TM-ZC: A Deep Learning-Based Predictor for the Z-Coordinate of Residues in α-Helical Transmembrane Proteins

Lü, Chang; Gong, Yu; Liu, Zhe; Guo, Yuanzhao; Ma, Zhiqiang; Wang, Han

doi:10.1109/access.2020.2976797

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…This work evaluated the singular value decomposition (SVD) of the distance map, the secondary structure, and the relative accessible surface area (rASA) of each residue. Previous work usually fetched the secondary structure and rASA using sequenced-based prediction tools when the real structures of proteins were unknown [ 35 , 36 ]. Now with AlphaFold2, we have the opportunity to directly calculate these features from the predicted accurate 3D structures as new predicted features different from those predicted by traditional sequence-based tools.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction

Liu

Pan

et al. 2022

Biology

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Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.

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Section: Resultsmentioning

confidence: 99%

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction

Liu

Pan

et al. 2022

Biology

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“…The Z-coordinate (Zcoord) is defined as the residue's distance to the center of the membrane [50] and reflects the high correlation with the ligand binding and the protein-protein binding regions [51]. It implicitly contains information about TMPs' secondary structure, such as re-entrant helices, interfacial helices, a TM helix's tilt, and loop lengths.…”

Section: Z-coordinatementioning

confidence: 99%

Prediction of Metal Ion Binding Sites of Transmembrane Proteins

Yin²,

Wang

2021

Computational and Mathematical Methods in Medicine

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The metal ion binding of transmembrane proteins (TMPs) plays a fundamental role in biological processes, pharmaceutics, and medicine, but it is hard to extract enough TMP structures in experimental techniques to discover their binding mechanism comprehensively. To predict the metal ion binding sites for TMPs on a large scale, we present a simple and effective two-stage prediction method TMP-MIBS, to identify the corresponding binding residues using TMP sequences. At present, there is no specific research on the metal ion binding prediction of TMPs. Thereby, we compared our model with the published tools which do not distinguish TMPs from water-soluble proteins. The results in the independent verification dataset show that TMP-MIBS has superior performance. This paper explores the interaction mechanism between TMPs and metal ions, which is helpful to understand the structure and function of TMPs and is of great significance to further construct transport mechanisms and identify potential drug targets.

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“…After that, a series of methods using machine learning including SVC, SVR, and SVM emerged, which can be automatically divided into two categories according to their functionality: binary classifier and rASA real value predictor. All of these machine learningbased methods were designed for α-TMPs, some methods were just effective with the transmembrane region of the proteins restrictedly, such as TMX (Liwicki et al, 2007;Wang et al, 2011), TMexpoSVC (Lai et al, 2013), and TMexpoSVR (Lai et al, 2013), only MPRAP (Illergård et al, 2010) and MemBrane-Rasa (Xiao and Shen, 2015;Yin et al, 2018) were able to predict rASA of the entire sequence. Our previous work (Lu et al, 2019a) combined Inception blocks with CapsNet, proving that deep learning takes many advantages for the prediction but there is still room for accuracy improvement.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we proposed an attention-enhanced bidirectional LSTM network named TMP-SSurface2 to predict rASA of TMPs at the residue level, which was implemented on top of the CNN-based Z-coordinate predictor TM-ZC ( Lu et al, 2020 ). TMP-SSurface2 was trained and tested against the non-redundant benchmark dataset we created with primary sequences as input, improving the Pearson correlation coefficients (CC) value performance of the old version from 0.584 to 0.659, and reduced the mean absolute error (MAE) from 0.144 to 0.140.…”

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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TM-ZC: A Deep Learning-Based Predictor for the Z-Coordinate of Residues in α-Helical Transmembrane Proteins

Cited by 4 publications

References 47 publications

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction

Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction

Prediction of Metal Ion Binding Sites of Transmembrane Proteins

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

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