A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

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Classification of Surface Water Using Machine Learning Methods from Landsat Data in Nepal

Proceedings ◽

10.3390/ecsa-5-05833 ◽

2018 ◽

Vol 4 (1) ◽

pp. 43 ◽

Cited By ~ 2

Author(s):

Tri Dev Acharya ◽

Anoj Subedi ◽

He Huang ◽

Dong Ha Lee

Keyword(s):

Machine Learning ◽

Surface Water ◽

Ground Truth ◽

Vital Role ◽

Google Earth ◽

Landsat 8 ◽

Learning Methods ◽

Machine Learning Methods ◽

Water Classification

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Therefore, the monitoring and estimation of surface water is an essential task. In Nepal, surface water has different characteristics such as varying temperature, turbidity, depth, and vegetation cover, for which remote sensing technology plays a vital role. Single or multiple water index methods have been applied in the classification of surface water with satisfactory results. In recent years, machine learning methods with training datasets, have been outperforming different traditional methods. In this study, we tried to use satellite images from Landsat 8 to classify surface water in Nepal. Input of Landsat bands and ground truth from high resolution images available form Google Earth is used, and their performance is evaluated based on overall accuracy. The study will be will helpful to select optimum machine learning methods for surface water classification and therefore, monitoring and management of surface water in Nepal.

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Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm

International Journal of Molecular Sciences ◽

10.3390/ijms22020939 ◽

2021 ◽

Vol 22 (2) ◽

pp. 939

Author(s):

Jiazhi Song ◽

Guixia Liu ◽

Jingqing Jiang ◽

Ping Zhang ◽

Yanchun Liang

Keyword(s):

Machine Learning ◽

Protein Function ◽

Learning Algorithm ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Atp Binding ◽

Deep Convolutional Neural Networks ◽

Binding Residues

Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein–ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.

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