Accessible Surface Area of Porous Materials: Understanding Theoretical Limits

2012 ◽  
Vol 24 (23) ◽  
pp. 3130-3133 ◽  
Author(s):  
Lev Sarkisov
Keyword(s):  
Porous Materials ◽  
Surface Area ◽  
Accessible Surface Area ◽  
Accessible Surface

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 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

scholarly journals Inter-protein residue covariation information unravels physically interacting protein dimers

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Sara Salmanian ◽  
Hamid Pezeshk ◽  
Mehdi Sadeghi
Keyword(s):  
Support Vector Machine ◽  
Mutual Information ◽  
Surface Area ◽  
Protein Sequences ◽  
Accessible Surface Area ◽  
Support Vector ◽  
Physical Interaction ◽  
Evolutionary Methods ◽  
Protein Dimers ◽  
Accessible Surface

Abstract Background Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only. Results In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively. Conclusions In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

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.

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