RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression

Tarafder, Sumit; Ahmed, Md Toukir; Iqbal, Sumaiya; Hoque, Tamjidul; Rahman, M. Sohel

doi:10.1016/j.jtbi.2017.12.029

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…ASA refers to the accessible area of each amino acid to a solvent of the protein in 3D configuration [65][66][67]. Since the value of an amino acid involves the protein configuration, the predicted ASA value of individual amino acids displays vital information regarding the protein structure.…”

Section: Accessible Surface Area (Asa)mentioning

confidence: 99%

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

Singh

Sharma

Dehzangi

et al. 2020

Genes

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Post-translational modification (PTM) is a critical biological reaction which adds to the diversification of the proteome. With numerous known modifications being studied, pupylation has gained focus in the scientific community due to its significant role in regulating biological processes. The traditional experimental practice to detect pupylation sites proved to be expensive and requires a lot of time and resources. Thus, there have been many computational predictors developed to challenge this issue. However, performance is still limited. In this study, we propose another computational method, named PupStruct, which uses the structural information of amino acids with a radial basis kernel function Support Vector Machine (SVM) to predict pupylated lysine residues. We compared PupStruct with three state-of-the-art predictors from the literature where PupStruct has validated a significant improvement in performance over them with statistical metrics such as sensitivity (0.9234), specificity (0.9359), accuracy (0.9296), precision (0.9349), and Mathew’s correlation coefficient (0.8616) on a benchmark dataset.

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Section: Accessible Surface Area (Asa)mentioning

confidence: 99%

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

Singh

Sharma

Dehzangi

et al. 2020

Genes

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“…The quantitative approach for measuring the exposure of residues is to calculate the relatively accessible surface area (rASA) of the residues (Tarafder et al, 2018). rASA reflects the exposure of a single residue to the solvent, making it a directive reference of protein structures.…”

Section: Introductionmentioning

confidence: 99%

TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence

et al. 2021

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Transmembrane protein (TMP) is an important type of membrane protein that is involved in various biological membranes related biological processes. As major drug targets, TMPs’ surfaces are highly concerned to form the structural biases of their material-bindings for drugs or other biological molecules. However, the quantity of determinate TMP structures is still far less than the requirements, while artificial intelligence technologies provide a promising approach to accurately identify the TMP surfaces, merely depending on their sequences without any feature-engineering. For this purpose, we present an updated TMP surface residue predictor TMP-SSurface2 which achieved an even higher prediction accuracy compared to our previous version. The method uses an attention-enhanced Bidirectional Long Short Term Memory (BiLSTM) network, benefiting from its efficient learning capability, some useful latent information is abstracted from protein sequences, thus improving the Pearson correlation coefficients (CC) value performance of the old version from 0.58 to 0.66 on an independent test dataset. The results demonstrate that TMP-SSurface2 is efficient in predicting the surface of transmembrane proteins, representing new progress in transmembrane protein structure modeling based on primary sequences. TMP-SSurface2 is freely accessible at https://github.com/NENUBioCompute/TMP-SSurface-2.0.

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Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis

Ahmed,

Kamruzzaman

2024

Smart Agricultural Technology

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RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression

Cited by 7 publications

References 66 publications

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids

TMP- SSurface2: A Novel Deep Learning-Based Surface Accessibility Predictor for Transmembrane Protein Sequence

Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis

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