Pub Date : 2024-09-30DOI: 10.1016/j.compbiolchem.2024.108231
Hui Tian , Ran Tang
Background
Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction.
Methods
In this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method.
Results
Data augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement.
Conclusions
In summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.
{"title":"Prediction of Crohn's disease based on deep feature recognition","authors":"Hui Tian , Ran Tang","doi":"10.1016/j.compbiolchem.2024.108231","DOIUrl":"10.1016/j.compbiolchem.2024.108231","url":null,"abstract":"<div><h3>Background</h3><div>Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the use of deep learning to decode genetic information for disease prediction.</div></div><div><h3>Methods</h3><div>In this paper, we present a deep learning-based model that utilizes a sequential convolutional attention network (SCAN) for feature extraction, incorporates adaptive additive interval losses to enhance these features, and employs support vector machines (SVM) for classification. To address the challenge of unbalanced Crohn's disease samples, we propose a random noise one-hot encoding data augmentation method.</div></div><div><h3>Results</h3><div>Data augmentation with random noise accelerates training convergence, while SCAN-SVM effectively extracts features with adaptive additive interval loss enhancing differentiation. Our approach outperforms benchmark methods, achieving an average accuracy of 0.80 and a kappa value of 0.76, and we validate the effectiveness of feature enhancement.</div></div><div><h3>Conclusions</h3><div>In summary, we use deep feature recognition to effectively analyze the potential information in genes, which has a good application potential for gene analysis and prediction of Crohn's disease.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108231"},"PeriodicalIF":2.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1016/j.compbiolchem.2024.108217
Aliza Naz, Fouzia Gul, Syed Sikander Azam
Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidans. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with ATP and hydrolyzing it. Therefore, targeting it was thought to have a profound effect on the normal functioning of the whole T2SS. A. xylosoxidans is a Gram-negative bacterium that poses a rising concern to immunocompromised people. It is responsible for many opportunistic infections mostly in people with cystic fibrosis. Due to its intrinsic and acquired resistance mechanisms, it is challenging to treat. In this current study, an extensive machine learning-enabled computational investigation was carried out. Drug libraries were screened using machine learning random forest algorithm trained on non-redundant dataset of 8722 antibacterial compounds with reported IC50 values. Active compounds were then further subjected to molecular docking. To unravel the dynamics and better understand the stability of complexes, the top complexes were subjected to MD Simulations followed by various post-simulation analyses including Trajectory analysis, Atom Contacts, SASA, Hydrogen Bond, RDF, binding free energy calculations, PCA, and AFD analysis. Findings from the study unanimously unveiled Asinex-BAS00263070–28551 as the best inhibitor as it instigated the recursive dynamics of the target by making key hydrogen bond interactions with Walker A motif, suggesting it could serve as the promising drug candidate against GspE. Further experimental in-vivo and in-vitro validation is still required to authenticate the therapeutic effects of these drugs.
{"title":"Recursive dynamics of GspE through machine learning enabled identification of inhibitors","authors":"Aliza Naz, Fouzia Gul, Syed Sikander Azam","doi":"10.1016/j.compbiolchem.2024.108217","DOIUrl":"10.1016/j.compbiolchem.2024.108217","url":null,"abstract":"<div><div>Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including <em>Achromobacter xylosoxidans</em>. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with ATP and hydrolyzing it. Therefore, targeting it was thought to have a profound effect on the normal functioning of the whole T2SS. <em>A. xylosoxidans</em> is a Gram-negative bacterium that poses a rising concern to immunocompromised people. It is responsible for many opportunistic infections mostly in people with cystic fibrosis. Due to its intrinsic and acquired resistance mechanisms, it is challenging to treat. In this current study, an extensive machine learning-enabled computational investigation was carried out. Drug libraries were screened using machine learning random forest algorithm trained on non-redundant dataset of 8722 antibacterial compounds with reported IC<sub>50</sub> values. Active compounds were then further subjected to molecular docking. To unravel the dynamics and better understand the stability of complexes, the top complexes were subjected to MD Simulations followed by various post-simulation analyses including Trajectory analysis, Atom Contacts, SASA, Hydrogen Bond, RDF, binding free energy calculations, PCA, and AFD analysis. Findings from the study unanimously unveiled Asinex-BAS00263070–28551 as the best inhibitor as it instigated the recursive dynamics of the target by making key hydrogen bond interactions with Walker A motif, suggesting it could serve as the promising drug candidate against GspE. Further experimental in-vivo and in-vitro validation is still required to authenticate the therapeutic effects of these drugs.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108217"},"PeriodicalIF":2.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1016/j.compbiolchem.2024.108228
P. Pradeep, Kamalakannan J.
PD is one of the neurodegenerative illnesses affects 1–2 individuals per 1000 people over the age of 60 and has a 1 % prevalence rate. It affects both the non-motor and motor aspects of movement, including initiation, execution, and planning. Prior to behavioral and cognitive abnormalities like dementia, movement-related symptoms including stiffness, tremor, and initiation issues may be observed. Patients with PD have substantial reductions in social interactions, quality of life (QoL), and familial ties, as well as significant financial burdens on both the individual and societal levels. The healthcare industry is mostly using ML approaches with the modalities like image, signal, and data as well. Therefore, this survey aims to conduct a review of 50 articles on Parkinson disease diagnosis using different modalities. The survey includes (i) Classifying multimodal articles on Parkinson disease diagnosis (image, signal, data) using various machine learning, deep learning, and other approaches. (ii) Analyzing different datasets, simulation tools used in the existing papers. (iii)Examining certain performance measures, assessing the best performance, and chronological review of reviewed paper. Finally, the review determines the research gaps and obstacles in this research topic.
{"title":"Comprehensive review of literature on Parkinson’s disease diagnosis","authors":"P. Pradeep, Kamalakannan J.","doi":"10.1016/j.compbiolchem.2024.108228","DOIUrl":"10.1016/j.compbiolchem.2024.108228","url":null,"abstract":"<div><div>PD is one of the neurodegenerative illnesses affects 1–2 individuals per 1000 people over the age of 60 and has a 1 % prevalence rate. It affects both the non-motor and motor aspects of movement, including initiation, execution, and planning. Prior to behavioral and cognitive abnormalities like dementia, movement-related symptoms including stiffness, tremor, and initiation issues may be observed. Patients with PD have substantial reductions in social interactions, quality of life (QoL), and familial ties, as well as significant financial burdens on both the individual and societal levels. The healthcare industry is mostly using ML approaches with the modalities like image, signal, and data as well. Therefore, this survey aims to conduct a review of 50 articles on Parkinson disease diagnosis using different modalities. The survey includes (i) Classifying multimodal articles on Parkinson disease diagnosis (image, signal, data) using various machine learning, deep learning, and other approaches. (ii) Analyzing different datasets, simulation tools used in the existing papers. (iii)Examining certain performance measures, assessing the best performance, and chronological review of reviewed paper. Finally, the review determines the research gaps and obstacles in this research topic.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108228"},"PeriodicalIF":2.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.compbiolchem.2024.108224
Yiqiong Bao , Ran Xu , Jingjing Guo
Participating in the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, TYK2 emerges as a promising therapy target in controlling various autoimmune diseases, including psoriasis and multiple sclerosis. Deucravacitinib (DEU) is a novel oral TYK2-specific inhibitor approved in 2022 that is clinically effective in moderate to severe psoriasis trials. Upon the AlphaFold2 predicted TYK2 pseudokinase domain (JH2) and kinase domain (JH1), we explored the details of the underlined allosteric inhibition mechanism on TYK2 JH2-JH1 with the aid of molecular dynamics simulation. Our results suggest that the allosteric inhibition of DEU on TYK2 is accomplished by affecting the JH2-JH1 interface and hampering the state transition and ATP binding in JH1. Particularly, DEU binding stabilized the autoinhibitory interface between JH2 and JH1 while disrupting the formation of the activation interface. As a result, the negative regulation of JH2 on JH1 was greatly enhanced. These findings offer additional details on the pseudokinase-dependent autoinhibition of the JAK kinase domain and provide theoretical support for the JH2-targeted drug discovery in JAK members.
{"title":"The multiple-action allosteric inhibition of TYK2 by deucravacitinib: Insights from computational simulations","authors":"Yiqiong Bao , Ran Xu , Jingjing Guo","doi":"10.1016/j.compbiolchem.2024.108224","DOIUrl":"10.1016/j.compbiolchem.2024.108224","url":null,"abstract":"<div><div>Participating in the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway, TYK2 emerges as a promising therapy target in controlling various autoimmune diseases, including psoriasis and multiple sclerosis. Deucravacitinib (DEU) is a novel oral TYK2-specific inhibitor approved in 2022 that is clinically effective in moderate to severe psoriasis trials. Upon the AlphaFold2 predicted TYK2 pseudokinase domain (JH2) and kinase domain (JH1), we explored the details of the underlined allosteric inhibition mechanism on TYK2 JH2-JH1 with the aid of molecular dynamics simulation. Our results suggest that the allosteric inhibition of DEU on TYK2 is accomplished by affecting the JH2-JH1 interface and hampering the state transition and ATP binding in JH1. Particularly, DEU binding stabilized the autoinhibitory interface between JH2 and JH1 while disrupting the formation of the activation interface. As a result, the negative regulation of JH2 on JH1 was greatly enhanced. These findings offer additional details on the pseudokinase-dependent autoinhibition of the JAK kinase domain and provide theoretical support for the JH2-targeted drug discovery in JAK members.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108224"},"PeriodicalIF":2.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.compbiolchem.2024.108225
Erika Vivanco , Eric Goles , Marco Montalva-Medel , María J. Poupin
Gonadal sex determination (GSD) is a complex but poorly understood process in the early stages of embryonic development. This process determines whether the bipotential gonadal primordium (BGP) will differentiate into testes or ovaries through the activation of genetic factors related to Sertoli or Granulosa cells, respectively. The study of this developmental process remains challenging due to experimental limitations and the complexity of the underlying genetic interactions. Boolean Networks (BNs) are binary networks that simulate genetic behavior and are commonly used for modeling gene regulatory networks (GRNs) due to their simplicity when dealing with a high number of gene interactions. Reported BNs usually use a synchronous (parallel) update scheme, which means that all the nodes (representing genes) update their values simultaneously. However, the use of this update scheme has been criticized because it cannot represent biological systems that are highly regulated at a temporal scale. Asynchronous and block-sequential updating schemes appear as an alternative to tackle this issue. In the first case, the updating scheme follows a random behavior while, in the second case, the set of network nodes is partitioned into blocks such that the nodes within a block are updated simultaneously, and the blocks are considered in a specific order sequence. To assess the impact of different updating approaches in a GRN associated to GSD we first made a node reduction without losing the main dynamics of the original network which are related to the formation of testes and ovaries. Then, we tested the effect of perturbations given by the inactivation of genes on the network attractors, specifically the SRY and WNT4 genes, since the former is only present in the Y chromosome and the latter is of importance in early embryo development. We found that both genes were crucial, but WNT4 alone showed a higher percentage of attractors towards a phenotype than the SRY alone. Finally, we found that using asynchronous and block-sequential updating schemes, the attraction basins – i.e., the set of configurations that reach an attractor – remain with similar percentages to those of the original network, which supports the robustness of the model.
{"title":"Dynamical robustness of a Boolean model for the human gonadal sex determination","authors":"Erika Vivanco , Eric Goles , Marco Montalva-Medel , María J. Poupin","doi":"10.1016/j.compbiolchem.2024.108225","DOIUrl":"10.1016/j.compbiolchem.2024.108225","url":null,"abstract":"<div><div>Gonadal sex determination (GSD) is a complex but poorly understood process in the early stages of embryonic development. This process determines whether the bipotential gonadal primordium (BGP) will differentiate into testes or ovaries through the activation of genetic factors related to Sertoli or Granulosa cells, respectively. The study of this developmental process remains challenging due to experimental limitations and the complexity of the underlying genetic interactions. Boolean Networks (BNs) are binary networks that simulate genetic behavior and are commonly used for modeling gene regulatory networks (GRNs) due to their simplicity when dealing with a high number of gene interactions. Reported BNs usually use a synchronous (parallel) update scheme, which means that all the nodes (representing genes) update their values simultaneously. However, the use of this update scheme has been criticized because it cannot represent biological systems that are highly regulated at a temporal scale. Asynchronous and block-sequential updating schemes appear as an alternative to tackle this issue. In the first case, the updating scheme follows a random behavior while, in the second case, the set of network nodes is partitioned into blocks such that the nodes within a block are updated simultaneously, and the blocks are considered in a specific order sequence. To assess the impact of different updating approaches in a GRN associated to GSD we first made a node reduction without losing the main dynamics of the original network which are related to the formation of testes and ovaries. Then, we tested the effect of perturbations given by the inactivation of genes on the network attractors, specifically the SRY and WNT4 genes, since the former is only present in the Y chromosome and the latter is of importance in early embryo development. We found that both genes were crucial, but WNT4 alone showed a higher percentage of attractors towards a phenotype than the SRY alone. Finally, we found that using asynchronous and block-sequential updating schemes, the attraction basins – i.e., the set of configurations that reach an attractor – remain with similar percentages to those of the original network, which supports the robustness of the model.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108225"},"PeriodicalIF":2.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.compbiolchem.2024.108222
Subhashree Subhasmita Nayak, Ramadas Krishna
The response regulator, MtrA, plays a major role in adaptation to the host environment, cell division, replication, and dormancy activation of Mycobacterium tuberculosis (Mtb). The phosphorylation of the response regulator MtrA alters the downstream activity, typically involving changes in DNA binding activity. However, there is a substantial knowledge gap in understanding the phosphorylation-mediated structural changes in MtrA. Additionally, the active conformation of the protein has yet to be determined. Therefore, in this study, we have investigated the phosphorylation-induced conformational changes of MtrA using all-atom molecular dynamics simulations under various phosphorylation conditions. The results from this study demonstrate that the phosphorylation at D56 (pD56-MtrA) increases the compactness of the MtrA protein by stabilizing the inter-domain interaction between the regulatory domain and DNA binding domain. Notably, the higher occupancy H-bond (over 95 %) between Arg200-Asn100 in case of the pD56-MtrA condition, which is otherwise absent in the non-phosphorylated (uMtrA) condition, suggests the importance of this interaction in the active conformation of the protein. The dynamic cross-correlation analysis reveals that phosphorylation (especially pD56-MtrA) reduces the anti-correlated motions and increases correlated motions between different domains. Moreover, the higher DNA binding affinity of pD56-MtrA compared to uMtrA supported by molecular docking and MD simulation followed by MMPBSA analysis suggests that pD56-MtrA is the possible active conformation of the MtrA protein. Overall, this investigation elucidates the key structural changes in MtrA under different phosphorylated conditions, which might help in designing novel therapeutics against tuberculosis.
反应调节因子 MtrA 在结核分枝杆菌（Mtb）适应宿主环境、细胞分裂、复制和休眠激活过程中发挥着重要作用。反应调节因子 MtrA 的磷酸化会改变其下游活性，通常涉及 DNA 结合活性的变化。然而，在了解磷酸化介导的 MtrA 结构变化方面还存在很大的知识差距。此外，该蛋白质的活性构象也尚未确定。因此，在本研究中，我们利用全原子分子动力学模拟研究了不同磷酸化条件下磷酸化诱导的 MtrA 构象变化。研究结果表明，D56 处的磷酸化（pD56-MtrA）通过稳定调控结构域和 DNA 结合结构域之间的相互作用，增加了 MtrA 蛋白的紧密性。值得注意的是，在 pD56-MtrA 条件下，Arg200-Asn100 之间的 H 键占有率较高（超过 95%），而在非磷酸化（uMtrA）条件下则不存在这种情况，这表明这种相互作用在蛋白质的活性构象中非常重要。动态交叉相关分析表明，磷酸化（尤其是 pD56-MtrA）减少了反相关运动，增加了不同结构域之间的相关运动。此外，通过分子对接和 MD 模拟以及 MMPBSA 分析，pD56-MtrA 与 uMtrA 相比具有更高的 DNA 结合亲和力，这表明 pD56-MtrA 可能是 MtrA 蛋白的活性构象。总之，这项研究阐明了不同磷酸化条件下 MtrA 的关键结构变化，这可能有助于设计新型结核病治疗药物。
{"title":"Phosphorylation at the D56 residue of MtrA in Mycobacterium tuberculosis enhances its DNA binding affinity by modulating inter-domain interaction","authors":"Subhashree Subhasmita Nayak, Ramadas Krishna","doi":"10.1016/j.compbiolchem.2024.108222","DOIUrl":"10.1016/j.compbiolchem.2024.108222","url":null,"abstract":"<div><div>The response regulator, MtrA, plays a major role in adaptation to the host environment, cell division, replication, and dormancy activation of <em>Mycobacterium tuberculosis</em> (Mtb). The phosphorylation of the response regulator MtrA alters the downstream activity, typically involving changes in DNA binding activity. However, there is a substantial knowledge gap in understanding the phosphorylation-mediated structural changes in MtrA. Additionally, the active conformation of the protein has yet to be determined. Therefore, in this study, we have investigated the phosphorylation-induced conformational changes of MtrA using all-atom molecular dynamics simulations under various phosphorylation conditions. The results from this study demonstrate that the phosphorylation at D56 (pD56-MtrA) increases the compactness of the MtrA protein by stabilizing the inter-domain interaction between the regulatory domain and DNA binding domain. Notably, the higher occupancy H-bond (over 95 %) between Arg200-Asn100 in case of the pD56-MtrA condition, which is otherwise absent in the non-phosphorylated (uMtrA) condition, suggests the importance of this interaction in the active conformation of the protein. The dynamic cross-correlation analysis reveals that phosphorylation (especially pD56-MtrA) reduces the anti-correlated motions and increases correlated motions between different domains. Moreover, the higher DNA binding affinity of pD56-MtrA compared to uMtrA supported by molecular docking and MD simulation followed by MMPBSA analysis suggests that pD56-MtrA is the possible active conformation of the MtrA protein. Overall, this investigation elucidates the key structural changes in MtrA under different phosphorylated conditions, which might help in designing novel therapeutics against tuberculosis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108222"},"PeriodicalIF":2.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.compbiolchem.2024.108226
Ahmet Arıhan Erözden , Nalan Tavsanli , Mahmut Çalışkan
The quest to discover the evolutionary relationships of organisms is an evolving, long-time topic of research. Such research gave rise to many different taxonomic databases and various definitions of systematic groups. One such group is the phylum Tardigrada. Tardigrades are an important field of study because of their biotechnological potential as well as their complex biological processes, which have the potential to answer questions about animal evolution. The evolutionary relationships within the phyla are subject to rigorous research, and new data is added to the literature constantly. For these studies, a widespread technique is the use of bioinformatic approaches in order to put forward concrete phylogenetic evidence. Bioinformatics is a field of computational biology that interprets large amounts of data in order to compute and demonstrate results. It is widely used not only for phylogeny but also for various different types of analyses and has been growing as a field since its foundation. This review discusses the different aspects, advantages, and methods of the use of bioinformatics in tardigrade phylogeny. It aims to put forward a defining picture of how the bioinformatic methods prove useful for providing phylogenetic results and elaborate on future perspectives.
{"title":"Advances in bioinformatic approaches to tardigrade phylogeny","authors":"Ahmet Arıhan Erözden , Nalan Tavsanli , Mahmut Çalışkan","doi":"10.1016/j.compbiolchem.2024.108226","DOIUrl":"10.1016/j.compbiolchem.2024.108226","url":null,"abstract":"<div><div>The quest to discover the evolutionary relationships of organisms is an evolving, long-time topic of research. Such research gave rise to many different taxonomic databases and various definitions of systematic groups. One such group is the phylum Tardigrada. Tardigrades are an important field of study because of their biotechnological potential as well as their complex biological processes, which have the potential to answer questions about animal evolution. The evolutionary relationships within the phyla are subject to rigorous research, and new data is added to the literature constantly. For these studies, a widespread technique is the use of bioinformatic approaches in order to put forward concrete phylogenetic evidence. Bioinformatics is a field of computational biology that interprets large amounts of data in order to compute and demonstrate results. It is widely used not only for phylogeny but also for various different types of analyses and has been growing as a field since its foundation. This review discusses the different aspects, advantages, and methods of the use of bioinformatics in tardigrade phylogeny. It aims to put forward a defining picture of how the bioinformatic methods prove useful for providing phylogenetic results and elaborate on future perspectives.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108226"},"PeriodicalIF":2.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.compbiolchem.2024.108227
Ping-Huan Kuo , Yu-Hsiang Li , Her-Terng Yau
This study employed machine learning techniques to predict the rate of feline infectious peritonitis (FIP) diagnoses, with a specific focus on mutations in the spike protein gene of the feline coronavirus (FCoV). FIP is a fatal viral disease affecting the peritoneum of cats and is primarily caused by mutations in FCoV. Its diagnosis largely relies on evaluations of various biomarkers and clinical symptoms. The current analysis of FCoV spike protein gene mutations exhibits certain limitations. To address this problem, the present study employed a large dataset—comprising information on FCoV copy numbers, spike protein mutation outcomes, and related clinical data—and used machine learning models to analyze the association between spike protein gene mutations and FIP diagnosis. Various algorithms were used to establish highly accurate predictive models, namely logistic regression, random forest, decision tree, neural network, support vector machine, gradient boosting tree, and categorical boosting (CatBoost) algorithms. The model obtained using the CatBoost algorithm was discovered to have accuracy of 0.9541. Accordingly, a highly accurate predictive model was developed to enable early diagnosis of FIP and improve the rate of survival in cats. The application of machine learning technology in this study yielded research findings that provide veterinarians with effective tools for managing and preventing FIP, a painful and deadly disease for cats. This study is a pioneering work in the systematic application of multiple machine learning models to the prediction of FIP and comparison of performance results to improve diagnostic accuracy and efficiency. This study is the first of its kind in the field of FIP.
{"title":"Development of feline infectious peritonitis diagnosis system by using CatBoost algorithm","authors":"Ping-Huan Kuo , Yu-Hsiang Li , Her-Terng Yau","doi":"10.1016/j.compbiolchem.2024.108227","DOIUrl":"10.1016/j.compbiolchem.2024.108227","url":null,"abstract":"<div><div>This study employed machine learning techniques to predict the rate of feline infectious peritonitis (FIP) diagnoses, with a specific focus on mutations in the spike protein gene of the feline coronavirus (FCoV). FIP is a fatal viral disease affecting the peritoneum of cats and is primarily caused by mutations in FCoV. Its diagnosis largely relies on evaluations of various biomarkers and clinical symptoms. The current analysis of FCoV spike protein gene mutations exhibits certain limitations. To address this problem, the present study employed a large dataset—comprising information on FCoV copy numbers, spike protein mutation outcomes, and related clinical data—and used machine learning models to analyze the association between spike protein gene mutations and FIP diagnosis. Various algorithms were used to establish highly accurate predictive models, namely logistic regression, random forest, decision tree, neural network, support vector machine, gradient boosting tree, and categorical boosting (CatBoost) algorithms. The model obtained using the CatBoost algorithm was discovered to have accuracy of 0.9541. Accordingly, a highly accurate predictive model was developed to enable early diagnosis of FIP and improve the rate of survival in cats. The application of machine learning technology in this study yielded research findings that provide veterinarians with effective tools for managing and preventing FIP, a painful and deadly disease for cats. This study is a pioneering work in the systematic application of multiple machine learning models to the prediction of FIP and comparison of performance results to improve diagnostic accuracy and efficiency. This study is the first of its kind in the field of FIP.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108227"},"PeriodicalIF":2.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142334427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.compbiolchem.2024.108221
Hazem Elkady , Walid E. Elgammal , Hazem A. Mahdy , Susi Zara , Simone Carradori , Dalal Z. Husein , Aisha A. Alsfouk , Ibrahim M. Ibrahim , Eslam B. Elkaeed , Ahmed M. Metwaly , Ibrahim H. Eissa
In this study, we present the design, synthesis, and evaluation of six new thiadiazole derivatives designed as VEGFR-2 inhibitors. The most promising compound, 18b, demonstrated promising inhibitory activity against VEGFR-2, with an IC50 value of 0.165 µg/mL. The in vitro assessments on MCF-7 and HepG2 cell lines revealed the superior anti-proliferative effects of compound 18b, exhibiting IC50 values of 0.06 and 0.17 µM, respectively. Further investigations into the cell cycle distribution of compound 18b on MCF-7 cells exhibited a cell cycle arrest at the S phase (52.96 %) and significantly reducing the percentage of cells in the G0-G1 and G2/M phases. Additionally, compound 18b demonstrated a remarkable pro-apoptotic effect, with 45.29 % total apoptosis, characterized by both early and late apoptosis, and minimal necrosis. These findings were corroborated by RT-PCR analysis, revealing a significant downregulation of the anti-apoptotic gene Bcl2 and upregulation of the pro-apoptotic gene BAX in compound 18b-treated cells compared to control MCF-7 cells. Moreover, in silico studies involving molecular docking, Density Functional Theory (DFT) calculations, Molecular Dynamics (MD) simulations, MM-GBSA, Principle Component Analysis of Trajectories (PCAT), in addition to Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions underscored the molecular interactions, energetics, and pharmacokinetic properties of compound 18b and the other derivatives further supporting its potential. Our integrated approach, combining in vitro experimens with in silico predictions provides valuable insights into the therapeutic potential of compound 18b as a robust VEGFR-2 inhibitor and lays the groundwork for future optimization.
{"title":"Anti-proliferative 2,3-dihydro-1,3,4-thiadiazoles targeting VEGFR-2: Design, synthesis, in vitro, and in silico studies","authors":"Hazem Elkady , Walid E. Elgammal , Hazem A. Mahdy , Susi Zara , Simone Carradori , Dalal Z. Husein , Aisha A. Alsfouk , Ibrahim M. Ibrahim , Eslam B. Elkaeed , Ahmed M. Metwaly , Ibrahim H. Eissa","doi":"10.1016/j.compbiolchem.2024.108221","DOIUrl":"10.1016/j.compbiolchem.2024.108221","url":null,"abstract":"<div><div>In this study, we present the design, synthesis, and evaluation of six new thiadiazole derivatives designed as VEGFR-2 inhibitors. The most promising compound, <strong>18b</strong>, demonstrated promising inhibitory activity against VEGFR-2, with an IC<sub>50</sub> value of 0.165 µg/mL. The <em>in vitro</em> assessments on MCF-7 and HepG2 cell lines revealed the superior anti-proliferative effects of compound <strong>18b</strong>, exhibiting IC<sub>50</sub> values of 0.06 and 0.17 µM, respectively. Further investigations into the cell cycle distribution of compound <strong>18b</strong> on MCF-7 cells exhibited a cell cycle arrest at the S phase (52.96 %) and significantly reducing the percentage of cells in the G0-G1 and G2/M phases. Additionally, compound <strong>18b</strong> demonstrated a remarkable pro-apoptotic effect, with 45.29 % total apoptosis, characterized by both early and late apoptosis, and minimal necrosis. These findings were corroborated by RT-PCR analysis, revealing a significant downregulation of the anti-apoptotic gene Bcl2 and upregulation of the pro-apoptotic gene BAX in compound <strong>18b</strong>-treated cells compared to control MCF-7 cells. Moreover, <em>in silico</em> studies involving molecular docking, Density Functional Theory (DFT) calculations, Molecular Dynamics (MD) simulations, MM-GBSA, Principle Component Analysis of Trajectories (PCAT), in addition to Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions underscored the molecular interactions, energetics, and pharmacokinetic properties of compound <strong>18b</strong> and the other derivatives further supporting its potential. Our integrated approach, combining <em>in vitro</em> experimens with <em>in silico</em> predictions provides valuable insights into the therapeutic potential of compound <strong>18b</strong> as a robust VEGFR-2 inhibitor and lays the groundwork for future optimization.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108221"},"PeriodicalIF":2.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1016/j.compbiolchem.2024.108223
Heng Dong , Baoshan Ma , Yangyang Meng , Yiming Wu , Yongjing Liu , Tao Zeng , Jinyan Huang
Background and objective
The reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference.
Method
This study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links.
Results
Comprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT.
Conclusion
We propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.
{"title":"GRNMOPT: Inference of gene regulatory networks based on a multi-objective optimization approach","authors":"Heng Dong , Baoshan Ma , Yangyang Meng , Yiming Wu , Yongjing Liu , Tao Zeng , Jinyan Huang","doi":"10.1016/j.compbiolchem.2024.108223","DOIUrl":"10.1016/j.compbiolchem.2024.108223","url":null,"abstract":"<div><h3>Background and objective</h3><div>The reconstruction of gene regulatory networks (GRNs) stands as a vital approach in deciphering complex biological processes. The application of nonlinear ordinary differential equations (ODEs) models has demonstrated considerable efficacy in predicting GRNs. Notably, the decay rate and time delay are pivotal in authentic gene regulation, yet their systematic determination in ODEs models remains underexplored. The development of a comprehensive optimization framework for the effective estimation of these key parameters is essential for accurate GRN inference.</div></div><div><h3>Method</h3><div>This study introduces GRNMOPT, an innovative methodology for inferring GRNs from time-series and steady-state data. GRNMOPT employs a combined use of decay rate and time delay in constructing ODEs models to authentically represent gene regulatory processes. It incorporates a multi-objective optimization approach, optimizing decay rate and time delay concurrently to derive Pareto optimal sets for these factors, thereby maximizing accuracy metrics such as AUROC (Area Under the Receiver Operating Characteristic curve) and AUPR (Area Under the Precision-Recall curve). Additionally, the use of XGBoost for calculating feature importance aids in identifying potential regulatory gene links.</div></div><div><h3>Results</h3><div>Comprehensive experimental evaluations on two simulated datasets from DREAM4 and three real gene expression datasets (Yeast, In vivo Reverse-engineering and Modeling Assessment [IRMA], and Escherichia coli [E. coli]) reveal that GRNMOPT performs commendably across varying network scales. Furthermore, cross-validation experiments substantiate the robustness of GRNMOPT.</div></div><div><h3>Conclusion</h3><div>We propose a novel approach called GRNMOPT to infer GRNs based on a multi-objective optimization framework, which effectively improves inference accuracy and provides a powerful tool for GRNs inference.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108223"},"PeriodicalIF":2.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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