ACO update rule is used,
then Segmentation procedure is begin by selecting best individual of the population and repeat
for s times; select random point and cut the solution from this random point to segments, select
one segment randomly and refold this segment, finally, if the cost of the new solution is less than
cost of the worst solution in the population the P-ACO update rule is used. The main EP-ACO
algorithm is shown in Figure 3
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47
Figure 3. EP-ACO algorithm
When applying P-ACO to 3D HP model; we want to know how to construct solution and then
follow the storage of solutions and update pheromone rules in Figure 2, in construct solution step:
ants start to construct solution. There are six possible positions on the 3D lattice for every amino
acid. They are the neighbor positions of the precedence amino acid. Since conformations are
rotationally invariant, the position of the first two amino acids can be fixed without loss of
generality. During the construction phase, ants fold a protein from the left end of the sequence
adding one amino acid at a time based on the two sources of information: pheromone matrix
value, and heuristic information. The transition probability to select the position of the next amino
acid is given as:
, =
, . ,
∑ , . ,∈{ , , , , , }
, (3)
where and β are parameters that determine the relative influence of pheromone and heuristic
information. The pheromone values , indicate the amount of pheromone deposited by each ant
on the path (i, d), the heuristic function , used here as illustrated in section 2.
5. Experiments and Results
In this section we apply P-ACO and EP-ACO to solve PSP on 3D HP lattice model, first a
comparative study between simple P-ACO approach and EP-ACO is done to show the
performance of our algorithm. Then the behavior of EP-ACO is compared with state of art
methods used to solve this problem.
For the following experiment results, All experiments were performed on PCs with 2 GHz Intel
core(TM)2 due CPU and 2 MB RAM, running windows 7 (our reference machine), the program
was written using java program language and run-time was measured in terms of CPU time.
Table 2 presents 3D HP instances considered for the computational experiments taken from [8].
For each HP sequence, the column Instance represents the sequence number; the Length
represents the number of amino acid in the protein sequence.
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48
Table 2. HP instances
Instance Length Protein Sequence
S1 20 HPHPPHHPHPPHPHHPPHPH
S2 24 HHPPHPPHPPHPPHPPHPPHPPHH
S3 25 PPHPPHHPPPPHHPPPPHHPPPPHH
S4 36 PPPHHPPHHPPPPPHHHHHHHPPHHPPPPHHPPHPP
S5 48
PPHPPHHPPHHPPPPPHHHHHHHHHHPPPPPPHHPPH
HPPHPPHHHHH
S6 50
HHPHPHPHPHHHHPHPPPHPPPHPPPPHPPPHPPPHP
HHHHPHPHPHPHH
S7 60
PPHHHPHHHHHHHHPPPHHHHHHHHHHPHPPPHHH
HHHHHHHHHPPPPHHHHHHPHHPHP
S8 64
HHHHHHHHHHHHPHPHPPHHPPHHPPHPPHHPPHH
PPHPPHHPPHHPPHPHPHHHHHHHHHHHH
Table3, presents comparison between P-ACO and our proposed EP-ACO, The parameters
settings for the P-ACO and our proposed EP-ACO are = 1, = 3, = 1 number of ants =
100, pop size =10 and ∆ is equal to the energy of that solution that will be added or removed, for
EP-ACO = 50 for small sequence length (n <48) for larger sequence length (n>48) we set
= 100, finally, we reinitialize the pheromone after 3000 iteration. Each run of algorithm ends
when the maximum number of evaluation to fitness function is equal to 10 .
All the experimental results reported in Table 3 are averaged over 30 independent runs. The
column Best means the best found energy (Fitness) value; the Mean is the mean of energy found
over 30 independent runs. As shown in Table 3, our algorithm EP-ACO achieves best result than
P-ACO.
Table 3. Comparison between P-ACO algorithm and EP-ACO algorithm
Instance
P-ACO EP-ACO
Best Mean Best Mean
S1 -11 -11 -11 -11
S2 -13 -12.6 -13 -13
S3 -9 -9 -9 -9
S4 -18 -17.8 -18 -18
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S5 -30 -29.4 -31 -31
S6 -28 -27 -32 31.2
S7 -52 -50.8 -54 -53.2
S8 -54 50.6 -58 -57.3
The performance of the proposed model is compared to the best results obtained by other
algorithms for protein structure prediction in 3D HP model. Table 4 presents the results as
follows: the best energy found by the proposed method (in the last column), the results of Protein
3D HP Model Folding Simulation Using a Hybrid of Genetic Algorithm and Particle Swarm
Optimization (HGAPSO) [12], Immune Algorithm for Protein Structure Prediction (IA) [8] and
Artificial Bee Colony Algorithm For Protein Structure Prediction On Lattice Models (MABC)
[15]. As shown in Table 4, the EP-ACO model is able to identify the protein configurations
having the best Fitness Energy for sequences S5 to S8. The structure of 8 protein sequences can
be clearly seen in Figure 4.
Table 4. The simulation results obtained from the proposed algorithm compared with the methods
given in the literature. Figures in bold indicate the lowest energy
Instance
HGA-PSO IA MABC EP-ACO
Best Best Best Best
S1 -11 -11 -11 -11
S2 -13 -13 -13 -13
S3 -9 -9 -9 -9
S4 -18 -18 -18 -18
S5 -29 -29 -29 -31
S6 -26 -23 -26 -32
S7 -49 -41 -49 -54
S8 - -42 - -58
.
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(S1) (S2)
(S1) (S2)
(S3) (S4)
(S5) (S6)
(S7) (S8)
Figure 4. Results of the structure of 8 protein sequence
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6. Conclusion
In this paper, we presented the Population Based Ant Colony algorithm for 3D HP Protein
Structure Prediction Problem then introduce a new approach called Enhanced Population-based
Ant Colony algorithm (EP-ACO) to avoid stagnation problem in population-based Ant Colony
algorithm and increase exploration in the search space escaping from local optima. It shown
experimentally that our algorithm EP-ACO achieves on nearly all test sequences comparable
results to other state of the art algorithms and is much better than simple P-ACO algorithm.
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Abdel-Rahman Hedar, received his Ph.D. degree in computer science from the
University Kyoto, Japan, in 2004, his MSc and BSc From Assiut university,
Assiut, Egypt, in 1997 and 1993, respectively. He is Director of Quality
Assurance Unit, Associate Professor , Computer Science Department , Faculty
of Computers and Information, Assiut University.
Marghny H. Mohamed, received his Ph.D. degree in computer science from the
University of Kyushu, Japan, in 2001, his MSc and BSc From Assiut
university, Assiut, Egypt, in 1993 and 1988, respectively. He is currently an
associate professor in the Department of Computer Science, and Vice Dean for
Education and Student Affairs of the Faculty of Computers and Information
Systems, University of Assiut, Egypt.