In this paper, we report a multiple sequence alignment result on the basis of 10 amino acid sequences of the M protein, which come from different coronaviruses (4 SARS associated and 6 others known). The alignment mo...In this paper, we report a multiple sequence alignment result on the basis of 10 amino acid sequences of the M protein, which come from different coronaviruses (4 SARS associated and 6 others known). The alignment model was based on the profile HMM (Hidden Markov Model), and the model training was implemented through the SAHMM (Self Adapting Hidden Markov Model) software developed by the authors.展开更多
The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very complex.There is a trade-off in the objectives in the existing techniques of Multiple...The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very complex.There is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in the queue of alignment compared to the well-known algorithmsin multiple sequence alignment.展开更多
Biological sequence alignment is one of the most important problems in computational biology. The objective of the alignment process is to maximize the alignment score between two given sequences of varying or equal l...Biological sequence alignment is one of the most important problems in computational biology. The objective of the alignment process is to maximize the alignment score between two given sequences of varying or equal length. The alignment score of two sequences is calculated based on matches, mismatches and gaps in the alignment. We have proposed a new genetic approach for finding optimized match between two DNA or protein sequences. The process is compared with two well known relevant sequence alignment techniques.展开更多
A fundamental goal in cellular signaling is to understand allosteric communication, the process by which sig-nals originating at one site in a protein propagate reliably to affect distant functional sites. The general...A fundamental goal in cellular signaling is to understand allosteric communication, the process by which sig-nals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Statistical coupling analysis （SCA） is a statistical technique that uses evolutionary data of a protein family to measure correlation between distant functional sites and suggests allosteric communication. In proteins, very distant and small interactions between collections of amino acids provide the communication which can be important for signaling process. In this paper, we present the SCA of protein alignment of the esterase family （pfam ID： PF00756） containing the sequence of antigen 85C secreted by Mycobacterium tuberculosis to identify a subset of interacting residues. Clustering analysis of the pairwise correlation highlighted seven important residue positions in the esterase family alignments. These resi-dues were then mapped on the crystal structure of antigen 85C （PDB ID： 1DQZ）. The mapping revealed corre-lation between 3 distant residues （Asp38, Leu123 and Met125） and suggests allosteric communication between them. This information can be used for a new drug against this fatal disease.展开更多
文摘In this paper, we report a multiple sequence alignment result on the basis of 10 amino acid sequences of the M protein, which come from different coronaviruses (4 SARS associated and 6 others known). The alignment model was based on the profile HMM (Hidden Markov Model), and the model training was implemented through the SAHMM (Self Adapting Hidden Markov Model) software developed by the authors.
基金The authors extend their appreciation to the Deanship of Scientific Research at Jouf University for funding this work through research Grant No(DSR2020–01–414).
文摘The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very complex.There is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in the queue of alignment compared to the well-known algorithmsin multiple sequence alignment.
文摘Biological sequence alignment is one of the most important problems in computational biology. The objective of the alignment process is to maximize the alignment score between two given sequences of varying or equal length. The alignment score of two sequences is calculated based on matches, mismatches and gaps in the alignment. We have proposed a new genetic approach for finding optimized match between two DNA or protein sequences. The process is compared with two well known relevant sequence alignment techniques.
文摘A fundamental goal in cellular signaling is to understand allosteric communication, the process by which sig-nals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Statistical coupling analysis （SCA） is a statistical technique that uses evolutionary data of a protein family to measure correlation between distant functional sites and suggests allosteric communication. In proteins, very distant and small interactions between collections of amino acids provide the communication which can be important for signaling process. In this paper, we present the SCA of protein alignment of the esterase family （pfam ID： PF00756） containing the sequence of antigen 85C secreted by Mycobacterium tuberculosis to identify a subset of interacting residues. Clustering analysis of the pairwise correlation highlighted seven important residue positions in the esterase family alignments. These resi-dues were then mapped on the crystal structure of antigen 85C （PDB ID： 1DQZ）. The mapping revealed corre-lation between 3 distant residues （Asp38, Leu123 and Met125） and suggests allosteric communication between them. This information can be used for a new drug against this fatal disease.
