Journal of Bioinformatics and Computational Biology最新文献

{"title":"COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins.","authors":"Wajid Arshad Abbasi,&nbsp;Asma Anjam,&nbsp;Sadia Khalil,&nbsp;Saiqa Andleeb,&nbsp;Maryum Bibi,&nbsp;Syed Ali Abbas","doi":"10.1142/S0219720022500196","DOIUrl":"https://doi.org/10.1142/S0219720022500196","url":null,"abstract":"<p><p>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.</p>","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}

{"title":"GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion.","authors":"Jing Li,&nbsp;Chen Zhang,&nbsp;Zhengwei Li,&nbsp;Ru Nie,&nbsp;Pengyong Han,&nbsp;Wenjia Yang,&nbsp;Hongmei Liao","doi":"10.1142/S0219720022500238","DOIUrl":"https://doi.org/10.1142/S0219720022500238","url":null,"abstract":"<p><p>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.</p>","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}

{"title":"Behavioral dynamics of bacteriophage gene regulatory networks.","authors":"Gatis Melkus,&nbsp;Karlis Cerans,&nbsp;Karlis Freivalds,&nbsp;Lelde Lace,&nbsp;Darta Zajakina,&nbsp;Juris Viksna","doi":"10.1142/S0219720022500214","DOIUrl":"https://doi.org/10.1142/S0219720022500214","url":null,"abstract":"<p><p>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 <i>the only</i> 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.</p>","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}

{"title":"The impact of simulation time in predicting binding free energies using end-point approaches.","authors":"Babak Sokouti,&nbsp;Siavoush Dastmalchi,&nbsp;Maryam Hamzeh-Mivehroud","doi":"10.1142/S021972002250024X","DOIUrl":"https://doi.org/10.1142/S021972002250024X","url":null,"abstract":"<p><p>The profound impact of <i>in silico</i> 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.</p>","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}

{"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,&nbsp;Xu Zhao,&nbsp;Jianrong Fan","doi":"10.1142/S0219720022500202","DOIUrl":"https://doi.org/10.1142/S0219720022500202","url":null,"abstract":"<p><p>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.</p>","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}

{"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}

{"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":"<p><p>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.</p>","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}

{"title":"Invariant transformers of Robinson and Foulds distance matrices for Convolutional Neural Network.","authors":"Nadia Tahiri,&nbsp;Andrey Veriga,&nbsp;Aleksandr Koshkarov,&nbsp;Boris Morozov","doi":"10.1142/S0219720022500123","DOIUrl":"https://doi.org/10.1142/S0219720022500123","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The evolutionary histories of genes are susceptible of differing greatly from each other which could be explained by evolutionary variations in horizontal gene transfers or biological recombinations. A phylogenetic tree would therefore represent the evolutionary history of each gene, which may present different patterns from the species tree that defines the main evolutionary patterns. In addition, phylogenetic trees of closely related species should be merged, thus minimizing the topological conflicts they present and obtaining consensus trees (in the case of homogeneous data) or supertrees (in the case of heterogeneous data). The traditional approaches are consensus tree inference (if the set of trees contains the same set of species) or supertrees (if the set of trees contains different, but overlapping sets of species). Consensus trees and supertrees are constructed to produce unique trees. However, these methods lose precision with respect to different evolutionary variability. Other approaches have been implemented to preserve this variability using the [Formula: see text]-means algorithm or the [Formula: see text]-medoids algorithm. Using a new method, we determine all possible consensus trees and supertrees that best represent the most significant evolutionary models in a set of phylogenetic trees, thereby increasing the precision of the results and decreasing the time required. &lt;b&gt;Results:&lt;/b&gt; This paper presents in detail a new method for predicting the number of clusters in a Robinson and Foulds (RF) distance matrix using a convolutional neural network (CNN). We developed a new CNN approach (called CNNTrees) for multiple tree classification. This new strategy returns a number of clusters of the input phylogenetic trees for different-size sets of trees, which makes the new approach more stable and more robust. The paper provides an in-depth analysis of the relevant, but very difficult, problem of constructing alternative supertrees using phylogenies with different but overlapping sets of taxa. This new model will play an important role in the inference of Trees of Life (ToL). &lt;b&gt;Availability and implementation:&lt;/b&gt; CNNTrees is available through a web server at https://tahirinadia.github.io/. The source code, data and information about installation procedures are also available at https://github.com/TahiriNadia/CNNTrees. &lt;b&gt;Supplementary information:&lt;/b&gt; Supplementary data are available on GitHub platform. The evolutionary history of species is not unique, but is specific to sets of genes. Indeed, each gene has its own evolutionary history that differs considerably from one gene to another. For example, some individual genes or operons may be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene must be represented by its own phylogenetic tree, which may exhibit different evolutionary patterns than the species tree that accounts for the major vertical descent patterns. T","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 4","pages":"2250012"},"PeriodicalIF":1.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10775458","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}

{"title":"TemporalGSSA: A numerically robust R-wrapper to facilitate computation of a metabolite-specific and simulation time-dependent trajectory from stochastic simulation algorithm (SSA)-generated datasets.","authors":"Siddhartha Kundu","doi":"10.1142/S0219720022500184","DOIUrl":"https://doi.org/10.1142/S0219720022500184","url":null,"abstract":"<p><p>Whilst data on biochemical networks has increased several-fold, our comprehension of the underlying molecular biology is incomplete and inadequate. Simulation studies permit data collation from disparate time points and the imputed trajectories can provide valuable insights into the molecular biology of complex biochemical systems. Although, stochastic simulations are accurate, each run is an independent event and the data that is generated cannot be directly compared even with identical simulation times. This lack of robustness will preclude a biologically meaningful result for the metabolite(s) of concern and is a significant limitation of this approach. \"TemporalGSSA\" or temporal Gillespie Stochastic Simulation Algorithm is an R-wrapper which will collate and partition SSA-generated datasets with identical simulation times (trials) into finite sets of linear models (technical replicates). Each such model (time step of a single run, absolute number of molecules for a metabolite) computes several coefficients (slope, intercept, etc.). These coefficients are averaged (mean slope, mean intercept) across all trials of a technical replicate and along with an imputed time step (mean, median, random) is incorporated into a linear regression equation. The solution to this equation is the number of molecules of a metabolite which is used to compute the molar concentration of the metabolite per technical replicate. The summarized (mean, standard deviation) data of this vector of technical replicates is the outcome or numerical estimate of the molar concentration of a metabolite and is dependent on the duration of the simulation. If the SSA-generated dataset comprises runs with differing simulation times, \"TemporalGSSA\" can compute the time-dependent trajectory of a metabolite provided the trials-per technical replicate constraint is complied with. The algorithms deployed by \"TemporalGSSA\" are rigorous, have a sound theoretical basis and have contributed meaningfully to our comprehension of the mechanism(s) that drive complex biochemical systems. \"TemporalGSSA\", is robust, freely accessible and easy to use with several readily testable examples.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":" ","pages":"2250018"},"PeriodicalIF":1.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40691252","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}

{"title":"Flux balance network expansion predicts stage-specific human peri_implantation embryo metabolism.","authors":"Andisheh Dadashi,&nbsp;Derek Martinez","doi":"10.1142/S021972002250010X","DOIUrl":"https://doi.org/10.1142/S021972002250010X","url":null,"abstract":"<p><p>Metabolism is an essential cellular process for the growth and maintenance of organisms. A better understanding of metabolism during embryogenesis may shed light on the developmental origins of human disease. Metabolic networks, however, are vastly complex with many redundant pathways and interconnected circuits. Thus, computational approaches serve as a practical solution for unraveling the genetic basis of embryo metabolism to help guide future experimental investigations. RNA-sequencing and other profiling technologies make it possible to elucidate metabolic genotype-phenotype relationships and yet our understanding of metabolism is limited. Very few studies have examined the temporal or spatial metabolomics of the human embryo, and prohibitively small sample sizes traditionally observed in human embryo research have presented logistical challenges for metabolic studies, hindering progress towards the reconstruction of the human embryonic metabolome. We employed a network expansion algorithm to evolve the metabolic network of the peri-implantation embryo metabolism and we utilized flux balance analysis (FBA) to examine the viability of the evolved networks. We found that modulating oxygen uptake promotes lactate diffusion across the outer mitochondrial layer, providing <i>in-silico</i> support for a proposed lactate-malate-aspartate shuttle. We developed a stage-specific model to serve as a proof-of-concept for the reconstruction of future metabolic models of development. Our work shows that it is feasible to model human metabolism with respect to time-dependent changes characteristic of peri-implantation development.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 4","pages":"2250010"},"PeriodicalIF":1.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10409009","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}