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11 pages, 2454 KiB

Open AccessArticle

iPVP-MCV: A Multi-Classifier Voting Model for the Accurate Identification of Phage Virion Proteins

by Haitao Han, Wenhong Zhu, Chenchen Ding and Taigang Liu

Symmetry 2021, 13(8), 1506; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081506 - 17 Aug 2021

Cited by 7 | Viewed by 2771

The classic structure of a bacteriophage is commonly characterized by complex symmetry. The head of the structure features icosahedral symmetry, whereas the tail features helical symmetry. The phage virion protein (PVP), a type of bacteriophage structural protein, is an essential material of the [...] Read more.

The classic structure of a bacteriophage is commonly characterized by complex symmetry. The head of the structure features icosahedral symmetry, whereas the tail features helical symmetry. The phage virion protein (PVP), a type of bacteriophage structural protein, is an essential material of the infectious viral particles and is responsible for multiple biological functions. Accurate identification of PVPs is of great significance for comprehending the interaction between phages and host bacteria and developing new antimicrobial drugs or antibiotics. However, traditional experimental approaches for identifying PVPs are often time-consuming and laborious. Therefore, the development of computational methods that can efficiently and accurately identify PVPs is desired. In this study, we proposed a multi-classifier voting model called iPVP-MCV to enhance the predictive performance of PVPs based on their amino acid sequences. First, three types of evolutionary features were extracted from the position-specific scoring matrix (PSSM) profiles to represent PVPs and non-PVPs. Then, a set of baseline models were trained based on the support vector machine (SVM) algorithm combined with each type of feature descriptors. Finally, the outputs of these baseline models were integrated to construct the proposed method iPVP-MCV by using the majority voting strategy. Our results demonstrated that the proposed iPVP-MCV model was superior to existing methods when performing the rigorous independent dataset test. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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10 pages, 1244 KiB

Open AccessArticle

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

by Dae Yeong Lim, Mobeen Ur Rehman and Kil To Chong

Symmetry 2021, 13(5), 899; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13050899 - 19 May 2021

Cited by 10 | Viewed by 1932

Abstract

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the [...] Read more.

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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19 pages, 4122 KiB

Open AccessArticle

iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions

by Sheraz Naseer, Rao Faizan Ali, Amgad Muneer and Suliman Mohamed Fati

Symmetry 2021, 13(4), 560; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13040560 - 29 Mar 2021

Cited by 24 | Viewed by 3494

Abstract

Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like [...] Read more.

Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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18 pages, 8952 KiB

Open AccessArticle

Application of Wind Tunnel Device for Evaluation of Biokinetic Parameters of Running

by Brane Širok, Jurij Gostiša, Matej Sečnik, Krzysztof Mackala and Milan Čoh

Symmetry 2021, 13(3), 505; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13030505 - 19 Mar 2021

Viewed by 1887

Abstract

The aim of the study was the application of high-tech wind tunnel device to identify the changes in the biokinetic parameters of running performed on the specially designed treadmill. The research was carried out in the “Planica Nordic Centre—PNC” in the wind tunnel [...] Read more.

The aim of the study was the application of high-tech wind tunnel device to identify the changes in the biokinetic parameters of running performed on the specially designed treadmill. The research was carried out in the “Planica Nordic Centre—PNC” in the wind tunnel system, where the AirRunner Assault treadmill, which was equipped with four sensors measuring the vertical and horizontal ground reaction forces, was installed. To obtain biokinetic data, the runners performed the treadmill’s run under conditions of airflow directed at each participant’s back (backwind speeds +3 m/s and +5 m/s) and the chest (headwind speeds −5 m/s and −7 m/s). The runner’s speed was measured via image analysis using a DSLR camera and markers on the belt of the treadmill. Additionally, a high-speed camera synchronised to the force acquisition system was used to analyse the contact phase via comparison of foot placement and time series of the ground reaction forces. The contact phases of the running step were found to be longer than the flight phases, with their duration ranging from 0.15 to 0.20 s and the maximum forces at take-off were found to be greater than when running with the backwind. It should be noted that the application of high-tech devices wind tunnel and treadmill were found to be sufficiently accurate to perform kinetic measurements of running parameters in changing conditions, such as resistance and assistance (facilitating). Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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19 pages, 6325 KiB

Open AccessArticle

GENAVOS: A New Tool for Modelling and Analyzing Cancer Gene Regulatory Networks Using Delayed Nonlinear Variable Order Fractional System

by Hanif Yaghoobi, Keivan Maghooli, Masoud Asadi-Khiavi and Nader Jafarnia Dabanloo

Symmetry 2021, 13(2), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13020295 - 09 Feb 2021

Viewed by 2357

Abstract

Gene regulatory networks (GRN) are one of the etiologies associated with cancer. Their dysregulation can be associated with cancer formation and asymmetric cellular functions in cancer stem cells, leading to disease persistence and resistance to treatment. Systems that model the complex dynamics of [...] Read more.

Gene regulatory networks (GRN) are one of the etiologies associated with cancer. Their dysregulation can be associated with cancer formation and asymmetric cellular functions in cancer stem cells, leading to disease persistence and resistance to treatment. Systems that model the complex dynamics of these networks along with adapting to partially known real omics data are closer to reality and may be useful to understand the mechanisms underlying neoplastic phenomena. In this paper, for the first time, modelling of GRNs is performed using delayed nonlinear variable order fractional (VOF) systems in the state space by a new tool called GENAVOS. Although the tool uses gene expression time series data to identify and optimize system parameters, it also models possible epigenetic signals, and the results show that the nonlinear VOF systems have very good flexibility in adapting to real data. We found that GRNs in cancer cells actually have a larger delay parameter than in normal cells. It is also possible to create weak chaotic, periodic, and quasi-periodic oscillations by changing the parameters. Chaos can be associated with the onset of cancer. Our findings indicate a profound effect of time-varying orders on these networks, which may be related to a type of cellular epigenetic memory. By changing the delay parameter and the variable order functions (possible epigenetics signals) for a normal cell system, its behaviour becomes quite similar to the behaviour of a cancer cell. This work confirms the effective role of the miR-17-92 cluster as an epigenetic factor in the cancer cell cycle. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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24 pages, 1415 KiB

Open AccessFeature PaperArticle

An Automated Model Reduction Method for Biochemical Reaction Networks

by Manvel Gasparyan, Arnout Van Messem and Shodhan Rao

Symmetry 2020, 12(8), 1321; https://0-doi-org.brum.beds.ac.uk/10.3390/sym12081321 - 07 Aug 2020

Cited by 5 | Viewed by 2854

Abstract

We propose a new approach to the model reduction of biochemical reaction networks governed by various types of enzyme kinetics rate laws with non-autocatalytic reactions, each of which can be reversible or irreversible. This method extends the approach for model reduction previously proposed [...] Read more.

We propose a new approach to the model reduction of biochemical reaction networks governed by various types of enzyme kinetics rate laws with non-autocatalytic reactions, each of which can be reversible or irreversible. This method extends the approach for model reduction previously proposed by Rao et al. which proceeds by the step-wise reduction in the number of complexes by Kron reduction of the weighted Laplacian corresponding to the complex graph of the network. The main idea in the current manuscript is based on rewriting the mathematical model of a reaction network as a model of a network consisting of linkage classes that contain more than one reaction. It is done by joining certain distinct linkage classes into a single linkage class by using the conservation laws of the network. We show that this adjustment improves the extent of applicability of the method proposed by Rao et al. We automate the entire reduction procedure using Matlab. We test our automated model reduction to two real-life reaction networks, namely, a model of neural stem cell regulation and a model of hedgehog signaling pathway. We apply our reduction approach to meaningfully reduce the number of complexes in the complex graph corresponding to these networks. When the number of species’ concentrations in the model of neural stem cell regulation is reduced by 33.33%, the difference between the dynamics of the original model and the reduced model, quantified by an error integral, is only 4.85%. Likewise, when the number of species’ concentrations is reduced by 33.33% in the model of hedgehog signaling pathway, the difference between the dynamics of the original model and the reduced model is only 6.59%. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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12 pages, 3309 KiB

Open AccessArticle

Optimization of Public Health Education Parameters for Controlling the Spread of HIV/AIDS Infection

by Mohammad Hossein Ostadzad, Salman Baroumand and Mohammad Reza Mahmoudi

Symmetry 2020, 12(4), 659; https://0-doi-org.brum.beds.ac.uk/10.3390/sym12040659 - 22 Apr 2020

Cited by 1 | Viewed by 2337

Abstract

Due to the prevalence of Human Immuno-deficiency Virus/Acquired Immuno-Deficiency Syndrome (HIV/AIDS) infection in society and the importance of preventing the spread of this disease, a mathematical model for sexual transmission of HIV/AIDS epidemic with asymptomatic and symptomatic phase and public health education is [...] Read more.

Due to the prevalence of Human Immuno-deficiency Virus/Acquired Immuno-Deficiency Syndrome (HIV/AIDS) infection in society and the importance of preventing the spread of this disease, a mathematical model for sexual transmission of HIV/AIDS epidemic with asymptomatic and symptomatic phase and public health education is stated as a symmetric system of differential equations in order to reduce the spread of this infectious disease. It is demonstrated that public health education has a considerable effect on the prevalence of the disease. Moreover, the cost of education is very high and for this reason, a cost-optimal control is applied to provide the best possible combination of the parameters corresponding to education in controlling the spread of the disease by means of the Genetic Algorithm (GA) and Simulated Annealing (SA). Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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Review

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37 pages, 1053 KiB

Open AccessReview

Racemization in Post-Translational Modifications Relevance to Protein Aging, Aggregation and Neurodegeneration: Tip of the Iceberg

by Victor V. Dyakin, Thomas M. Wisniewski and Abel Lajtha

Symmetry 2021, 13(3), 455; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13030455 - 11 Mar 2021

Cited by 11 | Viewed by 3433

Abstract

Homochirality of DNA and prevalent chirality of free and protein-bound amino acids in a living organism represents the challenge for modern biochemistry and neuroscience. The idea of an association between age-related disease, neurodegeneration, and racemization originated from the studies of fossils and cataract [...] Read more.

Homochirality of DNA and prevalent chirality of free and protein-bound amino acids in a living organism represents the challenge for modern biochemistry and neuroscience. The idea of an association between age-related disease, neurodegeneration, and racemization originated from the studies of fossils and cataract disease. Under the pressure of new results, this concept has a broader significance linking protein folding, aggregation, and disfunction to an organism’s cognitive and behavioral functions. The integrity of cognitive function is provided by a delicate balance between the evolutionarily imposed molecular homo-chirality and the epigenetic/developmental impact of spontaneous and enzymatic racemization. The chirality of amino acids is the crucial player in the modulation the structure and function of proteins, lipids, and DNA. The collapse of homochirality by racemization is the result of the conformational phase transition. The racemization of protein-bound amino acids (spontaneous and enzymatic) occurs through thermal activation over the energy barrier or by the tunnel transfer effect under the energy barrier. The phase transition is achieved through the intermediate state, where the chirality of alpha carbon vanished. From a thermodynamic consideration, the system in the homo-chiral (single enantiomeric) state is characterized by a decreased level of entropy. The oscillating protein chirality is suggesting its distinct significance in the neurotransmission and flow of perceptual information, adaptive associative learning, and cognitive laterality. The common pathological hallmarks of neurodegenerative disorders include protein misfolding, aging, and the deposition of protease-resistant protein aggregates. Each of the landmarks is influenced by racemization. The brain region, cell type, and age-dependent racemization critically influence the functions of many intracellular, membrane-bound, and extracellular proteins including amyloid precursor protein (APP), TAU, PrP, Huntingtin, α-synuclein, myelin basic protein (MBP), and collagen. The amyloid cascade hypothesis in Alzheimer’s disease (AD) coexists with the failure of amyloid beta (Aβ) targeting drug therapy. According to our view, racemization should be considered as a critical factor of protein conformation with the potential for inducing order, disorder, misfolding, aggregation, toxicity, and malfunctions. Full article

(This article belongs to the Special Issue Bioinformatics and Computational Biology)

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