Molecular Discovery Research Articles

Emerging Perspectives of YAP/TAZ in Human Skin Epidermal and Dermal Aging.

Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are key downstream effectors of the Hippo signaling pathway, which plays a central role in tissue homeostasis, organ development, and regeneration. While the dysregulation of YAP/TAZ has been linked to various human diseases, their involvement in the aging of human skin has only recently begun to manifest. In the skin, the YAP/TAZ effectors emerge as central regulators in maintaining homeostasis of epidermal stem cells and dermal extracellular matrix, and thus intimately linked to skin aging processes. This review underscores recent molecular breakthroughs highlighting how age-related decline of YAP/TAZ activity impacts human epidermal and dermal aging. Gaining insight into the evolving roles of YAP/TAZ in human skin aging presents a promising avenue for the development of innovative therapeutic approaches aimed at enhancing skin health and addressing age-related skin conditions.

Transient Structural Dynamics of Glycogen Phosphorylase from Nonequilibrium Hydrogen/Deuterium-Exchange Mass Spectrometry.

It remains a major challenge to ascertain the specific structurally dynamic changes that underpin protein functional switching. There is a growing need in molecular biology and drug discovery to complement structural models with the ability to determine the dynamic structural changes that occur as these proteins are regulated and function. The archetypal allosteric enzyme glycogen phosphorylase is a clinical target of great interest to treat type II diabetes and metastatic cancers. Here, we developed a time-resolved nonequilibrium millisecond hydrogen/deuterium-exchange mass spectrometry (HDX-MS) approach capable of precisely locating dynamic structural changes during allosteric activation and inhibition of glycogen phosphorylase. We resolved obligate transient changes in the localized structure that are absent when directly comparing active/inactive states of the enzyme and show that they are common to allosteric activation by AMP and inhibition by caffeine, operating at different sites. This indicates that opposing allosteric regulation by inhibitor and activator ligands is mediated by pathways that intersect with a common structurally dynamic motif. This mass spectrometry approach uniquely stands to discover local transient structural dynamics and could be used broadly to identify features that influence the structural transitions of proteins.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

GBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencing

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutio nary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms. However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).

Improved decision making with similarity based machine learning: applications in chemistry

Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, ‘the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy.

Schistosomiasis Drug Development: A Computational Approach

Schistosomiasis continues to be a significant global health concern, affecting millions of individuals worldwide. The limitations associated with the existing treatment, Praziquantel, highlight the urgent need for novel schistosomicides. The development of a structure-based drug with high efficacy offers a potential solution to the challenges posed by current treatments. Given the capital and time-intensive nature of traditional inhibitor identification methods, computational techniques have gained prominence as valuable tools in drug discovery. This study employed a comprehensive computational approach to identify potential drug candidates, laying the groundwork for future wet-lab experimentation. In this research, in silico molecular docking analysis assessed the binding affinity of various compounds to the Schistosoma japonicum tegument protein 4 (SGTP4). To facilitate this analysis, homology modeling generated a reliable 3D structure of SGTP4. This rigorous approach, encompassing homology modeling and subsequent validation, ensured the reliability and high quality of the SGTP4 protein model, providing a solid foundation for subsequent molecular docking and drug discovery investigations. The ligands examined included Praziquantel , Licochalcone, P96, Licarin, and Harmonine. Ligand preparations were conducted using LigPrep within the Schrodinger Maestro suite, and pharmacokinetic parameters were evaluated using the QikProp tool within the Schrodinger-2019-4 software suite. Subsequently, AutoDockTools version 4 facilitated the docking analysis of the prepared ligands. The docking results furnished valuable insights into the binding affinities and potential inhibitory activities of the assessed compounds against SGTP4. PZQ, Licochalcone, and P96 exhibited robust binding affinities and inhibitory potentials, characterized by their lower binding energies and Ki values. Licarin demonstrated moderate activity, whereas Harmonine exhibited comparatively weaker binding and inhibition. The amalgamated results of docking analysis and ADMET simulations establish a foundation for the selection of lead compounds for further experimental evaluation in the quest for effective anti-schistosomal drugs. Among the evaluated compounds, Licochalcone and Licarin emerge as promising candidates, characterized by favorable molecular properties, drug-like attributes, and bioavailability. While PZQ retains its importance as a reference, its limitations underscore the necessity of exploring alternative compounds. Furthermore, P96 exhibits potential, particularly with structural modifications, warranting further investigation. This study underscores the significance of computational methodologies in the initial stages of drug discovery. Nonetheless, it is essential to stress that wet-lab experimentation remains an indispensable step in drug development. The computational insights presented herein offer a compelling rationale for the wet-lab validation of the identified candidates. Such experiments can elucidate the mechanisms of action, evaluate safety profiles, and confirm efficacy against schistosomes. Therefore, we advocate for the integration of wet-lab experiments to complement computational approaches, presenting a synergistic strategy poised to address the deficiencies of PZQ and effectively combat schistosomiasis.

Revolutionizing Plant Virus Detection: A Review on Molecular Breakthroughs and their Implications

Revolutionizing Plant Virus Detection: A Review on Molecular Breakthroughs and their Implications

The Imageable Genome

Understanding human disease on a molecular level, and translating this understanding into targeted diagnostics and therapies are central tenets of molecular medicine1. Realizing this doctrine requires an efficient adaptation of molecular discoveries into the clinic. We present an approach to facilitate this process by describing the Imageable Genome, the part of the human genome whose expression can be assessed via molecular imaging. Using a deep learning-based hybrid human-AI pipeline, we bridge individual genes and their relevance in human diseases with specific molecular imaging methods. Cross-referencing the Imageable Genome with RNA-seq data from over 60,000 individuals reveals diagnostic, prognostic and predictive imageable genes for a wide variety of major human diseases. Having both the critical size and focus to be altered in its expression during the development and progression of any human disease, the Imageable Genome will generate new imaging tools that improve the understanding, diagnosis and management of human diseases.

Deep Generative Models in De Novo Drug Molecule Generation.

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in de novo generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.

Human Skin Aging and the Anti-Aging Properties of Retinol.

The skin is the most-extensive and -abundant tissue in the human body. Like many organs, as we age, human skin experiences gradual atrophy in both the epidermis and dermis. This can be primarily attributed to the diminishing population of epidermal stem cells and the reduction in collagen, which is the primary structural protein in the human body. The alterations occurring in the epidermis and dermis due to the aging process result in disruptions to the structure and functionality of the skin. This creates a microenvironment conducive to age-related skin conditions such as a compromised skin barrier, slowed wound healing, and the onset of skin cancer. This review emphasizes the recent molecular discoveries related to skin aging and evaluates preventive approaches, such as the use of topical retinoids. Topical retinoids have demonstrated promise in enhancing skin texture, diminishing fine lines, and augmenting the thickness of both the epidermal and dermal layers.

Identification of potent inhibitors for Leishmania donovani homoserine kinase: an integrated in silico and kinetic study

Leishmaniasis is caused by ∼20 species of Leishmania that affects millions in endemic areas. Available therapies are not sufficient to effectively control the disease, cause severe side effects and eventually lead to drug resistance, making the discovery of novel therapeutic molecules an immediate need. Molecular target-based drug discovery, where the target is a defined molecular gene, protein or a mechanism, is a rationale driven approach for novel therapeutics. Humans obtain the essential amino acid such as threonine from dietary sources, while Leishmania synthesize it de-novo. Enzymes of the threonine biosynthesis pathway, including the rate limiting Homoserine kinase (HSK) which converts L-homoserine into ortho-phospho homoserine are thus attractive targets for rationale driven therapy. The absence of HSK in humans and its presence in Leishmania donovani enhances the opportunity to exploit HSK as a molecular target for anti-leishmanials therapeutic development. In this study, we utilize structure-based high throughput drug discovery (SBDD), followed by biochemical validation and identified two potential inhibitors (RH00038 and S02587) from Maybridge chemical library that targets L. donovani HSK. These two inhibitors effectively induced the mortality of Leishmania donovani in both amastigote and promastigote stages, with one of them being specific to parasite and twice as effective as the standard therapeutic molecule.

Syn-Selective Difunctionalization of Bicyclobutanes Enabled by Photoredox-Mediated C-S σ-Bond Scission.

Given the importance of cyclic frameworks in molecular scaffolds and drug discovery, it is intriguing to precisely forge and manipulate ring systems in synthetic chemistry. In this field, the intermolecular synthesis of densely substituted cyclobutanes with precise diastereocontrol under simple reaction conditions remains a challenge. Herein, a photoredox strategy for the difunctionalization of bicyclo[1.1.0]butanes (BCBs) under high regio- and syn-selectivity is disclosed. C-S σ-bond cleavage of partially unsaturated sulfur-containing bifunctional reagents in an overall strain-release-driven process enables the thio-alkynylation, -alkenylation, and -allylation of BCBs under mild conditions and demonstrates the generality of this protocol. Mechanistic studies suggest that the intermediacy of cyclic distonic radical cations might be key for the efficient scission of C-S σ-bonds and the origin of diastereoselectivity.

Guided diffusion for inverse molecular design.

The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model. We demonstrate GaUDI's effectiveness in designing molecules for organic electronic applications by using single- and multiple-objective tasks applied to a generated dataset of 475,000 polycyclic aromatic systems. GaUDI shows improved conditional design, generating molecules with optimal properties and even going beyond the original distribution to suggest better molecules than those in the dataset. In addition to point-wise targets, GaUDI can also be guided toward open-ended targets (for example, a minimum or maximum) and in all cases achieves close to 100% validity of generated molecules.

Hepatorenal pathologies in TNF-transgenic mouse model of rheumatoid arthritis are alleviated by anti-TNF treatment

ObjectiveTo examine and quantify liver and kidney lesions and their response to anti-tumor necrosis factor (TNF) therapy in a TNF-Tg mouse model of rheumatoid arthritis (RA).MethodsFemale TNF-Tg (Tg3647) mice were used as the animal model for chronic RA. Ultrasound, immunofluorescence, histological staining, serology tests, and real-time RT-PCR were used to examine the pathological changes in the liver and kidney.ResultsTNF-Tg mice showed a significant decrease in the body weight and a dramatic increase in the volumes of the gallbladder, knee cavity, and popliteal lymph nodes. The liver and kidneys of TNF-Tg mice showed increased chronic inflammation and accumulation of immune cells and fibrosis, compared to wild-type (WT) mice. Moreover, upregulation of inflammatory factors and impaired normal function were observed in the liver and kidneys of TNF-Tg mice. Inflammatory infiltration and fibrosis of the liver and kidneys of female TNF-Tg mice were improved after anti-TNF treatment, and better treatment effects were achieved at 4.5-month-old mice when they were received 8 weeks of intervention.ConclusionsWe found that TNF drives the development of liver and kidney pathology in female TNF-Tg mice and that there are limitations to the loss of utility of anti-TNF for the prolonged treatment of RA-associated hepatic and renal injury. This study provides a reliable and clinically relevant animal model for further studies exploring the molecular mechanisms and drug discovery for hepatorenal pathologies in RA.

Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions.

Burgeoning developments in machine learning (ML) and its rapidly growing adaptations in chemistry are noteworthy. Motivated by the successful deployments of ML in the realm of molecular property prediction (MPP) and chemical reaction prediction (CRP), herein we highlight some of its most recent applications in predictive chemistry. We present a nonmathematical and concise overview of the progression of ML implementations, ranging from an ensemble-based random forest model to advanced graph neural network algorithms. Similarly, the prospects of various feature engineering and feature learning approaches that work in conjunction with ML models are described. Highly accurate predictions reported in MPP tasks (e.g., lipophilicity, solubility, distribution coefficient), using methods such as D-MPNN, MolCLR, SMILES-BERT, and MolBERT, offer promising avenues in molecular design and drug discovery. Whereas MPP pertains to a given molecule, ML applications in chemical reactions present a different level of challenge, primarily arising from the simultaneous involvement of multiple molecules and their diverse roles in a reaction setting. The reported RMSEs in MPP tasks range from 0.287 to 2.20, while those for yield predictions are well over 4.9 in the lower end, reaching thresholds of >10.0 in several examples. Our Review concludes with a set of persisting challenges in dealing with reaction data sets and an overall optimistic outlook on benefits of ML-driven workflows for various MPP as well as CRP tasks.

Chemical structure-aware molecular image representation learning.

Current methods of molecular image-based drug discovery face two major challenges: (1) work effectively in absence of labels, and (2) capture chemical structure from implicitly encoded images. Given that chemical structures are explicitly encoded by molecular graphs (such as nitrogen, benzene rings and double bonds), we leverage self-supervised contrastive learning to transfer chemical knowledge from graphs to images. Specifically, we propose a novel Contrastive Graph-Image Pre-training (CGIP) framework for molecular representation learning, which learns explicit information in graphs and implicit information in images from large-scale unlabeled molecules via carefully designed intra- and inter-modal contrastive learning. We evaluate the performance of CGIP on multiple experimental settings (molecular property prediction, cross-modal retrieval and distribution similarity), and the results show that CGIP can achieve state-of-the-art performance on all 12 benchmark datasets and demonstrate that CGIP transfers chemical knowledge in graphs to molecular images, enabling image encoder to perceive chemical structures in images. We hope this simple and effective framework will inspire people to think about the value of image for molecular representation learning.

Canonical Wnt signaling promotes HSC glycolysis and liver fibrosis through an LDH-A/HIF-1α transcriptional complex.

Aerobic glycolysis reprogramming occurs during HSC activation, but how it is initiated and sustained remains unknown. We investigated the mechanisms by which canonical Wnt signaling regulated HSC glycolysis and the therapeutic implication for liver fibrosis. Glycolysis was examined in HSC-LX2 cells upon manipulation of Wnt/β-catenin signaling. Nuclear translocation of lactate dehydrogenase A (LDH-A) and its interaction with hypoxia-inducible factor-1α (HIF-1α) were investigated using molecular simulation and site-directed mutation assays. The pharmacological relevance of molecular discoveries was intensified in primary cultures, rodent models, and human samples. HSC glycolysis was enhanced by Wnt3a but reduced by β-catenin inhibitor or small interfering RNA (siRNA). Wnt3a-induced rapid transactivation and high expression of LDH-A dependent on TCF4. Wnt/β-catenin signaling also stimulated LDH-A nuclear translocation through importin β2 interplay with a noncanonical nuclear location signal of LDH-A. Mechanically, LDH-A bound to HIF-1α and enhanced its stability by obstructing hydroxylation-mediated proteasome degradation, leading to increased transactivation of glycolytic genes. The Gly28 residue of LDH-A was identified to be responsible for the formation of the LDH-A/HIF-1α transcription complex and stabilization of HIF-1α. Furthermore, LDH-A-mediated glycolysis was required for HSC activation in the presence of Wnt3a. Results in vivo showed that HSC activation and liver fibrosis were alleviated by HSC-specific knockdown of LDH-A in mice. β-catenin inhibitor XAV-939 mitigated HSC activation and liver fibrosis, which were abrogated by HSC-specific LDH-A overexpression in mice with fibrosis. Inhibition of HSC glycolysis by targeting Wnt/β-catenin signaling and LDH-A had therapeutic promise for liver fibrosis.

A Comparison of the Fifth World Health Organization and the International Consensus Classifications of Mature T-Cell Lymphomas.

Peripheral T-cell lymphomas (PTCLs) are a rare subset of non-Hodgkin lymphomas that often carry significant difficulty in diagnosis and classification because of their rarity and biological complexity. Previous editions of the World Health Organization (WHO) classifications of hemopoietic neoplasms in 2001, 2008, and 2017 aimed to standardize hemopoietic neoplasm diagnosis in general. Since then, crucial clinico-pathological, immunophenotypic, and recent molecular discoveries have been made in the field of lymphomas, contributing to refining diagnostic criteria of several diseases, upgrading entities previously defined as provisional, and identifying new entities. In 2022, two different models were proposed to classify hematolymphoid neoplasms: the 5th edition of the WHO classification (WHO-HAEM5) and the International Consensus Classification (ICC). Of note, a common nosography is mandatory to ensure progress in health science and ensure the basis for a real precision medicine. In this article, the authors summarized the main differences with the previous fourth WHO edition and reviewed the main discrepancies between the two newest classifications, as far as PTCLs are concerned.

Posterior fossa ependymomas in children and adolescents - the state of the art

Central nervous system tumors in pediatric patients represent the most frequent solid tumor type in this age group, especially ependymomas, which comprise 5% of childhood intracranial tumors, and occur mainly in the posterior fossa. The MEDLINE and ClinicalTrials databases were searched for studies on posterior fossa ependymomas, 64 studies met the search criteria. 
 Symptoms include ataxia, vertigo, headache, vomiting, cranial nerve palsies, or papilledema, resulting from intracranial hypertension due to obstruction of CSF circulation. Imaging examination often reveals posterior fossa ependymomas in the fourth ventricle or cerebellopontine angle; they may extend through the foramina of Luschka, Magendie, and/or magnum. These tumors cause a significant mass effect, displacing rather than invading the hidbrain, and may involve vessels or cranial nerves, making surgical resection difficult. Various radiological features have been studied to distinguish between ependymomas and other histological types, as well as between ependymoma subtypes. 
 The 2021 WHO classification of central nervous system tumors classifies ependymomas by location. Three types occur in the posterior fossa: subependymomas, PF-A, and PF-B. These are differentiated by the degree of DNA methylation and CpG islands, which can be assessed by immunohistochemical analysis with H3K27me3. 
 The consensus on the treatment of posterior fossa ependymomas in children advocates the maximum possible surgical resection, followed by radiotherapy in patients >1 year. Chemotherapy remains controversial, with no survival gain. Recent studies suggest molecular subgroups as an independent prognostic factor in posterior fossa ependymomas. The therapeutic challenge of ependymomas requires new treatment options, based on new technologies and molecular discoveries.