Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

The elucidation of protein structure and function plays a pivotal role in understanding biological processes and facilitating drug discovery. With the exponential growth of protein sequence data, machine learning techniques have emerged as powerful tools for predicting protein characteristics from sequences alone. This review provides a comprehensive overview of the importance and application of machine learning in inferring protein structure and function.

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.

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.

A Global Health Reciprocal Innovation grant programme: 5-year review with lessons learnt.

Unilateral approaches to global health innovations can be transformed into cocreative, uniquely collaborative relationships between low-income and middle-income countries (LMICs) and high-income countries (HIC), constituted as 'reciprocal innovation' (RI). Since 2018, the Indiana Clinical and Translational Sciences Institute (CTSI) and Indiana University (IU) Center for Global Health Equity have led a grants programme sculpted from the core elements of RI, a concept informed by a 30-year partnership started between IU (Indiana) and Moi University (Kenya), which leverages knowledge sharing, transformational learning and translational innovations to address shared health challenges. In this paper, we describe the evolution and implementation of an RI grants programme, as well as the challenges faced.

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.

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.

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.

DEIA is essential to advance the goals of translational science: Perspectives from NCATS.

The National Center for Advancing Translational Science (NCATS) seeks to improve upon the translational process to advance research and treatment across all diseases and conditions and bring these interventions to all who need them. Addressing the racial/ethnic health disparities and health inequities that persist in screening, diagnosis, treatment, and health outcomes (e.g.

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.

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.

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.

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.

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.

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.

Children and Adults With Rare Diseases Need Innovative Medical Devices.

Rare diseases (RD) affect approximately 30 million Americans, half of whom are children. This study is the first to comprehensively evaluate their medical device needs via a survey of physicians. The study sought to identify and document the presumed unmet diagnostic and therapeutic device needs for RD management; clarify the magnitude of the potential unmet need; and generate meaningful data to inform medical device stakeholders.

Female Sex and Gender in Lung/Sleep Health and Disease. Increased Understanding of Basic Biological, Pathophysiological, and Behavioral Mechanisms Leading to Better Health for Female Patients with Lung Disease.

Female sex/gender is an undercharacterized variable in studies related to lung development and disease. Notwithstanding, many aspects of lung and sleep biology and pathobiology are impacted by female sex and female reproductive transitions. These may manifest as differential gene expression or peculiar organ development.

Creating a multi-center rare disease consortium - the Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR).

 Eosinophilic gastrointestinal disorders (EGIDs) affect various segments of the gastrointestinal tract. Since these disorders are rare, collaboration is essential to enroll subjects in clinical studies and study the broader population. The Rare Diseases Clinical Research Network (RDCRN), a program of the National Center for Advancing Translational Sciences (NCATS), funded the Consortium of Eosinophilic Gastrointestinal Disease Researchers (CEGIR) in 2014 to advance the field of EGIDs.

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.

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.

Facilitating Clinical Studies in Rare Diseases.

In recent years, there have been many scientific advances and new collaborations for rare diseases research and, ultimately, the health of patients living with rare diseases. However, for too many rare diseases, there still is no effective treatment, and our understanding of the incidence, prevalence, and underlying etiology is incomplete. To facilitate the studies needed to answer the many open questions there is a great need for the active involvement of all stakeholders, most importantly of patient groups.

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.

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 -formylkynurenine proceeded through an epoxide intermediate state. Accordingly, we synthesized a series of 3-substituted oxindoles from l-Trp, tryptamine and isatin.