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bioRxiv · 10.1101/2022.06.03.494705

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

Patiyal, S.·Dhall, A.·Bajaj, K.·Sahu, H.·Raghava, G. P. S.
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Abstract

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) Key PointsO_LIMachine learning based models were developed using different profiles C_LIO_LIPSSM profile of a protein was created to extract evolutionary information C_LIO_LIPSSM profiles of proteins were generated using PSI-BLAST C_LIO_LIConvolutional neural network based model was developed using PSSM profile C_LIO_LIWebserver, Python- and Perl-based standalone package, and GitHub is available C_LI Authors BiographyO_LISumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIKhushboo Bajaj is currently working as MTech in Computer Science and Engineering from Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIHarshita Sahu is currently working as MTech in Computer Science and Engineering from Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

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Patiyal, S., Dhall, A., Bajaj, K., Sahu, H., Raghava, G. P. S.. 2022-06-04. Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile. https://doi.org/10.1101/2022.06.03.494705

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