Anand Singh Rathore

The post-genomic era presented a series of challenges: initially, the annotation of protein structure and function; subsequently, the identification of proteins as drug and vaccine candidates; and currently, the design of protein-based therapeutics. Unlike traditional small-molecule drugs, protein therapeutics offer higher specificity, reduced off-target effects, and minimal side effects, making them increasingly attractive. This growing appeal is reflected in the rising proportion of biologics, including protein-based drugs, among recent FDA approvals. This chapter focuses on the computer-aided design of protein-based drugs, with a particular emphasis on machine learning (ML)-based methods, which have emerged as powerful techniques over the last two decades. We begin by compiling and presenting major databases and ML methodologies commonly employed in protein therapeutics. The first section details two recent methods developed for predicting the antibacterial activity of peptides. The second section addresses the safety assessment of therapeutic peptides, describing methods for predicting their cytotoxicity and hemotoxicity. The final section elucidates techniques for predicting the melting temperature of proteins and identifying anti-freezing proteins. This chapter is therefore poised to serve as a valuable resource for researchers and practitioners in drug discovery, computational biology, and bioinformatics, providing essential insights into the latest advancements in protein-based drug design.

Genetically modified (GM) crops play a crucial role in addressing pressing global issues, including public health, sustainable agriculture, and food security. To bring together a variety of computational resources related to GM crops, this review presents CoreGMC, a curated web‐based bioinformatics platform. Here, resources are organized systematically into four principal categories: crop improvement, nutritional enhancement, therapeutic applications, and safety assessment. The present study begins with a concise overview of GM crops, their significance, history and current global status. A vital component of developing GM crops with increased resilience and productivity is identifying genes essential for conferring resistance to biotic and abiotic challenges, explicitly highlighted in the “Crop Improvement” section. Following that, the “Nutritional Value” section describes databases and computational tools designed to forecast and facilitate the incorporation of genetic alterations aimed at enhancing the nutritional value of crops. To further illustrate the potential of modified crops in the biomedical sector, the “Therapeutic Applications” section provides information on predicting therapeutic proteins produced by these crops. Finally, the computational tools developed for forecasting a wide range of potential toxicities in products derived from GM crops are included in our “Safety Assessment” section. This review aims to compile and present all computing tools that directly or indirectly support the development of GM crops. The CoreGMC platform is accessible at https://webs.iiitd.edu.in/raghava/coregmc/ .

Ion channels are central to regulating neuronal communication, cardiac rhythm, and muscle contraction. Their modulation can induce therapeutic benefits but may also lead to adverse or toxic effects. This study presents IonNTxPred, a protein language model (PLM)‐based method for predicting protein ion channel modulators, including channel‐specific (such as sodium, potassium, calcium, and others) and moonlighting proteins capable of modulating multiple ion channels. We train, test, and evaluate our models on the largest dataset of non‐redundant ion channel modulating proteins, where no two proteins have more than 40% sequence identity. Composition analysis revealed that residues Cys, Gly, and Trp are highly prevalent, whereas Ala, Glu, Leu, Gln, and Val are scarce in ion channel modulating proteins, with Cys identified as a key discriminative residue. We explored both alignment‐based (BLAST, MERCI) and alignment‐free (machine learning, deep learning, and PLM‐based) approaches. Among these, our evolutionary information‐based PLM (ESM2‐t33) achieved the best performance, with an AUROC of 0.97 across ion channels, which further improved to 0.98 when integrated with BLAST output. The proposed method outperformed existing approaches on independent datasets. We used IonNTxPred to screen FDA‐approved and organismal proteins to identify candidates with ion channel modulating potential, supporting drug repurposing, discovery of new therapeutic proteins, and safety assessment of existing biologics. We implemented these models in a user‐friendly web server, IonNTxPred, which facilitates the design and discovery of ion channel modulating protein‐based drugs and supports the biosafety evaluation of therapeutic proteins through neurotoxin screening ( https://webs.iiitd.edu.in/raghava/ionntxpred/ ).

Ion channels are central to regulating neuronal communication, cardiac rhythm, and muscle contraction. Their modulation can induce therapeutic benefits but may also lead to adverse or toxic effects. This study presents IonNTXpred, a protein language model (PLM)-based method for predicting protein ion channel modulators, including channel-specific (Na□, K□, Ca²□, and others) and moonlighting proteins capable of modulating multiple ion channels. We train, test, and evaluate our models on the largest dataset of non-redundant ion channel–modulating proteins, where no two proteins have more than 40% sequence identity. Composition analysis revealed that residues Cys, Gly, and Trp are highly prevalent, whereas Ala, Glu, Leu, Gln, and Val are scarce in ion channel–modulating proteins, with Cys identified as a key discriminative residue. We explored both alignment-based (BLAST, MERCI) and alignment-free (machine learning, deep learning, and PLM-based) approaches. Among these, our evolutionary information–based PLM (ESM2-t33) achieved the best performance, with an AUROC of 0.97 across ion channels, which further improved to 0.98 when integrated with BLAST output. The proposed method outperformed existing approaches on independent datasets. We used IonNTXpred to screen FDA-approved and organismal proteins to identify candidates with ion channel–modulating potential, supporting drug repurposing, discovery of new therapeutic proteins, and safety assessment of existing biologics. We implemented these models in a user-friendly web server, IonNTXpred, which facilitates the design and discovery of ion channel–modulating protein-based drugs and supports the biosafety evaluation of therapeutic proteins through neurotoxin screening ( https://webs.iiitd.edu.in/raghava/ionntxpred/ ). Highlights: Prediction, design, and large-scale screening of ion channel–modulating proteins. Identifies neurotoxins to support safety evaluation of therapeutic proteins. Evolutionary information–based protein language model ESM2-t33 for prediction. Predicts moonlighting proteins capable of modulating multiple ion channels. Provides standalone software, a web server, and a PyPI package for user accessibility.

The accurate prediction of neurotoxicity in peptides and proteins is essential for the safety evaluation of therapeutic proteins and genetically modified (GM) organisms. Existing tools, including our earlier method NTxPred, typically use a single predictive model for both neurotoxic peptides and proteins, despite their structural and functional differences. This lack of specialization may lead to suboptimal performance and limited generalizability. To address this, we developed NTxPred2, distinct, specialized models for predicting neurotoxic peptides and neurotoxins (proteins). Our curated datasets include 877 neurotoxic and 877 non‐toxic peptides, and 775 neurotoxic and 775 non‐toxic proteins. Certain residues, like cysteine, are prevalent in both but in different magnitudes. Using composition and binary profiles, our machine‐learning models achieved an area under the curve (AUC) of 0.97 for peptides and 0.85 for proteins, improving to 0.89 with evolutionary information. Models using protein embeddings reached 0.96 AUC for peptides and 0.94 for proteins, while protein language models achieved 0.98 (esm2‐t30) and 0.91 (esm2‐t6). All models were validated via five‐fold cross‐validation, and the final models were evaluated on an independent dataset. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets that are not used for training. Our neurotoxicity prediction models will aid in the safety assessment of GM foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2 for predicting and scanning neurotoxins (https://webs.iiitd.edu.in/raghava/ntxpred2/, https://github.com/raghavagps/ntxpred2/).

Hemolytik 2.0 ( http://webs.iiitd.edu.in/raghava/hemolytik2/ ) is a comprehensive, manually curated database that provides experimentally validated information on both hemolytic and non-hemolytic peptides. Data were meticulously extracted from peer-reviewed publications and established peptide repositories, including the Antimicrobial Peptide Database (APD), UniProt, the Collection of Anti-Microbial Peptides (CAMP-R4), and the Dragon Antimicrobial Peptide Database (DAMPD). This updated version of the original Hemolytik resource encompasses 13,215 entries, representing approximately 8,700 unique peptides. Each entry in Hemolytik 2.0 offers detailed annotations, including peptide name, amino acid sequence, biological source and origin, functional characterization, terminal modifications, stereochemistry, structural classification (linear or cyclic), and experimentally determined hemolytic activity. In addition, the database provides molecular representations of peptides in SMILES (Simplified Molecular Input Line Entry System) format, alongside predicted tertiary structures and annotated secondary structural states. Additionally, a RESTful API has been integrated into the Hemolytik 2.0 repository to enable programmatic access and automated retrieval of peptide data. Hemolytik 2.0 serves as a valuable resource for the scientific community, particularly for researchers involved in the design and development of therapeutic peptides, by facilitating the identification and optimization of peptide candidates with minimal hemolytic potential and enhanced safety profiles.

Several formats, including FASTA, PIR, GenBank, EMBL, and GCG, have been developed for representing protein sequences composed of natural amino acids. Among these, FASTA remains the most widely used due to its simplicity and human readability. However, FASTA lacks the capability to represent chemically modified or non-natural residues, as well as structural annotations and mutations in protein variants. To address some of these limitations, the PEFF format was recently introduced as an extension of FASTA. Additionally, formats such as HELM and BILN have been proposed to represent amino acids and their modifications at the atomic level. Despite their advancements, these formats have not achieved widespread adoption within the bioinformatics community due to their complexity. To complement existing formats and overcome current challenges, we propose a new format called MAP (Modification and Annotation in Proteins), which enables comprehensive annotation of protein sequences. MAP introduces meta tags in the header for protein-level annotations and inline tags within the sequence for residue-level modifications. In this format, standard one-letter amino acid codes are augmented with curly-brace tags to denote various modifications, including phosphorylation, acetylation, non-natural residues, cyclization, and other residue-specific features. The header metadata also captures information such as organism, function, and sequence variants. We describe the structure, objectives, and capabilities of the MAP format and demonstrate its application in bioinformatics, particularly in the domain of protein therapeutics. To facilitate community adoption, we are developing a comprehensive suite of MAP-format resources, including a detailed manual, annotated datasets, and conversion tools, available at http://webs.iiitd.edu.in/raghava/maprepo/.

Most of the existing methods including our NTxPred, use a single model to predict both neurotoxic peptides and neurotoxic proteins (neurotoxins). In this study, we developed distinct models for predicting neurotoxic peptides and neurotoxins. Our peptide dataset consists of 877 neurotoxic and 877 non-neurotoxic peptides, while our protein dataset includes 775 neurotoxins and 775 non-neurotoxins. Preliminary analysis reveals that certain residues, such as cysteine, are more prevalent in both neurotoxic peptides and proteins, though their abundance differs in magnitude. First, we developed machine learning models using composition and binary profiles, achieving a maximum AUC of 0.97 for peptides and 0.85 for proteins. The performance for proteins improved from an AUC of 0.85 to 0.89 when evolutionary information was incorporated. Next, we built machine learning models using embeddings from protein language models, attaining an AUC of 0.96 for peptides and 0.94 for proteins. We also developed protein language models and achieved an AUC of 0.98 for peptides using esm2-t30 and 0.91 for proteins using esm2-t6. All models were trained and tested using 5-fold cross-validation, and final models were evaluated on an independent dataset not used in training. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets. Our neurotoxicity prediction models will aid in the safety assessment of genetically modified foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2, for predicting and scanning neurotoxins ( https://webs.iiitd.edu.in/raghava/ntxpred2/ , https://github.com/raghavagps/ntxpred2/ ). Highlights: Development of models for predicting neurotoxic peptides and neurotoxins ML Models Using LLM Embeddings for Neurotoxin Prediction Protein language models esm2-t30 for predicting neurotoxic peptides Important for risk assessment of genetically modified foods/proteins NTxPred2 is an updated version of NTxPred for predicting neurotoxicity

Several peptide-based drugs fail in clinical trials due to their toxicity or hemolytic activity against red blood cells (RBCs). Existing methods predict hemolytic peptides but not the concentration (HC50) required to lyse 50% of RBCs. In this study, we developed a classification model and regression model to identify and quantify the hemolytic activity of peptides. Our models were trained and validated on 1924 peptides with experimentally determined HC50 against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Our classification models achieved a maximum AUC of 0.909 using a hybrid model of ESM-2 and a motif-based approach. Regression models using compositional features achieved R of 0.739 with R² of 0.543. Our models outperform existing methods and are implemented in the web-based platform HemoPI2 and standalone software for designing hemolytic peptides with desired HC50 values (http://webs.iiitd.edu.in/raghava/hemopi2/). Highlights Developed classification and regression models to predict hemolytic activity and HC50 values of peptides. A hybrid model combining machine learning and motif prediction excels in accuracy. Benchmarking of the existing classification methods on independent datasets. Web server, standalone software, and pip package for hemolytic activity prediction of peptides/proteins.