Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile

Patiyal, Sumeet; Dhall, Anjali; Bajaj, Khushboo; Sahu, Harshita; Raghava, Gajendra P. S.

doi:10.1093/bib/bbac538

Cited by 11 publications

(7 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both CLAPE-RB and CLAPE-AB performed well on the testing sets, with CLAPE-AB achieving the AUC of 0.920 ( Supplementary Table 6 available online at http://bib.oxfordjournals.org/ ), which was relatively high and could be applied to accurately predict the paratope of a given antibody sequence. Moreover, the AUC of CLAPE-RB trained on TE161 was 0.830 ( Supplementary Table 6 available online at http://bib.oxfordjournals.org/ ), which surpassed the existing sequence-based RNA-binding sites models [ 44 , 45 ]. We also plotted the ROC and AUC curves to visualize the overall model performance of CLAPE-RB and CLAPE-AB ( Figure 6C and D ).…”

Section: Resultsmentioning

confidence: 96%

Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Liu,

Tian

2023

Briefings in Bioinformatics

View full text Add to dashboard Cite

Protein–DNA interaction is critical for life activities such as replication, transcription and splicing. Identifying protein–DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called Contrastive Learning And Pre-trained Encoder (CLAPE), which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein–DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the area under ROC curve values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein–ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape.

show abstract

Section: Resultsmentioning

confidence: 96%

Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Liu,

Tian

2023

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Description of Dataset 2 . The dataset is sourced from the research conducted on Pprint2 27 , a convolutional neural network-based method for the purpose of predicting protein-RNA interacting residues. It comprises a collection of 706 protein-RNA complexes, encompassing a total of 25525 binding residues (positive) and 216228 non-binding residues (negative).…”

Section: Methodsmentioning

confidence: 99%

Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction

Zhang,

Wang,

Wei

2023

Preprint

View full text Add to dashboard Cite

Protein-ligand interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-ligand interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others aim for multiple types but fail to effectively utilize information across different interactions, leading to increased complexity and inefficiency. This study presents a novel deep learning model named MucLiPred and a dual contrastive learning mechanism aimed at improving the prediction of multiple ligand-protein interactions and the identification of potential ligand-binding residues. We proposed two novel contrastive learning paradigms at residue and type levels, training the discriminative representation of samples. The residue-level contrastive learning hones in on distinguishing binding from non-binding residues with precision, shedding light on nuanced local interactions. In contrast, the type-level contrastive learning delves into the overarching context of ligand types, ensuring that representations of identical ligand types gravitate closer in the representational space and bolstering the model’s proficiency in discerning interaction motifs, enhancing the model’s ability to recognize global interaction patterns. This approach culminates in nuanced multi-ligand predictions, unraveling relationships between various ligand types, and fortifying the potential for precise protein-ligand interaction predictions. Empirical findings underscore MucLiPred’s dominance over existing models, highlighting its robustness and unparalleled prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential ligand-binding residues with precision. By optimizing the model’s structure, we discovered that separating representation and classification tasks, leads to improved performance. Consequently, MucLiPred stands out as a groundbreaking tool in protein-ligand interaction prediction, laying the groundwork for future endeavors in this complex arena.

show abstract

“…Description of Data Set 2. The data set is sourced from and split by the research conducted on Pprint2, 34 a convolutional neural network-based method for the purpose of predicting protein−RNA interacting residues. It comprises a collection of 706 protein−RNA complexes, encompassing a total of 25,525 binding residues (positive) and 216,228 nonbinding residues (negative).…”

Section: Data Setsmentioning

confidence: 99%

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Zhang,

Wang,

Wei

2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Protein−molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein−molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein−molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein−molecule interaction prediction, setting a new precedent for future research in this field.

show abstract

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile

Cited by 11 publications

References 58 publications

Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Multi-Level Contrastive Learning for Protein-Ligand Binding Residue Prediction

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Contact Info

Product

Resources

About