Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps

Amirkhani, Abdollah; Kolahdoozi, Mojtaba; Wang, Chen; Kurgan, Lukasz

doi:10.1109/tcbb.2018.2890261

Cited by 29 publications

(17 citation statements)

References 81 publications

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“…In this study, we make an attempt to train our models on large set of proteins. Initially, we obtained 1057 and 360 protein sequences from recently published articles hybridNAP [49] and proNA2020 [34]. In order to create dataset of redundant RNA-binding protein, we removing the redundant sequences using CD-HIT at 30%.…”

Section: Discussionmentioning

confidence: 99%

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

Patiyal

Dhall

Bajaj

et al. 2022

Preprint

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This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R, and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76 when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with MCC of 0.49 on the validation dataset. Our best model outperform existing methods when evaluated on the validation dataset. A user-friendly standalone software and web based server named "Pprint2" has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2) .

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Section: Discussionmentioning

confidence: 99%

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

Patiyal

Dhall

Bajaj

et al. 2022

Preprint

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“…This method predicts relative solvent accessibility from a single sequence (without alignment), and thus it much faster than the other predictors that require calculation of multiple sequence alignment. It also provides accurate prediction, which is why it was recently used in related studies (Zhang et al, 2017; Amirkhani et al, 2018; Meng and Kurgan, 2018). We convert the numeric relative solvent accessibility of residues into a binary annotation (solvent exposed vs .…”

Section: Methodsmentioning

confidence: 99%

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Ghadermarzi

et al. 2019

Front. Genet.

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Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

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“…In some of the methods, such as BindN-RF [27], DBindR [41], BindN+ [26], DNABR [42], and PDNASite [43], the performance is relatively higher, which could be due to the overfitting of the model, since the used datasets are smaller in size. On the other hand, recent methods like TargetDNA [44], HybridNAP [28], funDNApred [45], iProDNA-CapsNet [46], and ProNA2020 [30] have used larger datasets as compared to the previous methods; the DBPred had used the equivalent dataset to the recently developed methods and outperformed them. As shown in Table 5, most methods have provided the webserver facility, but many are non-functional now.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences

Patiyal

Dhall

Raghava

2021

Preprint

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DNA-protein interaction is one of the most crucial interactions in the biological system, which decide the fate of many processes such as transcription, regulation of gene expression, splicing, and many more. Though many computational approaches exist that can predict the DNA interacting residues from the protein sequences, there is still a significant opportunity for improvement in terms of performance and accessibility. In this study, we have downloaded the benchmark dataset from method hybridNAP and recently published method ProNA2020, for training and validation purposes, that comprise 864 and 308 proteins, respectively. We have implemented CD-HIT software to handle the redundancy with 30% identity, and left with 646 proteins for training and 46 proteins for validation purposes, in which the validation dataset do not share more than 30% of sequence identity with the training dataset. We have generated amino acid binary profiles, physicochemical-properties based binary profiles, PSSM profiles, and a combination of all profiles described as hybrid feature. 1D-CNN based model performed best as compared to other models for each set of features. The model developed using amino acid binary profile achieved AUROC of 0.83 and 0.74 for training and validation dataset. Using physicochemical properties based binary profile, model attained AUROC of 0.86 and 0.73 for training and validation dataset. Model generated using PSSM profile resulted in the better performance with AUROC 0.91 and 0.74 for training and validation dataset. And, model developed using hybrid of all features performed best with AUROC of 0.91, and 0.79 for training and validation dataset, respectively. We have compared our method’s performance with the current approach and shown improvements. We have included the best-performing models in the standalone and web server accessible at https://webs.iiitd.edu.in/raghava/dbpred. DBPred is an effective approach to predict the DNA interacting residues in the protein using its primary structure.

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Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps

Cited by 29 publications

References 81 publications

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

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

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

DBpred: A deep learning method for the prediction of DNA interacting residues in protein sequences

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