A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

Li, Yang; Liu, Xiaozhang; You, Zhu‐Hong; Li, Liping; Guo, Jianxin; Wang, Zheng

doi:10.1002/int.22332

Cited by 20 publications

(12 citation statements)

References 49 publications

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“…PSSM was originally introduced by Gribskov et al [ 33 ] in 1987 and is commonly used to detect distantly related proteins and protein folding patterns [ 34 ]. Currently, some researchers have done a lot of related work using PSSM encoding information in many fields of bioinformatics such as identification of DNA binding proteins [ 35 ], the identification of drug-target interaction [ 36 ], prediction of membrane protein types [ 37 ], and protein-protein interaction site prediction [ 38 ]. In this experiment, we employed the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) [ 39 ] to convert each protein sequence into a PSSM, which is widely adopted for the numerical representation of protein sequences for further use in PPI detection tasks.…”

Section: Materials and Methodologymentioning

confidence: 99%

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation

Wang

You

et al. 2022

Computational and Mathematical Methods in Medicine

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Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics. However, since traditional experimental methods are both time-consuming and costly, it is difficult to analyze and predict the massive amount of PPI data quickly and accurately. To address these issues, many computational systems employing machine learning knowledge were widely applied to PPIs prediction, thereby improving the overall recognition rate. In this paper, a novel and efficient computational technology is presented to implement a protein interaction prediction system using only protein sequence information. First, the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) was employed to generate a position-specific scoring matrix (PSSM) containing protein evolutionary information from the initial protein sequence. Second, we used a novel data processing feature representation scheme, MatFLDA, to extract the essential information of PSSM for protein sequences and obtained five training and five testing datasets by adopting a five-fold cross-validation method. Finally, the random fern (RFs) classifier was employed to infer the interactions among proteins, and a model called MatFLDA_RFs was developed. The proposed MatFLDA_RFs model achieved good prediction performance with 95.03% average accuracy on Yeast dataset and 85.35% average accuracy on H. pylori dataset, which effectively outperformed other existing computational methods. The experimental results indicate that the proposed method is capable of yielding better prediction results of PPIs, which provides an effective tool for the detection of new PPIs and the in-depth study of proteomics. Finally, we also developed a web server for the proposed model to predict protein-protein interactions, which is freely accessible online at http://120.77.11.78:5001/webserver/MatFLDA_RFs.

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Section: Materials and Methodologymentioning

confidence: 99%

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation

Wang

You

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The molecular structure is complex and difficult to use directly. To facilitate the calculation of drug molecular structure, it was necessary to vectorize its molecular structure [ 34 ]. The molecular fingerprint [ 35 ] is an abstract representation of a molecule, which encodes a molecule as a series of bit vectors, in which each bit on the molecular fingerprint corresponds to a molecular fragment, as shown in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Zhao

You

et al. 2021

Cancers

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Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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“…Pseudo amino acid sequence : Protein sequence carries a massive of information such as binding sites, which is essential to analyze the molecule interaction networks 38 . Although the protein sequences largely overlap with the nucleotide sequences, they are still complementary for each other, since the genomic data and peptide data have their own distinctions, the same genomic sequence do not always give the same peptide level sequence.…”

Section: Methodsmentioning

confidence: 99%

Imbalance deep multi‐instance learning for predicting isoform–isoform interactions

Zeng

Wang

et al. 2021

Int J Intell Syst

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Multi‐instance learning (MIL) can model complex bags (samples) that are further made of diverse instances (subsamples). In typical MIL, the labels of bags are known while those of individual instances are unknown and to be specified. In this paper we propose an imbalanced deep multi‐instance learning approach (IDMIL‐III) and apply it to predict genome‐wide isoform–isoform interactions (IIIs). This prediction task is crucial for precisely understanding the interactome between proteoforms and to reveal their functional diversity. The current solutions typically formulate the prediction of IIIs as a MIL problem by pairing two genes as a “bag” and any two isoforms spliced from these two genes as “instances.” The key instances (interacting isoform pairs) trigger the label of the positive (interacting) gene bags, which is important for identifying the IIIs. Furthermore, the prediction task was simplified as a balanced classification problem, which in practice is a rather imbalanced one. To address these issues, IDMIL‐III fuses RNA‐seq, nucleotide sequence, amino acid sequence and exon array data, and further introduces a novel loss function to separately model the loss of positive pairs and of negative pairs, and thus to avoid the expected loss dominated by majority negative pairs. In addition, it includes an attention strategy to identify positive isoform pairs from a positive gene bag. Extensive experimental results prove the effectiveness of IDMIL‐III on predicting IIIs. Particularly, IDMIL‐III achieves an F1 value as 95.4%, at least 3.8% higher than those of competitive methods at the gene‐level; and obtains an F1 as 29.8%, at least 2.4% higher than the state‐of‐the‐art methods at the isoform‐level. The code of IDMIL‐III is available at http://mlda.swu.edu.cn/codes.php?name=IDMIL-III.

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A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

Cited by 20 publications

References 49 publications

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation

A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Imbalance deep multi‐instance learning for predicting isoform–isoform interactions

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