SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

Agrawal, Piyush; Mishra, Gaurav; Raghava, Gajendra P. S.

doi:10.1101/625806

Cited by 3 publications

(2 citation statements)

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“…These methods fail if the structure of a protein is not available. To overcome this limitation, sequence‐based methods have been developed in the past for predicting the protein residues that interact with specific ligands such as ATP, GTP, NAD, and SAM . To the best of our knowledge, the method described here is the first for predicting NAG interacting residues in a protein from its amino acid sequence.…”

Section: Introductionmentioning

confidence: 99%

“…2 In the last few decades, substantial efforts have been made toward the identification of the ligand-binding residues in a protein as shown in the review Sousa et al 3 At first, nonspecific methods were developed to predict the binding sites or pockets in the proteins, irrespective of their ligands, 4,5 but soon it was realized that each ligand possesses different physical and chemical properties. Therefore, new computational methods specific to the ligands came into the picture, [6][7][8][9][10] which performed better as compared to nonspecific methods. 11,12 Broadly, computational methods can be divided into two categories, structure-based and sequence-based.…”

Section: Introductionmentioning

confidence: 99%

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NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

et al. 2019

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N‐acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG‐interacting residues in a protein from its primary sequence information. We extracted 231 NAG‐interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG‐interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG‐interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG‐interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG‐interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

et al. 2019

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GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

Agrawal

Kumar

Usmani

et al. 2019

Preprint

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18Background: In past number of web-based resources has been developed in the field of 19Bioinformatics. These resources are heavily used by scientific community to provide solution 20 for challenges faced by experimental researchers particularly in the field of biomedical 21 sciences. There are number of challenges in utilizing full potential of these services that 22includes internet speed, limits on computing power, and security of data. In order to enhance 23 utilities of these web-based assets, we developed a docker-based container that integrates 24 large number resources available in literature. 25Results: This paper describes GPSRdocker a docker-based container developed for providing 26 wide-range of computational tools in the field of bioinformatics particularly in genomics, 27proteomics and system biology. Majority of tools integrated in GPSRdocker are based on 28 web services developed at Raghava's group in last two decades. Broadly, these tools can be 29 categorized in three categories; i) general scripts, ii) supporting software and iii) major 30 standalone software. In order to facilitate students or developers working in the field of 31 bioinformatics, we developed general scripts in Perl and Python. These general-purpose 32 scripts serve as building block for any bioinformatics tools like computing 33 features/descriptors of a protein. Supporting software packages includes SCIKIT, WEKA, 34 SVM light , and PSI-BLAST; these software packages allow one to develop/implement 35 bioinformatics software. Major Standalone software is core of this container which allows 36 predicting function/class of biomolecules. These tools can be classified broadly in following 37 categories; protein annotation, epitope-based vaccines, prediction of interaction and drug 38 discovery. 39 Conclusion:A docker-based container has been developed which can be easily run on any 40 operating system as well as it can be directly ported on cloud. Scripts can be run to build 41 pipelines for addressing problems at system level like prediction of vaccine candidate for a 42 3 pathogen. GPSRdocker including manual is available free for academic use from 43 https://webs.iiitd.edu.in/gpsrdocker. 44

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SAMbinder: A Web Server for Predicting S-Adenosyl-L-Methionine Binding Residues of a Protein From Its Amino Acid Sequence

2020

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Motivation: S-adenosyl-L-methionine (SAM) is an essential cofactor present in the biological system and plays a key role in many diseases. There is a need to develop a method for predicting SAM binding sites in a protein for designing drugs against SAM associated disease. To the best of our knowledge, there is no method that can predict the binding site of SAM in a given protein sequence. Result: This manuscript describes a method SAMbinder, developed for predicting SAM interacting residue in a protein from its primary sequence. All models were trained, tested, and evaluated on 145 SAM binding protein chains where no two chains have more than 40% sequence similarity. Firstly, models were developed using different machine learning techniques on a balanced data set containing 2,188 SAM interacting and an equal number of noninteracting residues. Our random forest based model developed using binary profile feature got maximum Matthews Correlation Coefficient (MCC) 0.42 with area under receiver operating characteristics (AUROC) 0.79 on the validation data set. The performance of our models improved significantly from MCC 0.42 to 0.61, when evolutionary information in the form of the position-specific scoring matrix (PSSM) profile is used as a feature. We also developed models on a realistic data set containing 2,188 SAM interacting and 40,029 non-interacting residues and got maximum MCC 0.61 with AUROC of 0.89. In order to evaluate the performance of our models, we used internal as well as external cross-validation technique.

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SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

Cited by 3 publications

References 45 publications

NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

SAMbinder: A Web Server for Predicting S-Adenosyl-L-Methionine Binding Residues of a Protein From Its Amino Acid Sequence

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