Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

Gu, Xingyue; Chen, Zhihua; Wang, Donghua

doi:10.3389/fbioe.2020.00635

Cited by 13 publications

(8 citation statements)

References 88 publications

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“…The latter two groups represent the significance of global content of different amino acid groups and the order of amino acid residues within Cas12 sequences respectively. The C/T/D descriptors represent the Composition, Transition, and Distribution patterns of a specific physicochemical property in protein sequences 44–46 . Accordingly, the 20 amino acids are categorized into three main groups based on seven physicochemical attributes namely hydrophobicity, Van der Waals Volume, polarity, polarizability, charge, secondary structures, and solvent accessibility together accounting for total C/T/D descriptors 47,48 .Composition descriptors (CTDC), represent the global composition of the three encoded amino acid groups of a particular property within the complete protein sequence. The top contributing descriptor “normwaalsvolume.G3” in Figure 2b is a CTDC descriptor describing the importance of group-3 normalized Van der Waals Volume residues (M,H,K,F,R,Y,W) having large residual volume in the Cas12 protein sequences. These bulky residues in protein sequences are usually associated with bulkiness of the protein structure which may have implications on the three-dimensional structure and functional sites of the protein.…”

Section: Resultsmentioning

confidence: 99%

“…The C/T/D descriptors represent the Composition, Transition, and Distribution patterns of a specific physicochemical property in protein sequences 44–46 . Accordingly, the 20 amino acids are categorized into three main groups based on seven physicochemical attributes namely hydrophobicity, Van der Waals Volume, polarity, polarizability, charge, secondary structures, and solvent accessibility together accounting for total C/T/D descriptors 47,48 .Composition descriptors (CTDC), represent the global composition of the three encoded amino acid groups of a particular property within the complete protein sequence.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, these categories are commonly used for various protein or peptide prediction related tasks. [44][45][46][47][48]…”

Section: Description Of the Important Features Of The Two Cas Familiesmentioning

confidence: 99%

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Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Madugula,

Pujar,

Bharani

et al. 2024

Preprint

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The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Madugula,

Pujar,

Bharani

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Considering that there are still more features in 11,045 dimensions, we tried to use MRMD2.0 [ 36 – 38 ] for feature dimension reduction. MRMD2.0 integrates rich feature selection algorithms and feature ranking algorithms and is superior to the single feature selection algorithm.…”

Section: Discussionmentioning

confidence: 99%

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Zhao

Pan

Zou

et al. 2022

Computational and Mathematical Methods in Medicine

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Enhancers are a class of noncoding DNA elements located near structural genes. In recent years, their identification and classification have been the focus of research in the field of bioinformatics. However, due to their high free scattering and position variability, although the performance of the prediction model has been continuously improved, there is still a lot of room for progress. In this paper, density-based spatial clustering of applications with noise (DBSCAN) was used to screen the physicochemical properties of dinucleotides to extract dinucleotide-based auto-cross covariance (DACC) features; then, the features are reduced by feature selection Python toolkit MRMD 2.0. The reduced features are input into the random forest to identify enhancers. The enhancer classification model was built by word2vec and attention-based Bi-LSTM. Finally, the accuracies of our enhancer identification and classification models were 77.25% and 73.50%, respectively, and the Matthews’ correlation coefficients (MCCs) were 0.5470 and 0.4881, respectively, which were better than the performance of most predictors.

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“…CTDC features represent the distribution patterns of amino acids for specific structural or physicochemical properties in a protein or peptide sequence. CTDC refers to the composition of CTD descriptors that are computed by the following procedures: 1) transforming amino acid sequences into sequences for structural or physicochemical properties; 2) according to Tomii and Kanehisa’s major amino acid index clustering, 20 amino acids were divided into three groups for each of the seven different physicochemical properties, detailed calculation of which could be seen in previous studies ( Chen et al, 2020 ; Gu et al, 2020 ). In fact, CTDC has been successfully applied to the prediction of G protein-coupled receptors ( Gu et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites

Liu

Xu²,

Ya-ping³

et al. 2022

Front. Genet.

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Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.

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Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods

Cited by 13 publications

References 88 publications

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites

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