TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Liu, Zhe; Gong, Yu; Bao, Yihang; Guo, Yuanzhao; Wang, Han; Lin, Guan Ning

doi:10.3389/fbioe.2020.629937

Cited by 18 publications

(17 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluated the performance of MASSP relative to other methods in the field using the same test set. We first compared it to Jufo9D, which was previously developed in our group, and five other popular secondary structure prediction methods (PSIPRED, RaptorX-Property, SPINE-X, , NetSurfP-2.0, and TMP-SS). MASSP performs comparably or better than all secondary structure prediction methods evaluated in this work (Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…We then compared the performance of MASSP with six other commonly used methods (JUFO9D, MEMSAT3, OCTOPUS, TMHMM2, TMP-SS, and TOPCONS2) in predicting transmembrane segments and topology for TM-α and bitopic proteins (Tables and ). Our comparison shows that MASSP has the best performance in predicting the number and location of TMHs.…”

Section: Resultsmentioning

confidence: 99%

“…For transmembrane segment detection, MEMSAT3, 30 OCTOPUS 31 (for TM-α proteins), BOCTOPUS2, 32 and PRED-TMBB2 33 (for TM-β proteins) are the classic methods. Recent innovations in deep learning also enabled the development of more sophisticated methods, such as TMP-SS 34 and DMCTOP, 35 for membrane segment detection and topology prediction.…”

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Mendenhall

Capra

et al. 2021

J. Proteome Res.

View full text Add to dashboard Cite

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Mendenhall

Capra

et al. 2021

J. Proteome Res.

View full text Add to dashboard Cite

show abstract

“…The OneHot and HHblits profiles, which represent the sequential information and sequence conservatism, respectively, are two classic features in sequence-based protein-related prediction tasks [ 28 , 29 , 30 ]. The HHblits profiles were generated by HHblits [ 20 ], and the obtained matrix consisted of 30 dimensions.…”

Section: Methodsmentioning

confidence: 99%

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

Liu

Pan

et al. 2022

Biology

View full text Add to dashboard Cite

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.

show abstract

“…Despite improvement observed inaccuracy, it was not found to be computationally efficient. Yet another novel computational method using deep learning was investigated in [ 18 ], therefore, achieving secondary structure prediction accuracy. Moreover, a learning strategy performed based on the multi-task model was also utilized in predicting secondary structures and the trans-membrane helixes.…”

Section: Related Workmentioning

confidence: 99%

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction

Nallasamy

Seshiah²

2022

Neural Comput & Applic

View full text Add to dashboard Cite

In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective.

show abstract

TMPSS: A Deep Learning-Based Predictor for Secondary Structure and Topology Structure Prediction of Alpha-Helical Transmembrane Proteins

Cited by 18 publications

References 53 publications

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

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

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction

Contact Info

Product

Resources

About