MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets.

Bacteria can exceptionally evolve and develop pathogenic features making it crucial to determine novel pathogenic proteins for specific therapeutic interventions. Therefore, we have developed a machine-learning tool that predicts and functionally classifies pathogenic proteins into their respective pathogenic classes. Through construction of pathogenic proteins database and optimization of ML algorithms, Support Vector Machine was selected for the model construction. The developed SVM classifier yielded an accuracy of 81.72% on the blind-dataset and classified the proteins into three classes: Non-pathogenic proteins (Class-1), Antibiotic Resistance Proteins and Toxins (Class-2), and Secretory System Associated and capsular proteins (Class-3). The classifier provided an accuracy of 79% on real dataset-1, and 72% on real dataset-2. Based on the probability of prediction, users can estimate the pathogenicity and annotation of proteins under scrutiny. Tool will provide accurate prediction of pathogenic proteins in genomic and metagenomic datasets providing leads for experimental validations. Tool is available at: http://metagenomics.iiserb.ac.in/mp4 .

Molib: A machine learning based classification tool for the prediction of biofilm inhibitory molecules.

Identification of biofilm inhibitory small molecules appears promising for therapeutic intervention against biofilm-forming bacteria. However, the experimental identification of such molecules is a time-consuming task, and thus, the computational approaches emerge as promising alternatives. We developed the 'Molib' tool to predict the biofilm inhibitory activity of small molecules. We curated a training dataset of biofilm inhibitory molecules, and the structural and chemical features were used for feature selection, followed by algorithms optimization and building of machine learning-based classification models. On five-fold cross validation, Random Forest-based descriptor, fingerprint and hybrid classification models showed accuracies of 0.93, 0.88 and 0.90, respectively. The performances of all models were evaluated on two different validation datasets including biofilm inhibitory and non-inhibitory molecules, attesting to its accuracy (≥ 0.90). The Molib web server would serve as a highly useful and reliable tool for the prediction of biofilm inhibitory activity of small molecules.

Mechanistic and structural insight into promiscuity based metabolism of cardiac drug digoxin by gut microbial enzyme.

The recent advances in microbiome studies have revealed the role of gut microbiota in altering the pharmacological properties of oral drugs, which contributes to patient-response variation and undesired effect of the drug molecule. These studies are essential to guide us for achieving the desired efficacy and pharmacological activity of the existing drug molecule or for discovering novel and more effective therapeutics. However, one of the main limitations is the lack of atomistic details on the binding and metabolism of these drug molecules by gut-microbial enzymes. Therefore, in this study, for a well-known and important FDA-approved cardiac glycoside drug, digoxin, we report the atomistic details and energy economics for its binding and metabolism by the Cgr2 protein of Eggerthella lenta DSM 2243. It was observed that the binding pocket of digoxin to Cgr2 primarily involved the negatively charged polar amino acids and a few non-polar hydrophobic residues. The drug digoxin was found to bind Cgr2 at the same binding site as that of fumarate, which is the proposed natural substrate. However, digoxin showed a much lower binding energy (17.75 ± 2 Kcal mol-1 ) than the binding energy (42.17 ± 2 Kcal mol-1 ) of fumarate. This study provides mechanistic insights into the structural and promiscuity-based metabolism of widely used cardiac drug digoxin and presents a methodology, which could be useful to confirm the promiscuity-based metabolism of other orally administrated drugs by gut microbial enzymes and also help in designing strategies for improving the efficacy of the drugs.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches. In this study, we developed SynerGNet, a graph neural network model designed to accurately predict the synergistic effect of drug pairs against cancer cell lines. SynerGNet utilizes cancer-specific featured graphs created by integrating heterogeneous biological features into the human protein-protein interaction network, followed by a reduction process to enhance topological diversity. Leveraging synergy data provided by AZ-DREAM Challenges, the model yields a balanced accuracy of 0.68, significantly outperforming traditional machine learning. Encouragingly, augmenting the training data with carefully constructed synthetic instances improved the balanced accuracy of SynerGNet to 0.73. Finally, the results of an independent validation conducted against DrugCombDB demonstrated that it exhibits a strong performance when applied to unseen data. SynerGNet shows a great potential in detecting drug synergy, positioning itself as a valuable tool that could contribute to the advancement of combination therapy for cancer treatment.

GutBug: A Tool for Prediction of Human Gut Bacteria Mediated Biotransformation of Biotic and Xenobiotic Molecules Using Machine Learning.

Dietary components and bioactive molecules present in functional foods and nutraceuticals provide various beneficial effects including modulation of host gut microbiome. These metabolites along with orally administered drugs can be potentially bio-transformed by gut microbiome, which can alter their bioavailability and intended biological or pharmacological activity resulting in individual or population-specific variation in drug and dietary responses. Experimental determination of microbiome-mediated metabolism of orally ingested molecules is difficult due to the enormous diversity and complexity of the gut microbiome. To address this problem, we developed "GutBug", a web-based resource that predicts all possible bacterial metabolic enzymes that can potentially biotransform xenobiotics and biotic molecules using a combination of machine learning, neural networks and chemoinformatic methods. Using 3,457 enzyme substrates for training and a curated database of 363,872 enzymes from ∼700 gut bacterial strains, GutBug can predict complete EC number of the bacterial enzymes involved in a biotransformation reaction of the given molecule along with the reaction centres with accuracies between 0.78 and 0.97 across different reaction classes. Validation of GutBug's performance using 27 molecules known to be biotransformed by human gut bacteria, including complex polysaccharides, flavonoids, and oral drugs further attests to GutBug's accuracy and utility. Thus, GutBug enhances our understanding of various metabolite-gut bacterial interactions and their resultant effects on the human host health across populations, which will find enormous applications in diet design and intervention, identification and administration of new prebiotics, development of nutraceutical products, and improvements in drug designing. GutBug is available at https://metabiosys.iiserb.ac.in/gutbug.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8,798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that Random Forest and Gradient Boosting Trees models trained on the augmented data achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.

Unlocking the Potential of Kinase Targets in Cancer: Insights from CancerOmicsNet, an AI-Driven Approach to Drug Response Prediction in Cancer.

Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases. We recently developed CancerOmicsNet, an artificial intelligence model employing graph-based algorithms to predict the cancer cell response to treatment with kinase inhibitors. The performance of this approach has been evaluated in large-scale benchmarking calculations, followed by the experimental validation of selected predictions against several cancer types. To shed light on the decision-making process of CancerOmicsNet and to better understand the role of each kinase in the model, we employed a customized saliency map with adjustable channel weights. The saliency map, functioning as an explainable AI tool, allows for the analysis of input contributions to the output of a trained deep-learning model and facilitates the identification of essential kinases involved in tumor progression. The comprehensive survey of biomedical literature for essential kinases selected by CancerOmicsNet demonstrated that it could help pinpoint potential druggable targets for further investigation in diverse cancer types.

Pyridine Borane as Alternative Reducing Agent to Sodium Cyanoborohydride for the PEGylation of L-asparaginase.

PEGylation is a reductive alkylation of a protein N-terminal/α-amine of protein with mPEG chain by reducing agent. To obtain quantitative and site-specific PEGylation, sodium cyanoborohydride is commonly used as a reducing agent. The reduction process of sodium cyanoborohydride produces highly poisonous hydrogen cyanide, which may render the final product toxic. Herein, we have studied various reducing agents such as dimethylamine borane, triethylamine borane, trimethylamine borane, pyridine borane, morpholine borane, 2-picoline borane, and 5-ethyl-2-methyl-pyridine borane were tested as alternatives to sodium cyanoborohydride for the PEGylation of L-asparaginase. The characterization of reacted pegaspargase was carried out by SDS-PAGE, Western blotting, SEC-HPLC, RP-HPLC, SEC-MALS, CD, enzyme activity, and cell proliferation assays using with lymphoblast cells and MTS/PMS as substrate. Pyridine borane was determined to be the best acceptable reducing agent for PEGylation in terms of purity and activity. As a result, instead of sodium cyanoborohydride, pyridine borane can be employed.

Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design.

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.

GraphSite: Ligand Binding Site Classification with Deep Graph Learning.

The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effectively detect and classify ligand binding sites in proteins is of paramount importance to modern structure-based drug discovery. These complex and non-trivial tasks require sophisticated algorithms from the field of artificial intelligence to achieve a high prediction accuracy. In this communication, we describe GraphSite, a deep learning-based method utilizing a graph representation of local protein structures and a state-of-the-art graph neural network to classify ligand binding sites. Using neural weighted message passing layers to effectively capture the structural, physicochemical, and evolutionary characteristics of binding pockets mitigates model overfitting and improves the classification accuracy. Indeed, comprehensive cross-validation benchmarks against a large dataset of binding pockets belonging to 14 diverse functional classes demonstrate that GraphSite yields the class-weighted F1-score of 81.7%, outperforming other approaches such as molecular docking and binding site matching. Further, it also generalizes well to unseen data with the F1-score of 70.7%, which is the expected performance in real-world applications. We also discuss new directions to improve and extend GraphSite in the future.

Discovery of a tetracyclic indole alkaloid that postpones fibrillation of hen egg white lysozyme protein.

Protein aggregation, such as amyloid fibril formation, is molecular hallmark of many neurodegenerative disorders including Alzheimer's, Parkinson's, and Prion disease. Indole alkaloids are well-known as the compounds having the ability to inhibit protein fibrillation. In this study, we experimentally and computationally have investigated the anti-amyloid property of a derivative of a synthesized tetracyclic indole alkaloid (TCIA), possessing capable functional groups. The fibrillation reaction of Hen White Egg Lysozyme (HEWL) was performed in absence and presence of the indole alkaloid. For quantitative analysis, we used Thioflovin T binding assay which showed ~50% reduction in fibril formation in the presence of 20 µM TCIA. Using TEM imaging, we observed a significant morphological change in our model protein in the presence of TCIA. In addition, we exploited FT-IR assay by which Amide I peak's shifting toward lower wavenumber was clearly observed. Using Molecular Docking, the interaction of the inhibitor (TCIA) with the protein's amyloidogenic region was modeled. Also, different biophysical parameters were calculated by Molecular Dynamics (MD) simulation. Various biochemical assays, conformational change, and hydrophobicity exposure of the protein during amyloid formation indicated that the compound assists HEWL to keep its native structure via destabilizing ß-sheet structure.

Preparation and characterization of microencapsulated DwPT trivalent vaccine using water soluble chitosan and its in-vitro and in-vivo immunological properties.

The paper explained the microencapsulation of three different antigenic materials viz. Diphtheria toxoid (DT), whole cell pertussis antigens (PT and FHA) and tetanus toxoid (TT) by coacervation method using water soluble chitosan as a polymer crosslinked by vanillin/TPP co-crosslinkers for the development of oral trivalent DwPT vaccine. Instrumental characterization of chitosan microspheres suggested specific interaction with vanillin/TPP, higher thermal stability, amorphous nature, spherical morphology with size less than 2µm along with positive charge density offering mucoadhesive properties. Furthermore, PT and FHA showed higher encapsulation up to 94% followed by TT and DT. Cumulative release rate of DT was (68.47%), TT (73.67%), PT (43%) and FHA (53%). Release kinetics interpreted using DD solver program, indicated protein release followed first order kinetics and obeyed Korsmeyer-peppas model, stating fickian diffusion relates to diffusion, erosion and controlled release rate of the encapsulated toxoids. Application of formulations on caco-2 cell line showed negligible cytotoxic effect and efficient uptake of FITC labelled microspheres. The obtained in-vivo results suggests that the final trivalent DwPT formulation were having successful elicitation of both systemic (IgG) and mucosal (sIgA) immune response in balb/c mice. Overall studies indicated that DwPT formulation could be a suitable alternative to available injectable DaPT vaccine.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

Synthesis and evaluation of oxindoles as promising inhibitors of the immunosuppressive enzyme indoleamine 2,3-dioxygenase 1.

Indoleamine 2,3-dioxygenase 1 (IDO1) is considered as an important therapeutic target for the treatment of cancer, chronic infections and other diseases that are associated with immune suppression. Recent developments in understanding the catalytic mechanism of the IDO1 enzyme revealed that conversion of l-tryptophan (l-Trp) to N-formylkynurenine proceeded through an epoxide intermediate state. Accordingly, we synthesized a series of 3-substituted oxindoles from l-Trp, tryptamine and isatin. Compounds with C3-substituted oxindole moieties showed moderate inhibitory activity against the purified human IDO1 enzyme. Their optimization led to the identification of potent compounds, 6, 22, 23 and 25 (IC50 = 0.19 to 0.62 µM), which are competitive inhibitors of IDO1 with respect to l-Trp. These potent compounds also showed IDO1 inhibition potencies in the low-micromolar range (IC50 = 0.33-0.49 µM) in MDA-MB-231 cells. The cytotoxicity of these potent compounds was trivial in different model cancer (MDA-MB-231, A549 and HeLa) cells and macrophage (J774A.1) cells. Stronger selectivity for the IDO1 enzyme (124 to 210-fold) over the tryptophan 2,3-dioxygenase (TDO) enzyme was also observed for these compounds. These results suggest that the oxindole moiety of the compounds could mimic the epoxide intermediate state of l-Trp. Therefore, the structural simplicity and low-micromolar inhibition potencies of these 3-substituted oxindoles make them quite attractive for further investigation of IDO1 function and immunotherapeutic applications.

Tartrate/tripolyphosphate as co-crosslinker for water soluble chitosan used in protein antigens encapsulation.

In drug delivery research, several toxic chemical crosslinkers and non-toxic ionic crosslinkers have been exploited for the synthesis of microparticles from acetic acid soluble chitosan. This paper hypothesized the implementation of sodium potassium tartrate (SPT) as an alternative crosslinker for sodium tripolyphosphate (TPP) and SPT/TPP co-crosslinkers for synthesis of the microparticles using water soluble chitosan (WSC) for encapsulation of Bovine serum albumin (BSA) as a model protein, and Tetanus toxoid (TT) as a model vaccine. The crosslinking was confirmed by FT-IR, SEM with EDS. The XRD entailed molecular dispersion of proteins and thermal analysis confirmed the higher stability of STP/TPP co-crosslinked formulations. The resultant microparticles were exhibiting crosslinking degree (52-67%), entrapment efficiency (72-80%), particle size (0.3-1.7µm), zeta potential (+24 to 46mV) and mucoadhesion (41-68%). The superiority of SPT over TPP was confirmed by higher crosslinking degree and entrapment efficiency. However, co-crosslinking were advantageous in higher regression values for Langmuir adsorption isotherm, slower swelling tendency and extended 30days controlled in-vitro release study. TT release obeyed the Quasi-Fickian diffusion mechanism for single and cocrosslinked formulations. Overall, in crosslinking of chitosan as biological macromolecules, STP/TPP may be alternative for single ionic crosslinked formulations for protein antigen delivery.

Fabrication of chitosan microspheres using vanillin/TPP dual crosslinkers for protein antigens encapsulation.

Microspheres were prepared from water soluble chitosan using dual vanillin/TPP crosslinkers. Placebo (C1), Bovine serum albumin (BSA) (C2), monovalent tetanus toxoid (TT) (C3) and divalent tetanus (TT) and diphtheria toxoids (DT) (C4) encapsulated microspheres were studied in terms of size (1-4 µm), encapsulation efficiency (75-80%), swelling and mucoadhesion (56-68%). FT-IR, TGA, XRD and SEM characterization of microspheres suggested specific interaction, more thermal stability, amorphous nature and rough surfaces of encapsulated microspheres. EDS confirmed the co-crosslinking and ninhydrin tests were showing higher crosslinking density. Zeta potential was 47.7 to 66.2 +mV indicating the potential stability of the colloidal system. Equilibrium adsorption isotherms described encapsulated microspheres followed the Langmuir isotherm model, suggesting monolayer adsorption of the mucin on microspheres. In-vitro release studies up to four weeks indicated zero order kinetics and obeyed swelling-controlled super case II transport release mechanism. Thus, the present study could be helpful in developing the multivalent oral vaccine.

Physicochemical and thermal characterization of nonedible oilseed residual waste as sustainable solid biofuel.

The present study aims to investigate the potential of nonedible oilseed Jatropha (Jatropha curcas) and Karanja (Pongamia pinnata) defatted residual biomasses (whole seed, kernel, and hull), as solid biofuel. These biomasses showed good carbon contents (39.8-44.5%), whereas, fewer amounts were observed for sulfur (0.15-0.90%), chlorine (0.64-1.76%), nitrogen (0.9-7.2%) and ash contents (4.0-8.7%). Their volatile matter (60.23-81.6%) and calorific values (17.68-19.98 MJ/kg) were found to be comparable to coal. FT-IR and chemical analyses supported the presence of good amount of cellulose, hemicellulose and lower lignin. The pellets prepared without any additional binder, showed better compaction ratio, bulk density and compressive strength. XRF analysis carried out for determination of slagging-fouling indices, suggested their ash deposition tendencies in boilers, which can be overcome significantly with the optimization of the blower operations and control of ash depositions. Thus, overall various chemical, physical properties, thermal decomposition, surface morphological studies and their high biofuel reactivity indicated that residual biomasses of Jatropha and Karanja seeds have high potential to be utilized as a solid biofuel.