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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 3. |
- Wu, Chuanyan, et al.
(författare)
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PEPRF : Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-based Features via Random Forest
- 2021
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Ingår i: Current Bioinformatics. - : Bentham Science Publishers Ltd.. - 1574-8936. ; 16:9, s. 1161-1168
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Tidskriftsartikel (refereegranskat)abstract
- Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, in-formation-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, Re-liefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential pro-teins.
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| 4. |
- Xiang, Yusen, et al.
(författare)
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Ginkgolic acids inhibit SARS-CoV-2 and its variants by blocking the spike protein/ACE2 interplay
- 2023
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Ingår i: International Journal of Biological Macromolecules. - : Elsevier. - 0141-8130 .- 1879-0003. ; 226, s. 780-792
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Tidskriftsartikel (refereegranskat)abstract
- Targeting the interaction between the spike protein receptor binding domain (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and angiotensin-converting enzyme 2 (ACE2) is a potential therapeutic strategy for treating coronavirus disease 2019 (COVID-19). However, we still lack small-molecule drug candidates for this target due to the missing knowledge in the hot spots for the protein-protein interaction. Here, we used NanoBiT technology to identify three Ginkgolic acids from an in-house traditional Chinese medicine (TCM) library, and they interfere with the S-RBD/ACE2 interplay. Our pseudovirus assay showed that one of the compounds, Ginkgolic acid C17:1 (GA171), significantly inhibits the entry of original SARS-CoV-2 and its variants into the ACE2-overexpressed HEK293T cells. We investigated and proposed the binding sites of GA171 on S-RBD by combining molecular docking and molecular dynamics simulations. Site-directed mutagenesis and surface plasmon resonance revealed that GA171 specifically binds to the pocket near R403 and Y505, critical residues of S-RBD for S-RBD interacting with ACE2. Thus, we provide structural insights into developing new small-molecule inhibitors and vaccines against the proposed S-RBD binding site.
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| 5. |
- Zhang, Jialin, et al.
(författare)
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FS-GBDT : identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:3
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.
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| 6. |
- Wu, Chuanyan, et al.
(författare)
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A novel model for protein sequence similarity analysis based on spectral radius
- 2018
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Ingår i: Journal of Theoretical Biology. - : Elsevier BV. - 0022-5193. ; 446, s. 61-70
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Tidskriftsartikel (refereegranskat)abstract
- Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.
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| 7. |
- Wu, Chuanyan, et al.
(författare)
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MHMDA : Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information
- 2019
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Ingår i: IEEE Access. - 2169-3536. ; 7, s. 106687-106693
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Tidskriftsartikel (refereegranskat)abstract
- Microbes are vital in human health. It is helpful to promote diagnostic and treatment of human disease and drug development by identifying microbe-disease associations. However, knowledge in this area still needs to be further improved. In this paper, a new computational model using matrix completion to predict human microbe-disease associations (mHMDA, Fig. 1) is developed. First, we extract the disease feature by Gaussian kernel-based similarity and symptom-based similarity. Meanwhile, the microbe feature is computed by Gaussian kernel-based similarity. As treating potential association as the missing elements of a matrix, the matrix completion is adopted to get the potential microbe-disease associations. Leave-one-out cross-validation (LOOCV) is carried out which get the AUC (The area under ROC curve) of 0.928 showing the effectiveness of mHMDA. Furthermore, 5-fold CV get the AUCs of 0.8838 ± 0.0044 (mean ± standard deviation). Moreover, through the four case studies (asthma, inflammatory bowel disease (IBD), type 2 diabetes (T2D), and type 1 diabetes (T1D)), we find that nine, ten, nine, and eight of top-ten inferred microorganisms for the four diseases are previously verified by experiments. All these results indicate the effectiveness of mHMDA. mHMDA might be helpful to infer the disease-related microorganisms.
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| 8. |
- Wu, Chuanyan, et al.
(författare)
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PTPD : Predicting therapeutic peptides by deep learning and word2vec
- 2019
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD).∗: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively.∗: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.
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