Shipra Jain

Purpose: Over the years, several proteins and peptides with diverse therapeutic properties such as anticancer, antimicrobial, antihypertensive effects have been discovered. However, only a few hundred proteins are considered druggable with US FDA approval, while most of them failed during clinical trials. Experimental Design: This study systematically investigates the compositional and physicochemical properties of FDA‐approved proteins to develop predictive models for identifying druggable proteins. Our main dataset comprises of 356 FDA approved proteins as positive dataset and equal number of randomly selected proteins as negative dataset. We used 80% data as training set and 20% as independent validation data, with no protein in validation dataset having more than 40% similarity with any protein in training dataset. Result: Random forest‐based model developed using SVC‐L1 selected features obtained maximum performance AUC of 0.80 with MCC 0.61 on validation data. In addition to this, we performed MERCI‐based motif analysis to find exclusive motifs/ patterns in druggable proteins. Finally, we proposed an ensemble‐based method combining best performing machine learning model with exclusive motifs and achieved AUC 0.92 with MCC 0.83 on independent validation dataset. Conclusion and Clinical Relevance: In order to serve the scientific community, web server and standalone package of “ThPPred” facilitating prediction and designing of druggable proteins is proposed ( https://webs.iiitd.edu.in/raghava/thppred/ ).

Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites were retained for further analysis. Differential methylation analysis using statistical tests identified 30,791 CpG sites as significantly altered in CRC samples, where p is less than 0.05. Univariate scoring enabled the selection of top ranking features, which were further refined using multiple feature selection algorithms, including Recursive Feature Elimination, Sequential Feature Selection, and SVC L1. Various machine learning models such as Logistic Regression, Support Vector Machines, Random Forest, and Multi layer Perceptron were trained and tested using independent validation datasets. The best performance was achieved with an MLP model trained on 25 features selected by RFE, reaching an AUROC of 0.89 and MCC of 0.78 on validation data. Additionally, a deep learning based convolutional neural network achieved an AUROC of 0.78. Functional annotation of the most predictive CpG sites identified several genes involved in key cellular processes, some of which were validated for differential expression in CRC using the GEPIA2 platform. Our study highlights the potential of cfDNA methylation markers combined with ML and DL models for noninvasive and accurate CRC detection, paving the way for clinically relevant diagnostic tools.

In 2013, our group developed IL4pred, a host-independent method for predicting interleukin-4 (IL-4) inducing peptides, which has been widely used by the scientific community. In this study, we present a second-generation method, IL4Pred2, which is a host-specific approach designed to predict IL-4 inducing peptides separately for human and mouse hosts. All models were trained, tested, and benchmarked on experimentally validated data obtained from the IEDB. We employed a wide range of state-of-the-art techniques for prediction, including similarity-based approaches, machine learning, deep learning methods, and large language models. Our best model achieved highest AUC 0.80 with MCC 0.45 for human and AUC 0.82 with MCC 0.50 for mouse on independent set of main datasets. All models were trained, test and optimized on training dataset. We validate our final model on an independent dataset which is not used in training or hyperparameter optimization of models. In this study, we developed models on three types of datasets called Main, Alternate1 and Alternate2 for predicting IL-4 inducing peptides. This abstract show performance of our models on Main dataset, performance of models on other datasets have been discussed in manuscript. One of the major objectives of this study is to facilitate research community in the area of immunotherapy and vaccine development. Thus, we developed, a web server and standalone software IL4pred2 for predicting, designing and scanning IL-4 inducing peptides in proteins ( https://webs.iiitd.edu.in/raghava/il4pred2/ and https://github.com/raghavagps/il4pred2 ).

Rheumatoid arthritis (RA) is an autoimmune disorder in which the immune system mounts an abnormal response to self-antigens, leading to chronic inflammation and joint damage. Therefore, identifying antigenic regions in a protein that trigger RA is crucial for developing protein-based therapeutics. In this study, we developed models for predicting RA-inducing peptides using a dataset comprising of 291 experimentally confirmed RA-inducing peptides and 165 RA non-inducing peptides. Our initial analysis revealed that certain residues, such as glycine, proline, and tyrosine, are significantly enriched in RA-inducing peptides. While alignment-based techniques like BLAST and MERCI offered high precision, they suffered from limited coverage. We developed machine/deep learning based prediction and obtained highest performance (AUC = 0.75) using XGboost on an independent dataset. We also developed prediction methods using large language models and achieved highest performance (AUC 0.72) using ProtBERT. Our ensemble model achieved highest performance (AUC = 0.80 & MCC = 0.45) on an independent dataset that combine XGBoost and MERCI-derived motifs. All models were rigorously evaluated on an independent dataset not used during training or testing of models. This study will be valuable for assessing the risk of proteins used in probiotics, genetically modified foods, and protein-based therapeutics. Our most effective approach has been implemented in RAIpred, a web server and standalone software tool for predicting and scanning RA-inducing peptides. ( https://webs.iiitd.edu.in/raghava/raipred/ ). Highlights: Rheumatoid arthritis (RA), an incurable chronic joint disorder with diverse systemic complications. An attempt to identify antigenic regions in a protein which trigger this severe disease. Utilizing sequence composition based features for developing models. Implementation of ML, DL and LLM based models for prediction of RA-inducing peptides. Development of webserver, standalone, pypi and GitHub package for users.

Nuclear Factor kappa B (NF-κB) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-κB-related diseases, we developed a computational approach to predict drugs capable of inhibiting NF-κB signaling pathways. In this study, we utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques. Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved an AUC of 0.75 on the validation dataset. All models were trained using five-fold cross-validation and rigorously evaluated on an independent validation dataset, which was not used during training or hyperparameter optimization. Finally, our best-performing model was employed to screen FDA-approved drugs for potential NF-κB inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the model’s predictive reliability. Our best- performing models have been integrated into a standalone software and web server, NfκBin, to enable the scientific community to perform high-throughput screening of Nf-κB inhibitors from chemical libraries. (https://webs.iiitd.edu.in/raghava/nfkbin/). Highlights 1) Inhibitors of Nf-κB can be used to manage disease like rheumatoid arthritis, asthma. 2) A novel tool NfκBin developed for screening Nf-κB inhibitors from chemical library. 3) Cutting-edge feature selection techniques used to identify the most relevant chemical descriptors. 4) Models were developed and validated on a robust dataset of inhibitors and non-inhibitors. 5) Integrated best-performing models into the NfκBin software and web server.

In the past, numerous proteins/peptides have been discovered, which have a wide range of therapeutic properties like anticancer, antimicrobial, antihypertensive. Only few hundreds of proteins are druggable (approved by US FDA), most of the proteins fails in clinical trials. In this study, an attempt had been made to understand properties of FDA approved proteins to develop models for predicting druggable proteins. Our main dataset 356 FDA approved proteins as positive dataset and equal number of randomly selected proteins as negative dataset. We used 80% data for training and 20% for independent validation, no protein in validation dataset have more than 40% similarity with any protein in training dataset. We deployed machine learning based models using a five-fold cross-validation and test on validation dataset. Our random forest-based model developed using SVC-L1 selected features obtained maximum performance AUC of 0.80 with MCC 0.61 on validation data. In addition to this, we performed MERCI-based motif analysis to find motifs in druggable proteins. Finally, we developed an ensemble-based method combining best performing machine learning model with motifs and achieved AUC 0.92 with MCC 0.83 on independent validation dataset. We developed a web server and standalone package ThPPred to facilitate scientific community in predicting and designing druggable proteins ( https://webs.iiitd.edu.in/raghava/thppred/ ). Highlights: Analysis of FDA approved or druggable proteins Discrimination of druggable and non-druggable proteins Machine learning based models for predicting druggable molecules Identification of motifs in druggable proteins A web server for providing service to community

Journal of Computational Biology

Interleukins are a distinctive class of molecules exhibiting various immune signaling functions. Immunoregulatory cytokine, Interleukin 13 (IL13), is primarily synthesized by activated T-helper 2 cells, mast cells, and basophils. IL13, is known to stimulate many allergic and autoimmune diseases, such as asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition to such disorders, IL13 also leads to carcinogenesis by inhibiting tumor immunosurveillance. Due to its role in various diseases, predicting IL13-inducing peptides or regions in a protein is vital to designing safe protein vaccines and therapeutics. IL13pred is an in silico tool which aids in identifying, predicting, and designing IL13-inducing peptides. The IL13pred web server and standalone package is easily accessible at (https://webs.iiitd.edu.in/raghava/il13pred/). Key words Interleukin 13 Inducing peptides prediction Vaccine development Immunotherapy