Journal of Bioinformatics and Computational Biology最新文献_第7页

Author Index Volume 20 (2022). 作者索引第20卷(2022)。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-12-01 DOI: 10.1142/s0219749922990015

引用次数: 0

Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides. 反馈- avpgan:用于生成抗病毒肽的反馈引导生成对抗网络。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-12-01 DOI: 10.1142/S0219720022500263

Kano Hasegawa, Yoshitaka Moriwaki, Tohru Terada, Cao Wei, Kentaro Shimizu

{"title":"Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.","authors":"Kano Hasegawa, Yoshitaka Moriwaki, Tohru Terada, Cao Wei, Kentaro Shimizu","doi":"10.1142/S0219720022500263","DOIUrl":"https://doi.org/10.1142/S0219720022500263","url":null,"abstract":"In this study, we propose Feedback-AVPGAN, a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling approach that uses deep learning methods, comprises a generator and a discriminator. The generator is used to generate peptides; the generated proteins are fed to the discriminator to distinguish between the AVPs and non-AVPs. The original GAN design uses actual data to train the discriminator. However, not many AVPs have been experimentally obtained. To solve this problem, we used the Feedback method to allow the discriminator to learn from the existing as well as generated synthetic data. We implemented this method using a classifier module that classifies each peptide sequence generated by the GAN generator as AVP or non-AVP. The classifier uses the transformer network and achieves high classification accuracy. This mechanism enables the efficient generation of peptides with a high probability of exhibiting antiviral activity. Using the Feedback method, we evaluated various algorithms and their performance. Moreover, we modeled the structure of the generated peptides using AlphaFold2 and determined the peptides having similar physicochemical properties and structures to those of known AVPs, although with different sequences.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 6","pages":"2250026"},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9118189","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}

引用次数: 1

Accounting for treatment during the development or validation of prediction models. 在预测模型的开发或验证过程中考虑处理。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-12-01 DOI: 10.1142/S0219720022710019

Wei Xin Chan, Limsoon Wong

{"title":"Accounting for treatment during the development or validation of prediction models.","authors":"Wei Xin Chan, Limsoon Wong","doi":"10.1142/S0219720022710019","DOIUrl":"https://doi.org/10.1142/S0219720022710019","url":null,"abstract":"Clinical prediction models are widely used to predict adverse outcomes in patients, and are often employed to guide clinical decision-making. Clinical data typically consist of patients who received different treatments. Many prediction modeling studies fail to account for differences in patient treatment appropriately, which results in the development of prediction models that show poor accuracy and generalizability. In this paper, we list the most common methods used to handle patient treatments and discuss certain caveats associated with each method. We believe that proper handling of differences in patient treatment is crucial for the development of accurate and generalizable models. As different treatment strategies are employed for different diseases, the best approach to properly handle differences in patient treatment is specific to each individual situation. We use the Ma-Spore acute lymphoblastic leukemia data set as a case study to demonstrate the complexities associated with differences in patient treatment, and offer suggestions on incorporating treatment information during evaluation of prediction models. In clinical data, patients are typically treated on a case by case basis, with unique cases occurring more frequently than expected. Hence, there are many subtleties to consider during the analysis and evaluation of clinical prediction models.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 6","pages":"2271001"},"PeriodicalIF":1.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10523629","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}

引用次数: 1

COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins. COYOTE:基于序列衍生结构描述符的糖蛋白计算鉴定。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-10-01 Epub Date: 2022-09-12 DOI: 10.1142/S0219720022500196

Wajid Arshad Abbasi, Asma Anjam, Sadia Khalil, Saiqa Andleeb, Maryum Bibi, Syed Ali Abbas

{"title":"COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins.","authors":"Wajid Arshad Abbasi, Asma Anjam, Sadia Khalil, Saiqa Andleeb, Maryum Bibi, Syed Ali Abbas","doi":"10.1142/S0219720022500196","DOIUrl":"https://doi.org/10.1142/S0219720022500196","url":null,"abstract":"Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics. Experimental procedures involving wet-lab experiments to reveal glycoproteins are very time-consuming, laborious, and highly costly. However, costly and tedious experimental procedures can be assisted by ranking the most probable glycoproteins through computational methods with improved accuracy. In this study, we have proposed a novel machine learning-based predictive model for glycoproteins identification. Our proposed model is based on sequence-derived structural descriptors (SDSD) that fill the gap of unavailability of protein 3D structures and lack of accuracy in sequence information alone. Through a series of simulation studies, we have shown that our proposed model gives state-of-the-art generalization performance verified through various machine learning-centric and biologically relevant techniques and metrics. Through data mining in this study, we have also identified the role of descriptors in determining glycoproteins. Python-based standalone code together with a webserver implementation of our proposed model (COYOTE: identifiCation Of glYcoprOteins Through sEquences) is available at the URL: https://sites.google.com/view/wajidarshad/software.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2250019"},"PeriodicalIF":1.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33464194","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}

引用次数: 0

GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion. GCMCDTI:基于矩阵补全预测药物-靶标相互作用的图卷积自编码器框架。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-10-01 Epub Date: 2022-11-09 DOI: 10.1142/S0219720022500238

Jing Li, Chen Zhang, Zhengwei Li, Ru Nie, Pengyong Han, Wenjia Yang, Hongmei Liao

{"title":"GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion.","authors":"Jing Li, Chen Zhang, Zhengwei Li, Ru Nie, Pengyong Han, Wenjia Yang, Hongmei Liao","doi":"10.1142/S0219720022500238","DOIUrl":"https://doi.org/10.1142/S0219720022500238","url":null,"abstract":"Identification of potential drug-target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug-target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2250023"},"PeriodicalIF":1.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40673948","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}

引用次数: 1

Behavioral dynamics of bacteriophage gene regulatory networks. 噬菌体基因调控网络的行为动力学。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-10-01 DOI: 10.1142/S0219720022500214

Gatis Melkus, Karlis Cerans, Karlis Freivalds, Lelde Lace, Darta Zajakina, Juris Viksna

{"title":"Behavioral dynamics of bacteriophage gene regulatory networks.","authors":"Gatis Melkus, Karlis Cerans, Karlis Freivalds, Lelde Lace, Darta Zajakina, Juris Viksna","doi":"10.1142/S0219720022500214","DOIUrl":"https://doi.org/10.1142/S0219720022500214","url":null,"abstract":"We present hybrid system-based gene regulatory network models for lambda, HK022, and Mu bacteriophages together with dynamics analysis of the modeled networks. The proposed lambda phage model LPH2 is based on an earlier work and incorporates more recent biological assumptions about the underlying gene regulatory mechanism, HK022, and Mu phage models are new. All three models provide accurate representations of experimentally observed lytic and lysogenic behavioral cycles. Importantly, the models also imply that lysis and lysogeny are the only stable behaviors that can occur in the modeled networks. In addition, the models allow to derive switching conditions that irrevocably lead to either lytic or lysogenic behavioral cycle as well as constraints that are required for their biological feasibility. For LPH2 model the feasibility constraints place two mutually independent requirements on comparative order of cro and cI protein binding site affinities. However, HK022 model, while broadly similar, does not require any of these constraints. Biologically very different lysis-lysogeny switching mechanism of Mu phage is also accurately reproduced by its model. In general the results show that hybrid system model (HSM) hybrid system framework can be successfully applied to modeling small ([Formula: see text] gene) regulatory networks and used for comprehensive analysis of model dynamics and stable behavior regions.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 5","pages":"2250021"},"PeriodicalIF":1.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10759590","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}

引用次数: 0

The impact of simulation time in predicting binding free energies using end-point approaches. 模拟时间对终点法预测束缚自由能的影响。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-10-01 DOI: 10.1142/S021972002250024X

Babak Sokouti, Siavoush Dastmalchi, Maryam Hamzeh-Mivehroud

{"title":"The impact of simulation time in predicting binding free energies using end-point approaches.","authors":"Babak Sokouti, Siavoush Dastmalchi, Maryam Hamzeh-Mivehroud","doi":"10.1142/S021972002250024X","DOIUrl":"https://doi.org/10.1142/S021972002250024X","url":null,"abstract":"The profound impact of in silico studies for a fast-paced drug discovery pipeline is undeniable for pharmaceutical community. The rational design of novel drug candidates necessitates considering optimization of their different aspects prior to synthesis and biological evaluations. The affinity prediction of small ligands to target of interest for rank-ordering the potential ligands is one of the most routinely used steps in the context of virtual screening. So, the end-point methods were employed for binding free energy estimation focusing on evaluating simulation time effect. Then, a set of human aldose reductase inhibitors were selected for molecular dynamics (MD)-based binding free energy calculations. A total of 100[Formula: see text]ns MD simulation time was conducted for the ligand-receptor complexes followed by prediction of binding free energies using MM/PB(GB)SA and LIE approaches under different simulation time. The results revealed that a maximum of 30[Formula: see text]ns simulation time is sufficient for determination of binding affinities inferred from steady trend of squared correlation values (R2) between experimental and predicted [Formula: see text]G as a function of MD simulation time. In conclusion, the MM/PB(GB)SA algorithms performed well in terms of binding affinity prediction compared to LIE approach. The results provide new insights for large-scale applications of such predictions in an affordable computational cost.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 5","pages":"2250024"},"PeriodicalIF":1.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10472803","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}

引用次数: 1

Computational design and experimental confirmation of conformationally constrained peptides to compete with coactivators for pediatric PPAR[Formula: see text] by minimizing indirect readout effect. 通过最小化间接读数效应，计算设计和实验确认构象约束肽与助激活剂竞争用于儿科PPAR[公式:见文本]。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-10-01 Epub Date: 2022-09-12 DOI: 10.1142/S0219720022500202

Caijie Gao, Xu Zhao, Jianrong Fan

{"title":"Computational design and experimental confirmation of conformationally constrained peptides to compete with coactivators for pediatric PPAR[Formula: see text] by minimizing indirect readout effect.","authors":"Caijie Gao, Xu Zhao, Jianrong Fan","doi":"10.1142/S0219720022500202","DOIUrl":"https://doi.org/10.1142/S0219720022500202","url":null,"abstract":"The peroxisome proliferator-activated receptor-[Formula: see text] (PPAR[Formula: see text]) is a member of PPAR nuclear receptor family, and its antagonists have been widely used to treat pediatric metabolic disorders. Traditional type-1 and type-2 PPAR[Formula: see text] antagonists are all small-molecule compounds that have been developed to target the ligand-binding site (LBS) of PPAR[Formula: see text], which is not overlapped with the coactivator-interacting site (CIS) of PPAR[Formula: see text]. In this study, we described the rational design of type-3 peptidic antagonists that can directly disrupt PPAR[Formula: see text]-coactivator interaction by physically competing with coactivator proteins for the CIS site. In the procedure, seven reported PPAR[Formula: see text] coactivator proteins were collected and eight 11-mer helical peptide segments that contain the core PPAR[Formula: see text]-binding LXXLL motif were identified in these coactivators, which, however, possessed a large flexibility and intrinsic disorder when splitting from coactivator protein context, and thus would incur a considerable entropy penalty (i.e. indirect readout) upon binding to PPAR[Formula: see text] CIS site. By carefully examining the natively folded conformation of these helical peptides in their parent protein context and in their interaction mode with the CIS site, we rationally designed a hydrocarbon bridge across the solvent-exposed, ([Formula: see text], [Formula: see text]+ 4) residues to constrain their helical conformation, thus largely minimizing the unfavorable indirect readout effect but having only a moderate influence on favorable enthalpy contribution (i.e. direct readout) upon PPAR[Formula: see text]-peptide binding. The computational findings were further substantiated by fluorescence competition assays.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2250020"},"PeriodicalIF":1.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33464193","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}

引用次数: 1

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology 肿瘤学中潜在个体分子谱和生存率的非参数贝叶斯联合模型

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-09-08 DOI: 10.1142/s0219720022500226

Sarah-Laure Rincourt, S. Michiels, D. Drubay

{"title":"A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology","authors":"Sarah-Laure Rincourt, S. Michiels, D. Drubay","doi":"10.1142/s0219720022500226","DOIUrl":"https://doi.org/10.1142/s0219720022500226","url":null,"abstract":"The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"1 1","pages":"2250022"},"PeriodicalIF":1.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44087582","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}

引用次数: 0

iRNA5hmC-HOC: High-order correlation information for identifying RNA 5-hydroxymethylcytosine modification. iRNA5hmC-HOC:用于鉴定RNA5 -羟甲基胞嘧啶修饰的高阶相关信息。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-08-01 Epub Date: 2022-08-03 DOI: 10.1142/S0219720022500172

Hongliang Zou

{"title":"iRNA5hmC-HOC: High-order correlation information for identifying RNA 5-hydroxymethylcytosine modification.","authors":"Hongliang Zou","doi":"10.1142/S0219720022500172","DOIUrl":"https://doi.org/10.1142/S0219720022500172","url":null,"abstract":"RNA 5-hydroxymethylcytosine (5 hmC) is an important RNA modification, which plays vital role in several biological processes. Currently, it is a hot topic to identify 5 hmC sites due to its benefit in understanding its biological functions. Therefore, in this study, we developed a predictor called iRNA5 hmC-HOC, which is based on a high-order correlation information method to identify 5 hmC sites. To build the model, 22 different classes of dinucleotide physicochemical (PC) properties were employed to represent RNA sequences, and the least absolute shrinkage and selection operator (LASSO) algorithm was adopted to select the most discriminative features. In the jackknife test, the proposed method achieved 89.80% classification accuracy based on support vector machine (SVM). As compared with the state-of-the-art predictors, our proposed method has significant improvement on the classification performance. It indicates that the proposed method might be a promising tool in identifying RNA 5 hmC modification sites. The dataset and source codes are available at https://figshare.com/articles/online_resource/iRNA5hmC-HOC/15177450.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2250017"},"PeriodicalIF":1.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40576562","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}

引用次数: 0