Background: Up to now, there have been three published versions of a yeast genome-scale metabolic model:iFF708,iND750andiLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of theSaccharomyces cerevisiaegenome-scale model,iIN800that includes a more rigorous and detailed description of lipid metabolism.Results: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scalein silicosingle gene deletions byiIN800were consistent with experimental data. In addition,¹³C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability ofiIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets.Conclusion: Performing integrated analyses usingiIN800as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states.

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs.

Long non-coding RNAs (lncRNAs) play important roles in the regulation of complex cellular processes, including transcriptional and post-transcriptional regulation of gene expression relevant for development and stress response, among others. Compared to other important crops, there is limited knowledge of cassava lncRNAs and their roles in abiotic stress adaptation. In this study, we performed a genome-wide study of ncRNAs in cassava, integrating genomics- and transcriptomics-based approaches. In total, 56,840 putative ncRNAs were identified, and approximately half the number were verified using expression data or previously known ncRNAs. Among these were 2229 potential novel lncRNA transcripts with unmatched sequences, 250 of which were differentially expressed in cold or drought conditions, relative to controls. We showed that lncRNAs might be involved in post-transcriptional regulation of stress-induced transcription factors (TFs) such as zinc-finger, WRKY, and nuclear factor Y gene families. These findings deepened our knowledge of cassava lncRNAs and shed light on their stress-responsive roles.

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

Hypertension or elevated blood pressure is a serious medical condition that significantly increases the risks of cardiovascular disease, heart disease, diabetes, stroke, kidney disease, and other health problems, that affect people worldwide. Thus, hypertension is one of the major global causes of premature death. Regarding the prevention and treatment of hypertension with no or few side effects, antihypertensive peptides (AHTPs) obtained from natural sources might be useful as nutraceuticals. Therefore, the search for alternative/novel AHTPs in food or natural sources has received much attention, as AHTPs may be functional agents for human health. AHTPs have been observed in diverse organisms, although many of them remain underinvestigated. The identification of peptides with antihypertensive activity in the laboratory is time- and resource-consuming. Alternatively, computational methods based on robust machine learning can identify or screen potential AHTP candidates prior to experimental verification. In this paper, we propose Ensemble-AHTPpred, an ensemble machine learning algorithm composed of a random forest (RF), a support vector machine (SVM), and extreme gradient boosting (XGB), with the aim of integrating diverse heterogeneous algorithms to enhance the robustness of the final predictive model. The selected feature set includes various computed features, such as various physicochemical properties, amino acid compositions (AACs), transitions, n-grams, and secondary structure-related information; these features are able to learn more information in terms of analyzing or explaining the characteristics of the predicted peptide. In addition, the tool is integrated with a newly proposed composite feature (generated based on a logistic regression function) that combines various feature aspects to enable improved AHTP characterization. Our tool, Ensemble-AHTPpred, achieved an overall accuracy above 90% on independent test data. Additionally, the approach was applied to novel experimentally validated AHTPs, obtained from recent studies, which did not overlap with the training and test datasets, and the tool could precisely predict these AHTPs.

Alpha (α)-enolase (e), a glycolytic enzyme, has an alternative role as a surface receptor of several bacteria mediating plasminogen (pg) binding. It is also recognized as a virulence factor of some pathogenic bacteria facilitating plasminogen activation and host cell invasion. A mycoplasmal α-enolase is also plasminogen binding protein. Molecular interaction of Mycoplasma. pneumoniae enolase with plasminogen would be useful for exploring the pathogen-host interaction. In an attempt to identify plasminogen binding sites of M. pneumoniae enolase, homology modeling and docking studies were conducted to obtain modeled structures of the M. pneumoniae enolase-plasminogen complex. The refined model was validated further by standard methods. Molecular docking revealed hydrogen bonding of eLys70-pgTyr50, eAsn165-pgThr66, eAla168-pgGlu21, eAsp17-pgLys70, and eAsn213-pgPro68/pgAsn69. Substantial decreases in accessible surface area (ASA) were observed and in concurrence with hydrogen bond pattern. These findings provide a detailed prediction of key residues that interact at the protein-protein interface. Our theoretical prediction is consistent with known biochemical data. The predicted interaction complex can be of great assistance in understanding structural insights, which is necessary to pathogen and host-component interaction. The ability of M. pneumoniae enolase to bind plasminogen may be indicative of an important role in invasion of this pathogen to host.