scholarly journals TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

Crystals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 640 ◽  
Author(s):  
Chang Lu ◽  
Zhe Liu ◽  
Bowen Kan ◽  
Yingli Gong ◽  
Zhiqiang Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Surface Area ◽  
Transmembrane Protein ◽  
Pearson Correlation ◽  
Transmembrane Proteins ◽  
Correlation Coefficients ◽  
Accessible Surface Area ◽  
Interaction Sites ◽  
Accessible Surface

Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs’ surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues’ surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP’s rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

scholarly journals Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

2021 ◽  
Author(s):  
Shibao Wang ◽  
Jianqi Zhuang ◽  
Jiaqi Mu ◽  
Jia Zheng ◽  
Jiewei Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Influencing Factors ◽  
Landslide Susceptibility ◽  
Pearson Correlation ◽  
Confusion Matrix ◽  
Tibet Plateau ◽  
Validation Data ◽  
Landslide Hazards ◽  
Qinghai Tibet Plateau

Abstract The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.

scholarly journals Impact of residue accessible surface area on the prediction of protein secondary structures

2008 ◽  
Vol 9 (1) ◽  
pp. 357 ◽  
Author(s):  
Amir Momen-Roknabadi ◽  
Mehdi Sadeghi ◽  
Hamid Pezeshk ◽  
Sayed-Amir Marashi
Keyword(s):  
Surface Area ◽  
Secondary Structures ◽  
Accessible Surface Area ◽  
Protein Secondary Structures ◽  
Accessible Surface
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