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Article ; Online: Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm.

Mohammadi, Rahele / Neamatollahi, Peyman / Moradi, Morteza / Naghibzadeh, Mahmoud / Savadi, Abdorreza

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like ...

Abstract	Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3355964
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Article ; Online: RPTRF: A rapid perfect tandem repeat finder tool for DNA sequences.

Behboudi, Reza / Nouri-Baygi, Mostafa / Naghibzadeh, Mahmoud

Bio Systems

2023 Volume 226, Page(s) 104869

Abstract: The sequencing of eukaryotic genomes has shown that tandem repeats are abundant in their sequences. In addition to affecting some cellular processes, tandem repeats in the genome may be associated with specific diseases and have been the key to resolving ...

Abstract	The sequencing of eukaryotic genomes has shown that tandem repeats are abundant in their sequences. In addition to affecting some cellular processes, tandem repeats in the genome may be associated with specific diseases and have been the key to resolving criminal cases. Any tool developed for detecting tandem repeats must be accurate, fast, and useable in thousands of laboratories worldwide, including those with not very advanced computing capabilities. The proposed method, the Rapid Perfect Tandem Repeat Finder (RPTRF), minimizes the need for excess character comparison processing by indexing the input file and significantly helps to accelerate and prepare the output without artifacts by using an interval tree in the filtering section. The experiments demonstrated that the RPTRF is very fast in discovering all perfect tandem repeats of all categories of any genomic sequences. Although the detection of imperfect TRs is not the focus of the RPTRF, comparisons show that it even outperforms some other tools (in five selected gold standards) designed explicitly for this purpose. The implemented tool and how to use it are available on GitHub.
MeSH term(s)	Base Sequence ; Genomics ; Tandem Repeat Sequences/genetics ; Sequence Analysis, DNA
Language	English
Publishing date	2023-02-27
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	186234-0
ISSN	1872-8324 ; 0303-2647
ISSN (online)	1872-8324
ISSN	0303-2647
DOI	10.1016/j.biosystems.2023.104869
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Article ; Online: SSA: Subset sum approach to protein β-sheet structure prediction.

Eghdami, Mahdie / Naghibzadeh, Mahmoud

Computational biology and chemistry

2021 Volume 94, Page(s) 107552

Abstract: The three-dimensional structures of proteins provide their functions and incorrect folding of its β-strands can be the cause of many diseases. There are two major approaches for determining protein structures: computational prediction and experimental ... ...

Abstract	The three-dimensional structures of proteins provide their functions and incorrect folding of its β-strands can be the cause of many diseases. There are two major approaches for determining protein structures: computational prediction and experimental methods that employ technologies such as Cryo-electron microscopy. Due to experimental methods's high costs, extended wait times for its lengthy processes, and incompleteness of results, computational prediction is an attractive alternative. As the focus of the present paper, β-sheet structure prediction is a major portion of overall protein structure prediction. Prediction of other substructures, such as α-helices, is simpler with lower computational time complexities. Brute force methods are the most common approach and dynamic programming is also utilized to generate all possible conformations. The current study introduces the Subset Sum Approach (SSA) for the direct search space generation method, which is shown to outperform the dynamic programming approach in terms of both time and space. For the first time, the present work has calculated both the state space cardinality of the dynamic programming approach and the search space cardinality of the general brute force approaches. In regard to a set of pruning rules, SSA has demonstrated higher efficiency with respect to both time and accuracy in comparison to state-of-the-art methods.
MeSH term(s)	Protein Conformation, beta-Strand ; Proteins/chemistry ; Software
Chemical Substances	Proteins
Language	English
Publishing date	2021-07-31
Publishing country	England
Document type	Journal Article
ISSN	1476-928X
ISSN (online)	1476-928X
DOI	10.1016/j.compbiolchem.2021.107552
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Article ; Online: The Eficient Alignment of Long DNA Sequences Using Divide and Conquer Approach

Mahmoud Naghibzadeh / Samira Babaei / Behshid Behkmal / Mojtaba Hatami

International Journal of Information and Communication Technology Research, Vol 14, Iss 3, Pp 48-

2022 Volume 56

Abstract: discovering mutations in DNA sequences is the most common approach to diagnosing many genome-related diseases. The optimal alignment of DNA sequences is a reliable approach to discover mutations in one sequence in comparison to the reference sequence. ... ...

Abstract	discovering mutations in DNA sequences is the most common approach to diagnosing many genome-related diseases. The optimal alignment of DNA sequences is a reliable approach to discover mutations in one sequence in comparison to the reference sequence. Needleman-Wunsch is the most applicable software for optimal alignment of the sequences and Smith-Waterman is the most applicable one for local optimal alignment of sequences. Their performances are excellent with short sequences, but as the sequences become long their performance degeneration grows exponentially to the point that it is practically impossible to align the sequences such as compete human DNAs. Therefore, many researches are done or being conducted to find ways of performing the alignment with tolerable time and memory consumptions. One such effort is breaking the sequences into same number of parts and align corresponding parts together to produce the overall alignment. With this, there are three achievements simultaneously: run time reduction, main memory utilization reduction, and the possibility to better utilize multiprocessors, multicores and General-Purpose Graphic Processing Units (GPGPUs). In this research, the method for breaking long sequences into smaller parts is based on the divide and conquer approach. The breaking points are selected along the longest common subsequence of the current sequences. The method is evaluated to be very efficient with respect to both time and main memory utilization which are the two confining factors.
Keywords	dna sequence alignment ; divide and conquer approach ; longest common subsequence ; big genome data ; desease diagnosis ; Information technology ; T58.5-58.64 ; Telecommunication ; TK5101-6720 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	004
Language	English
Publishing date	2022-09-01T00:00:00Z
Publisher	Iran Telecom Research Center
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Parallelizing Assignment Problem with DNA Strands.

Khorsand, Babak / Savadi, Abdorreza / Naghibzadeh, Mahmoud

Iranian journal of biotechnology

2020 Volume 18, Issue 1, Page(s) e2547

Abstract: Background: Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the ... ...

Abstract	Background: Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the effective solutions for NP-hard problems. However, solving these problems in polynomial time needs massive parallel machines and is not applicable up to now. Objectives: DNA (Deoxyribonucleic acid) computing provides a fantastic method to solve NP-hard problems in polynomial time. Accordingly, one of the famous NP-hard problems is assignment problem, which is designed to find the best assignment of n jobs to n persons in a way that it could maximize the profit or minimize the cost. Material and methods: Applying bio molecular operations of Adelman Lipton model, a novel parallel DNA algorithm have been proposed for solving the assignment problem. Results: The proposed algorithm can solve the problem in time complexity, and just O(n Conclusions: In this article, using DNA computing, we proposed a parallel DNA algorithm to solve the assignment problem in linear time.
Language	English
Publishing date	2020-01-01
Publishing country	Iran
Document type	Journal Article
ZDB-ID	2223669-7
ISSN	2322-2921 ; 1728-3043
ISSN (online)	2322-2921
ISSN	1728-3043
DOI	10.30498/IJB.2020.195413.2547
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Statistics and Patterns of Occurrence of Simple Tandem Repeats in SARS-CoV-1 and SARS-CoV-2 Genomic Data.

Savari, Hossein / Shafiey, Hassan / Savadi, Abdorreza / Saadati, Nayyereh / Naghibzadeh, Mahmoud

Data in brief

2021 Volume 36, Page(s) 107057

Abstract: ... Naghibzadeh et al. 2020]. Simple tandem repeats (microsatellites, STR) are extracted and investigated across ...

Abstract	The data presented in this article is related to the research article entitled "Developing an ultra-efficient microsatellite discoverer to find structural differences between SARS-CoV-1 and Covid-19" [Naghibzadeh et al. 2020]. Simple tandem repeats (microsatellites, STR) are extracted and investigated across all viral families from four main viral realms. An ultra-efficient and reliable software, which is recently developed by the authors and published in the above-mentioned article, is used for extracting STRs. The analysis is done for k-mer tandem repeats where k varies from one to seven. In particular the frequency of trimer STRs is shown to be low in RNA viruses compared with DNA viruses. Special attention is paid to seven zoonotic viruses from family Coronaviridae which caused several severe human crises during last two decades including MERS, SARS 2003 and Covid-19.
Language	English
Publishing date	2021-04-21
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409 ; 2352-3409
ISSN (online)	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2021.107057
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: SARS-CoV-2-human protein-protein interaction network.

Khorsand, Babak / Savadi, Abdorreza / Naghibzadeh, Mahmoud

Informatics in medicine unlocked

2020 Volume 20, Page(s) 100413

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. ... ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.
Keywords	covid19
Language	English
Publishing date	2020-08-13
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2020.100413
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network.

Khorsand, Babak / Savadi, Abdorreza / Zahiri, Javad / Naghibzadeh, Mahmoud

Mathematical biosciences and engineering : MBE

2020 Volume 17, Issue 4, Page(s) 3109–3129

Abstract: More than ten million deaths make influenza virus one of the deadliest of history. About half a million sever illnesses are annually reported consequent of influenza. Influenza is a parasite which needs the host cellular machinery to replicate its genome. ...

Abstract	More than ten million deaths make influenza virus one of the deadliest of history. About half a million sever illnesses are annually reported consequent of influenza. Influenza is a parasite which needs the host cellular machinery to replicate its genome. To reach the host, viral proteins need to interact with the host proteins. Therefore, identification of host-virus protein interaction network (HVIN) is one of the crucial steps in treating viral diseases. Being expensive, time-consuming and laborious of HVIN experimental identification, force the researches to use computational methods instead of experimental ones to obtain a better understanding of HVIN. In this study, several features are extracted from physicochemical properties of amino acids, combined with different centralities of human protein-protein interaction network (HPPIN) to predict protein-protein interactions between human proteins and Alphainfluenzavirus proteins (HI-PPIs). Ensemble learning methods were used to predict such PPIs. Our model reached 0.93 accuracy, 0.91 sensitivity and 0.95 specificity. Moreover, a database including 694522 new PPIs was constructed by prediction results of the model. Further analysis showed that HPPIN centralities, gene ontology semantic similarity and conjoint triad of virus proteins are the most important features to predict HI-PPIs.
MeSH term(s)	Alphavirus ; Host-Pathogen Interactions ; Humans ; Influenza, Human ; Orthomyxoviridae ; Protein Interaction Mapping ; Protein Interaction Maps
Language	English
Publishing date	2020-09-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2020176
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Comprehensive host-pathogen protein-protein interaction network analysis.

Khorsand, Babak / Savadi, Abdorreza / Naghibzadeh, Mahmoud

BMC bioinformatics

2020 Volume 21, Issue 1, Page(s) 400

Abstract: Background: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to ... ...

Abstract	Background: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. Results: As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. Conclusions: Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.
MeSH term(s)	Communicable Diseases/metabolism ; Communicable Diseases/pathology ; Communicable Diseases/virology ; Databases, Protein ; Host-Pathogen Interactions ; Humans ; Protein Interaction Maps ; Proteins/metabolism ; User-Computer Interface ; Viruses/pathogenicity
Chemical Substances	Proteins
Language	English
Publishing date	2020-09-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-020-03706-z
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Article ; Online: LPTD: a novel linear programming-based topology determination method for cryo-EM maps.

Behkamal, Bahareh / Naghibzadeh, Mahmoud / Pagnani, Andrea / Saberi, Mohammad Reza / Al Nasr, Kamal

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 10, Page(s) 2734–2741

Abstract: Summary: Topology determination is one of the most important intermediate steps toward building the atomic structure of proteins from their medium-resolution cryo-electron microscopy (cryo-EM) map. The main goal in the topology determination is to ... ...

Abstract	Summary: Topology determination is one of the most important intermediate steps toward building the atomic structure of proteins from their medium-resolution cryo-electron microscopy (cryo-EM) map. The main goal in the topology determination is to identify correct matches (i.e. assignment and direction) between secondary structure elements (SSEs) (α-helices and β-sheets) detected in a protein sequence and cryo-EM density map. Despite many recent advances in molecular biology technologies, the problem remains a challenging issue. To overcome the problem, this article proposes a linear programming-based topology determination (LPTD) method to solve the secondary structure topology problem in three-dimensional geometrical space. Through modeling of the protein's sequence with the aid of extracting highly reliable features and a distance-based scoring function, the secondary structure matching problem is transformed into a complete weighted bipartite graph matching problem. Subsequently, an algorithm based on linear programming is developed as a decision-making strategy to extract the true topology (native topology) between all possible topologies. The proposed automatic framework is verified using 12 experimental and 15 simulated α-β proteins. Results demonstrate that LPTD is highly efficient and extremely fast in such a way that for 77% of cases in the dataset, the native topology has been detected in the first rank topology in <2 s. Besides, this method is able to successfully handle large complex proteins with as many as 65 SSEs. Such a large number of SSEs have never been solved with current tools/methods. Availability and implementation: The LPTD package (source code and data) is publicly available at https://github.com/B-Behkamal/LPTD. Moreover, two test samples as well as the instruction of utilizing the graphical user interface have been provided in the shared readme file. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Cryoelectron Microscopy/methods ; Models, Molecular ; Programming, Linear ; Protein Conformation ; Protein Structure, Secondary ; Proteins/chemistry
Chemical Substances	Proteins
Language	English
Publishing date	2022-05-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac170
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