StackCBPred: A stacking based prediction of protein-carbohydrate binding sites from sequence

Gattani, Suraj; Mishra, Avdesh; Hoque, Tamjidul

doi:10.1016/j.carres.2019.107857

Cited by 30 publications

(22 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While various ligand binding site prediction software programs exist, including some specific to carbohydrate ligands, , we chose FTMap for its speed and ease of use via its online web server . FTMap predicts ligand binding “hot spots” by extensively sampling the surface of a macromolecular receptor using various small organic molecules as probes.…”

Section: Resultsmentioning

confidence: 99%

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta

et al. 2021

View full text Add to dashboard Cite

Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein–glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein–glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein–glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein–glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein–glycoligand docking pipeline for generating putative protein–glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.

show abstract

Section: Resultsmentioning

confidence: 99%

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recently, there has been an increasing interest in ensemble machine learning which shows predictive capability in many applications [41,42,43,44,45]. In ensemble learning, multiple base predictors are constructed where their results are integrated with a specific strategy to fetch the final results.…”

Section: E Stacking Ensemble Learningmentioning

confidence: 99%

“…In ensemble learning, multiple base predictors are constructed where their results are integrated with a specific strategy to fetch the final results. than a single predictor model [41,42]. Stacking ensemble learning gets the results from multiple models and combines them into a new model.…”

Section: E Stacking Ensemble Learningmentioning

confidence: 99%

“…In the first level or level-0 called base model, multiple base models are implemented individually. In the second level or level-1 called meta-model, one meta-model is implemented on the probability results of the first level predictors to decrease the generalization error and provide the final prediction result [41,42]. Figure 2 shows the framework of the stacking ensemble learning.…”

Section: E Stacking Ensemble Learningmentioning

confidence: 99%

See 1 more Smart Citation

PUStackNGly: Positive-Unlabeled and Stacking Learning for N-Linked Glycosylation Site Prediction

et al. 2022

View full text Add to dashboard Cite

N-linked glycosylation is one of the most common protein post-translation modifications (PTMs) in humans where the Asparagine (N) amino acid of the protein is attached to the glycan. It is involved in most biological processes and associated with various human diseases as diabetes, cancer, coronavirus, influenza, and Alzheimer's. Accordingly, identifying N-linked glycosylation sites will be beneficial to understanding the system and mechanism of glycosylation. Due to the experimental challenges of glycosylation site identification, machine learning becomes very important to predict the glycosylation sites. This paper proposes a novel N-linked glycosylation predictor based on bagging positive-unlabeled (PU) learning and stacking ensemble machine learning (PUStackNGly). In the proposed PUStackNGly, comprehensive sequence and structural-based features are extracted using different feature extraction descriptors. Then, ensemble-based feature selection is employed to select the most significant and stable features. The ensemble bagging PU learning selects the reliable negative samples from the unlabeled samples using four supervised learning methods (support vector machines, random forest, logistic regression, and XGBoost). Then, stacking ensemble learning is applied using four base classifiers: logistic regression, artificial neural networks, random forest, and support vector machine. The experiments results show that PUStackNGly has a promising predicting performance compared to supervised learning methods. Furthermore, the proposed PUStackNgly outperforms the existing N-linked glycosylation prediction tools on an independent dataset with 95.11% accuracy, 100% recall 80.7% precision, 89.32% F1 score, 96.93% AUC, and 0.87 MCC.

show abstract

“…Briefly, the "no free lunch" theorem states that no single machine learning algorithm is best suited to all scenarios and datasets due to the associated generalization error [58][59][60] because one machine learning method would learn certain information from the dataset, whereas another would learn something different, depending on the specific underlying statistical learning principle. Stacking is an ensemble technique that combines information from multiple predictive models to generate a new model, and generally improves the prediction results through minimization of generalization error [61][62][63]. Here, the results (the difference between the predicted value and the original value) of different regressors used in the base layer along with the dataset provided to train the base layer are passed as a training dataset for the regressor used in the stacked meta layer.…”

Section: Stacking Frameworkmentioning

confidence: 99%

Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards

Lailvaux

Mishra²,

Kabir³

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts missing performance data accurately at the level of the individual from simple morphological measures. This model performed exceptionally well, even for performance traits that were missing values for >90% of the sampled individuals. Furthermore, incorporating phylogeny did not improve model fit, indicating that the phenotypic data alone preserved sufficient information to predict the performance based on morphological information. This approach can both significantly increase our understanding of performance evolution and act as a bridge to incorporate performance into future work on phenomics.

show abstract

StackCBPred: A stacking based prediction of protein-carbohydrate binding sites from sequence

Cited by 30 publications

References 76 publications

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta

Development and Evaluation of GlycanDock: A Protein–Glycoligand Docking Refinement Algorithm in Rosetta

PUStackNGly: Positive-Unlabeled and Stacking Learning for N-Linked Glycosylation Site Prediction

Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards

Contact Info

Product

Resources

About