{"id":32639490,"url":"https://github.com/raghavagps/nagbinder","last_synced_at":"2026-06-22T21:31:28.642Z","repository":{"id":106902304,"uuid":"198935271","full_name":"raghavagps/nagbinder","owner":"raghavagps","description":"A method for predicting NAG interacting residues in a protein from its primary sequence","archived":false,"fork":false,"pushed_at":"2026-05-05T06:38:06.000Z","size":11078,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2026-05-05T08:27:55.905Z","etag":null,"topics":["machine-learning-model","nag-binding","nag-interacting","prediction-algorithm","protein-bioinformatics"],"latest_commit_sha":null,"homepage":"http://webs.iiitd.edu.in/raghava/nagbinder","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/raghavagps.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2019-07-26T02:52:43.000Z","updated_at":"2026-05-05T06:36:26.000Z","dependencies_parsed_at":"2023-04-26T23:00:38.274Z","dependency_job_id":null,"html_url":"https://github.com/raghavagps/nagbinder","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/raghavagps/nagbinder","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Fnagbinder","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Fnagbinder/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Fnagbinder/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Fnagbinder/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raghavagps","download_url":"https://codeload.github.com/raghavagps/nagbinder/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raghavagps%2Fnagbinder/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34666961,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-22T02:00:06.391Z","response_time":106,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning-model","nag-binding","nag-interacting","prediction-algorithm","protein-bioinformatics"],"created_at":"2025-10-31T02:12:18.247Z","updated_at":"2026-06-22T21:31:28.637Z","avatar_url":"https://github.com/raghavagps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NAGbinder\nPrediction of NAG interacting residues\n\nNAGbinder is a Python-based tool for predicting NAG interacting residues in an uncharacterized protein chain. It involves various prediction models developed using machine learning techniques such as, Support Vector Classifier, Random Forest, Artificial Neural Network, which is implemented using Scikit package. These models are developed using features like (i) Binary Profile of patterns and (ii) Evolutionary Information (PSSM) matrix generated using PSI-BLAST.\nResidues have the score equal or above the selected threshold are said to be “Interacting” whereas residues showing lesser value than the threshold are considered as “Non-Interacting”. Prediction model developed using binary profiles where Random Forest was implemented, performed best in our study.\n\n# Reference\nPatiyal et al. (2020) An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence. \n\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/31654438\"\u003e Protein Sci. 201-210. doi: 10.1002/pro.3761\u003c/a\u003e\n\n# Zenodo\nhttps://doi.org/10.5281/zenodo.20034155\n\n## Web Server\nhttps://webs.iiitd.ac.in/raghava/nagbinder/\n# Installation\n\nCommand for downloading NAGbinder\n```\ngit clone https://github.com/raghavagps/nagbinder\n```\n\nNAGbinder is open-source Python-based software, which operates on the Python environment (Python version 3.3 or above) and can run on multi-OS systems (such as Windows, Linux and Mac operating systems). Before running NAGbinder, the user should make sure of all the following packages are available in their Python environment: sys, wget, os, shutil, scipy, numpy(), pandas(), sklearn version 0.19.1, math and re. Installation of Anaconda is recommended, and it is freely available on https://www.anaconda.com/download/ .\nThe user also needs to download the blastpr folder to run the prediction. Please run the commands given below to download and untar blastpr\n**COMMANDS**\n```\nwget -c http://webs.iiitd.edu.in/gpsr2/blastpr.zip\nunzip blastpr.zip\n```\n# For users who want to do prediction by using our NAGbinder package\n\n```\n1. cd nagbinder\n2. unzip nag_models.zip\n3. python3 nagbinder.py -h\n```\n\n# Examples for users to do NAG interacting residue prediction.\n\nThe input protein sequence for nagbinder.py should be in fasta format. Please find the example in example folder. The following parameters are required by nagbinder.py\n\n**COMMAND**\n```\npython3 nagbinder.py -i \u003cinput_file\u003e -o \u003coutput_file\u003e -m \u003cmethod\u003e -t \u003cthreshold\u003e\n```\nwhere,\n-       \u003cinput_file\u003e: Input file having sequence file in FASTA format\n-       \u003coutput_file\u003e: Output file generated by NAGbinder having prediction result\n- \\\u003cthreshold\u003e: User defined threshold score (between 0-1)\n- \\\u003cmethod\u003e: Machine Learning method and the type of input feature it used\n\u003e The value of method can be between 1-6 with each numeral representing the following prediction methods:\n\u003e1. Binary SVC\n\u003e2. Binary Random Forest\n\u003e3. Binary MLP\n\u003e4. Binary KNN\n\u003e5. PSSM SVC\n\u003e6. PSSM Random Forest\n\n\n### For more information type the following command\n```\npython3 nagbinder.py –h\n```\n\nIn our package, we have provided 6 different machine learning models which utilizes different features.\n- Method '1' is Support Vector Classifier which utilizes binary profile of the pattern as an input feature.\n- Method '2' is Random Forest Classifier which also utilizes binary profile of the pattern as an input feature.\n- Method '3' is Artificial Neural Network model developed using binary profile of the pattern as input feature.\n- Method '4' is K Nearest Neighbor method developed using binary profile of the pattern as input feature.\n- Method '5' is Support Vector Classifier which utilizes evolutionary information in the form of PSSM profile as an input feature.\n- Method '6' is Random Forest classifier which also utilizes evolutionary information in the form of PSSM profile as an input feature. The PSSM profile is generated using PSI-BLAST by running against the SwissProt database.\n\n## NAGbinder – Datasets\n\nNAGbinder provides gold‑standard datasets of NAG ligand‑interacting protein chains derived from the PDB. Standard protocols were used for dataset generation. The datasets are non‑redundant (CD‑HIT at 40% sequence identity) and comprise 231 NAG‑binding protein chains, split into training and validation sets.\n\nDataset\tProtein chains\tNAG‑interacting residues\tNon‑interacting residues\nTraining\t186\t1,335\t47,198\nValidation\t45\t650\t27,733\nTotal\t231\t1,985\t74,931\nTo facilitate effective use, we provide three dataset types:\n\nProtein chains with interaction annotation\nPatterns of length 9 (binary profiles)\nPSSM profiles of patterns (evolutionary information)\n## 📁 Dataset Type 1 – Protein chains with interaction annotation\n\nContains full protein chains where interacting residues are marked with + and non‑interacting residues with -.\n\nDataset\tDescription\tFiles\nMain\t186 NAG‑interacting protein chains with residue‑level annotations \nValidation\t45 NAG‑interacting protein chains with residue‑level annotations\n## 📁 Dataset Type 2 – Patterns (window length 9)\n\nContains sliding window patterns of length 9 generated from the PDB chains. Positive and negative patterns are provided separately for each chain.\n\nDataset\tDescription\tFiles\nMain\tPatterns (window length 9) from 186 NAG‑interacting chains – separate positive/negative pattern files per chain\t\nValidation\tPatterns (window length 9) from 45 NAG‑interacting chains – separate positive/negative pattern files per chain\t\n## 📁 Dataset Type 3 – PSSM profiles of patterns (window length 9)\n\nContains PSSM (Position‑Specific Scoring Matrix) profiles for each pattern of length 9, generated from the PDB chains. Positive and negative profiles are provided separately for each chain.\n\nDataset\tDescription\tFiles\nMain\tPSSM profiles for patterns from 186 NAG‑interacting chains – separate positive/negative profile files per chain\t\nValidation\tPSSM profiles for patterns from 45 NAG‑interacting chains – separate positive/negative profile files per chain\t\nM profiles for patterns from 45 NAG‑interacting chains – separate positive/negative profile files per chain\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavagps%2Fnagbinder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraghavagps%2Fnagbinder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraghavagps%2Fnagbinder/lists"}