EPTool: A New Enhancing PSSM Tool for Protein Secondary Structure Prediction

Guo, Yuzhi; Wu, Jiaxiang; Ma, Hehuan; Wang, Sheng; Huang, Junzhou

doi:10.1089/cmb.2020.0417

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…PSSM represents the distribution of amino acid types on each position in the protein sequence [ 46 ]. Following the same procedure in [ 5 , 41 , 47 ], we get the PSSM matrix by searching Uniref50 database [ 48 ], and concatenate it with the one-hot vectors. As shown in Figure 2 , the input feature size is

where

and n is the length of the protein sequence.…”

Section: Methodsmentioning

confidence: 99%

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction

Guo

Wu²,

Ma³

et al. 2022

Biomolecules

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The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing. It has achieved high levels of sentence scoring, as well as reduced the latency. Conditionally Parameterized Convolution (CondConv) is another novel study which has gained great success in the image processing area. Compared with vanilla CNN, CondConv uses extra sample-dependant modules to conditionally adjust the convolutional network. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilizes the power of both CondConv and GCNN. CondGCNN leverages an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences by better capturing protein sequential features. In addition, we explore the similarity between the secondary structure prediction problem and the image segmentation problem, and propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) to capture fine boundary details in secondary structure. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Casp11, CASP12, CASP13, and CASP14 datasets. We also conducted ablation studies over each component to verify the effectiveness. Our method is expected to be useful for any protein related prediction tasks, which is not limited to protein secondary structure prediction.

show abstract

where

and n is the length of the protein sequence.…”

Section: Methodsmentioning

confidence: 99%

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction

Guo

Wu²,

Ma³

et al. 2022

Biomolecules

Self Cite

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 1B , the DeepPPISP proposed by Zeng et al ( Zeng et al, 2020 ) for PPIS prediction had three types of input: position-specific scoring matrix (PSSM), secondary structure, and raw protein sequences. The PSSM is an excellent feature extractor for protein sequences and thus have widely been applied to problems in the field of computational biology, such as predicting protein post-translational modification ( Huang et al, 2013 ; Huang et al, 2014 ; Dehzangi et al, 2017 ), membrane type ( Wang et al, 2019 ), protein-RNA binding site ( Liu et al, 2021 ), and structure ( Guo et al, 2021 ). The quality of PSSM features is closely associated with the underlying multiple sequence alignments.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

Wang

Zhang

et al. 2021

Front. Genet.

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Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.

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“…A survey about the PPII helix structures prediction shows that most algorithms use the traditional features and manually select features to combine. Most research works only adopt the local features [26][27][28][29] or the global features [30][31][32][33], which decreases the accuracy of PPII helix structure prediction. Both the local and longrange interactions among amino acid residues determine the PPII helix.…”

Section: Local Feature Extraction By Multichannel Convolutionmentioning

confidence: 99%

BERT-PPII: The Polyproline Type II Helix Structure Prediction Model Based on BERT and Multichannel CNN

Feng

Wang

et al. 2022

BioMed Research International

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Predicting the polyproline type II (PPII) helix structure is crucial important in many research areas, such as the protein folding mechanisms, the drug targets, and the protein functions. However, many existing PPII helix prediction algorithms encode the protein sequence information in a single way, which causes the insufficient learning of protein sequence feature information. To improve the protein sequence encoding performance, this paper proposes a BERT-based PPII helix structure prediction algorithm (BERT-PPII), which learns the protein sequence information based on the BERT model. The BERT model’s CLS vector can fairly fuse sample’s each amino acid residue information. Thus, we utilize the CLS vector as the global feature to represent the sample’s global contextual information. As the interactions among the protein chains’ local amino acid residues have an important influence on the formation of PPII helix, we utilize the CNN to extract local amino acid residues’ features which can further enhance the information expression of protein sequence samples. In this paper, we fuse the CLS vectors with CNN local features to improve the performance of predicting PPII structure. Compared to the state-of-the-art PPIIPRED method, the experimental results on the unbalanced dataset show that the proposed method improves the accuracy value by 1% on the strict dataset and 2% on the less strict dataset. Correspondingly, the results on the balanced dataset show that the AUCs of the proposed method are 0.826 on the strict dataset and 0.785 on less strict datasets, respectively. For the independent test set, the proposed method has the AUC value of 0.827 on the strict dataset and 0.783 on the less strict dataset. The above experimental results have proved that the proposed BERT-PPII method can achieve a superior performance of predicting the PPII helix.

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EPTool: A New Enhancing PSSM Tool for Protein Secondary Structure Prediction

Cited by 9 publications

References 5 publications

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction

Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

BERT-PPII: The Polyproline Type II Helix Structure Prediction Model Based on BERT and Multichannel CNN

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