Molecular Informatics Research Articles

A cloud-based learning module for biomarker discovery.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Preliminary study on molecular mechanism of COVID-19 intervention by Polygonum cuspidatum through computer bioinformatics.

To explore the mechanism of action of Polygonum cuspidatum in intervening in coronavirus disease 2019 using a network pharmacology approach and to preliminarily elucidate its mechanism. The active ingredients and action targets of P cuspidatum were classified and summarized using computer virtual technology and molecular informatics methods. The active ingredients and relevant target information of P cuspidatum were identified using the TCM Systematic Pharmacology Database and Analysis Platform, the TCM Integrated Pharmacology Research Platform v2.0, and the SwissTarget database. The GENECARDS database was used to search for COVID-19 targets. The STRING database was analyzed and combined with Cytoscape 3.7.1 software to construct a protein interaction network map to screen the core targets. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was then performed. The core compound, polydatin, was selected and the core targets were analyzed by computer virtual docking using software such as discovery studio autodock tool. In vitro cell models were constructed to experimentally validate the activity of the core compound, polydatin. By computer screening, we identified 9 active ingredients and their corresponding 286 targets from P cuspidatum. A search of the GENECARDS database for COVID-19 yielded 303 core targets. By mapping the active ingredient targets to the disease targets, 27 overlapping targets could be extracted as potential targets for the treatment of COVID-19 with P cuspidatum. In addition, the enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathway on core targets showed that the coronavirus disease, MAPK signaling pathway, NF kappa B signaling pathway, and other signaling pathways were highly enriched. Combined with the degree-high target analysis in the protein interaction network, it was found to be mainly concentrated in the NF-kappaB (NF-κB) signaling pathway, indicating that the NF-κB signaling pathway may be an important pathway for P cuspidatum intervention. In vitro assays showed no effect of 0.1 to 10 μM polydatin on cell viability, but an inhibitory effect on the transcriptional activity of NF-κB-RE. Molecular docking showed stable covalent bonding of polydatin molecules with Il-1β protein at residue leu-26, TNF protein ser-60, residue gly-121, and residue ile-258 of ICAM-1 protein, indicating a stable docking result. The treatment of COVID-19 with P cuspidatum is characterized by multi-component, multi-target, and multi-pathway, which can exert a complex network of regulatory effects through the interaction between different targets, providing a new idea and basis for further exploration of the mechanism of action of P cuspidatum in the treatment of COVID-19.

A molecular informatics and in-vitro approach to evaluate the HMG-CoA reductase inhibitory efficacy of monoterpenes, carvacrol and geraniol

Currently, statins, the β-hydroxy-β-methyl-glutaryl-CoA reductase (HMG-R) inhibitors, are widely used to lower cholesterol, nevertheless, they have several side effects. Consequently, the present study is designed to unravel the cardioprotective role of selected natural monoterpenoids (carvacrol and geraniol) via in-vitro targeting and molecular informatics study of HMG-R. Computational molecular informatics study revealed that carvacrol and geraniol efficiently occupies the catalytic site of HMG-R with the binding affinity (ΔG) of −4.60, and −1.99 Kcal/mol, respectively, and molecular mechanical-generalized Born surface area (MM-GBSA) free binding energy was depicted as −17.05 and −29.48 Kcal/mol, respectively. Further, molecular dynamics simulation was carried out for 100 ns. Carvacrol and geraniol potentially and competitively inhibit the in-vitro HMG-R activity with an IC50 value of 78.23 ± 2.21 µM, and 72.91 ± 2.92 µM, respectively. Thus, both carvacrol and geraniol exhibited significant anti-hypercholesterolemic activity while the molecular simulation studies depicted that the GR complex showed better stability than the carvacrol complex.

Designing molecules with autoencoder networks.

Autoencoders are versatile tools in molecular informatics. These unsupervised neural networks serve diverse tasks such as data-driven molecular representation and constructive molecular design. This Review explores their algorithmic foundations and applications in drug discovery, highlighting the most active areas of development and the contributions autoencoder networks have made in advancing this field. We also explore the challenges and prospects concerning the utilization of autoencoders and the various adaptations of this neural network architecture in molecular design.

Developing virtual and augmented reality applications for science, technology, engineering and matheducation.

The Rhode Island IDeA Network of Biomedical Research Excellence Molecular Informatics Core at the University of Rhode Island Information Technology Services Innovative Learning Technologies developed virtual and augmented reality applications to teach concepts in biomedical science, including pharmacology, medicinal chemistry, cell cultureand nanotechnology. The apps were developed as full virtual reality/augmented reality and 3D gaming versions, which do not require virtual reality headsets. Development challenges included creating intuitive user interfaces, text-to-voice functionality, visualization of moleculesand implementing complex science concepts. In-app quizzes are used to assess the user's understanding of topics, and user feedback was collected for several apps to improve the experience. The apps were positively reviewed by users and are being implemented into the curriculum at theUniversity of Rhode Island.

Cover Picture: (Mol. Inf. 6/2023)

Molecular InformaticsVolume 42, Issue 6 2380601 Cover PictureFree Access Cover Picture: (Mol. Inf. 6/2023) First published: 22 June 2023 https://doi.org/10.1002/minf.202380601AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Molecular Informatics presents highest-quality interdisciplinary research that leads to a deeper understanding of biomolecular complexes on the level of biological systems that are relevant for drug discovery and chemical biology, protein and nucleic acid engineering and design, bio-nanomolecular structures, macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, virtual screening, and novel technologies for the design of biologically active molecules. Volume42, Issue6June 20232380601 RelatedInformation

Progress of the “Molecular Informatics” Section in 2022

This is the first Editorial of the "Molecular Informatics" Section (MIS) of the International Journal of Molecular Sciences (IJMS), which was created towards the end of 2018 (the first article was submitted on 27 September 2018) and has experienced significant growth from 2018 to now [...].

Cover Picture: (Mol. Inf. 5/2023)

Molecular InformaticsVolume 42, Issue 5 2380501 Cover PictureFree Access Cover Picture: (Mol. Inf. 5/2023) First published: 11 May 2023 https://doi.org/10.1002/minf.202380501AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Molecular Informatics presents highest-quality interdisciplinary research that leads to a deeper understanding of biomolecular complexes on the level of biological systems that are relevant for drug discovery and chemical biology, protein and nucleic acid engineering and design, bio-nanomolecular structures, macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, virtual screening, and novel technologies for the design of biologically active molecules. Volume42, Issue5May 20232380501 RelatedInformation

The role of a molecular informatics platform to support next generation risk assessment

Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS) program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities.

Cover Picture: (Mol. Inf. 4/2023)

Molecular InformaticsVolume 42, Issue 4 2380401 Cover PictureFree Access Cover Picture: (Mol. Inf. 4/2023) First published: 12 April 2023 https://doi.org/10.1002/minf.202380401AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Molecular Informatics presents highest-quality interdisciplinary research that leads to a deeper understanding of biomolecular complexes on the level of biological systems that are relevant for drug discovery and chemical biology, protein and nucleic acid engineering and design, bio-nanomolecular structures, macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, virtual screening, and novel technologies for the design of biologically active molecules. Volume42, Issue4April 20232380401 RelatedInformation

Propitious Indazole Compounds as β‐ketoacyl‐ACP Synthase Inhibitors and Mechanisms Unfolded for TB Cure: Integrated Rational Design and MD Simulations

AbstractMycobacterium tuberculosis β‐ketoacyl‐ACP synthase I (KasA) involves in mycolic acid biosynthesis for cell wall maintenance; hence, it is a critical target in TB drug design. Thiolactomycin (TLM) and derivatives are the known standard KasA enzyme activity inhibitors. However, TLM analogues have poor activity against KasA protein. Indazole sulphonamide chemotype (JSF‐3285/JFX) was recently reported as a promising KasA enzyme inhibitor. JSF‐3285 mechanism is unclear; thus, it provides a means for designing KasA inhibitors. This study unfolds six hits as unprecedented KasA inhibitors. The inhibitory mechanisms of the screened compounds were investigated and compared with a standard inhibitor (TLM) using integrated molecular informatics and dynamics. JFX, M1, M2, and M5 molecules showed stronger interactions with KasA, having binding energy (kcal/mol) of −44.05, −41.52, −39.51, and −35.9, respectively, against −11.69 for TLM. Molecules showed good predicted inhibitory constants, drug‐likeness, ADME, and synthetic accessibility. KasA complex C‐α atoms RMSD and RMSF showed stable and erratic fluctuations compared to apo KasA. The findings provide potential antimycobacterial lead‐like molecules for future TB drugs.

Cover Picture: (Mol. Inf. 12/2022)

Molecular InformaticsVolume 41, Issue 12 2281201 Cover PictureFree Access Cover Picture: (Mol. Inf. 12/2022) First published: 09 December 2022 https://doi.org/10.1002/minf.202281201AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Molecular Informatics presents highest-quality interdisciplinary research that leads to a deeper understanding of biomolecular complexes on the level of biological systems that are relevant for drug discovery and chemical biology, protein and nucleic acid engineering and design, bio-nanomolecular structures, macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, virtual screening, and novel technologies for the design of biologically active molecules. Volume41, Issue12December 20222281201 RelatedInformation

Expression profiles of the gene retinoid X receptor in <italic>Parafossarulus striatulus </italic>(freshwater snail) exposed to environmental hormones

<p indent="0mm">Retinoid X receptor (RXR) is an important gene with various roles in the regulation of sex development and endocrine behavior in mollusks. To reveal the regulatory effect of environmental hormones on RXR gene expression in mollusks, the RXR gene was cloned from <italic>Parafossarulus striatulus</italic> using reverse transcriptase polymerase chain reaction and rapid-amplification of cDNA ends, and then primers were designed based on the previous construction of the RXR transcriptome of <italic>P</italic>.<italic> striatulus</italic>. Real-time quantitative reverse transcription polymerase chain reaction was performed to quantify the expression level of the RXR gene in <italic>P</italic>.<italic> striatulus</italic> exposed to environmental hormones. The expression of the RXR gene was first inhibited and then increased after E2 treatment, but was first induced and then inhibited after MT treatment. The results indicate that environmental hormones may have toxicological effects on <italic>P</italic>.<italic> striatulus</italic>. At the molecular level, the results provide an analytical method to determine the effects of environmental hormones on RXR and the associated regulation of endocrine behavior of mollusks, and to establish a basis for molecular informatics analysis.

Design of peptides with strong binding affinity to poly(methyl methacrylate) resin by use of molecular simulation-based materials informatics

Peptides with strong binding affinities for poly(methyl methacrylate) (PMMA) resin were designed by use of materials informatics technology based on molecular dynamics simulation for the purpose of covering the resin surface with adhesive peptides, which were expected to result in eco-friendly and biocompatible biomaterials. From the results of binding affinity obtained with this molecular simulation, it was confirmed that experimental values could be predicted with errors <10%. By analyzing the simulation data with the response-surface method, we found that three peptides (RWWRPWW, EWWRPWR, and RWWRPWR), which consist of arginine (R), tryptophan (W), and proline (P), have strong binding affinity to the PMMA resin. These amino acids were effective because arginine and tryptophan have strong binding affinities for methoxycarbonyl groups and methyl groups, which are the main constituents of the PMMA resin, and proline stabilizes the flat zigzag structures of the peptides in water. The strong binding affinities of the three peptides were confirmed by experiments (surface plasmon resonance methods).

Abstract A04: NCI programs supporting technology development for population studies in digital age

Abstract Cancer is the result of a complex interplay of genetic, environmental, host, and societal factors operating over a prolonged time. The development of novel molecular technologies and informatics tools can facilitate a more comprehensive study of the risk factors contributing to the development of and outcomes from cancer in population studies. The National Cancer Institute (NCI) leads two programs in this area: the Innovative Molecular Analysis Technologies (IMAT) Program and the Informatics Technology for Cancer Research (ITCR) Program. IMAT supports the development, technical maturation, and dissemination of novel and potentially transformative next-generation technologies. ITCR program supports research-driven informatics technology development spanning all aspects of cancer research and stages of tool development, from algorithm development to prototyping, enhancement, and sustainment of these tools. The funding opportunities through these programs can support the development and application of new technologies for epidemiology research. For example, molecular technologies may address needs in areas such as exposure assessment, epigenetics, genomics, transcriptomics, imaging, and collection of biospecimens. Informatics technology needs may include genomic tools for data analysis, interpretation and visualization, annotation of genetic variants, supporting sharing of data, natural language processing of electronic health records (EHRs), managing cohort data collection, data harmonization, and extracting unstructured phenotype data from medical records. Importantly, the tools and resources that have been developed through these programs can be leveraged for use by epidemiology researchers. Learn more about the funding opportunities and technologies developed through these programs at https://imat.cancer.gov and https://itcr.cancer.gov. Citation Format: Leah E. Mechanic, Danielle M. Carrick, Tony Dickherber, Juli Klemm, NCI Innovative Molecular Analysis Technologies (IMAT) Program, NCI Informatics Technology for Cancer Research (ITCR) Program. NCI programs supporting technology development for population studies in digital age [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A04.

The Literature of Chemoinformatics: 1978–2018

This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry.

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing.

MotivationMining drug–disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug–disease associations. Importantly, the complexity of molecular target interactions, such as protein–protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context.ResultsIn this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug–protein, disease–protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug–disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis.Availability and implementationSource code and preprocessed datasets are at: https://github.com/zcwang0702/BiFusion.

Cellular Processes Involved in Jurkat Cells Exposed to Nanosecond Pulsed Electric Field.

Recently, nanosecond pulsed electric field (nsPEF) has been considered as a new tool for tumor therapy, but its molecular mechanism of function remains to be fully elucidated. Here, we explored the cellular processes of Jurkat cells exposed to nanosecond pulsed electric field. Differentially expressed genes (DEGs) were acquired from the GEO2R, followed by analysis with a series of bioinformatics tools. Subsequently, 3D protein models of hub genes were modeled by Modeller 9.21 and Rosetta 3.9. Then, a 100 ns molecular dynamics simulation for each hub protein was performed with GROMACS 2018.2. Finally, three kinds of nsPEF voltages (0.01, 0.05, and 0.5 mV/mm) were used to simulate the molecular dynamics of hub proteins for 100 ns. A total of 1769 DEGs and eight hub genes were obtained. Molecular dynamic analysis, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and the Rg, demonstrated that the 3D structure of hub proteins was built, and the structural characteristics of hub proteins under different nsPEFs were acquired. In conclusion, we explored the effect of nsPEF on Jurkat cell signaling pathway from the perspective of molecular informatics, which will be helpful in understanding the complex effects of nsPEF on acute T-cell leukemia Jurkat cells.

Combined High-Throughput Imaging and Sequencing: Addressing the collections on demand requirement in SYNTHESYS+ project

Imagine you are a scientist, working on collections. You have your pet taxon and you need information which is distributed in a number of books and publications but also in the specimens deposited in Museums or Herbaria. Instead of paying visits to these establishments, around the world, you wish there was a means to transform all the information you need into a digitized form of the physical objects, you can reach from the screen of your laptop, tablet or cell phone. You dream you were able to watch, inspect and even dissect the type material you need online but also to compare it with others they way sequences are blasted against large databases, these days. You plan to make global research on this taxon and try to derive patterns from both the molecular and organismal level of the biological organization and to link the patterns resulting to the drivers of change for this taxon. This is the vision of the Virtual Museum of Natural History and one of the ways to achieve this vision is to address the “collections on demand” requirement. One of the possible means to address this requirement is the digitization through the use of the micro-ct technology. The micro-CT virtual laboratory (vLab), developed by LifeWatchGreece research infrastructure (RI), makes it possible the online exploration and dissemination of micro-CT datasets, which are only rarely made available to the public due to their large size and a lack of dedicated online platforms for the interactive manipulation of 3D data. This presentation shows the development of such a “collections on demand” function, implemented by the SYNTHESYS+ project (DiSSCo RI), which combines such high-throughput technologies, that is micro-ct and genomics, to address the scientific community’s requirements. We show that this approach to combine patterns deriving from the application of novel techniques, which represent different kinds of observations is possible and we propose certain case studies as examples. The innovation aspects of this function include: Expansion and development of cost models for Collections on Demand; Development of standards and guidelines for exchange of collection-derived imaging data; Construction of new data pipelines and standard workflows, enabling access to complex digital content such as 3D scans; Development of novel molecular lab protocols, workflows and informatics pipelines, to enable large scale; DNA sequencing of NH collections. Expansion and development of cost models for Collections on Demand; Development of standards and guidelines for exchange of collection-derived imaging data; Construction of new data pipelines and standard workflows, enabling access to complex digital content such as 3D scans; Development of novel molecular lab protocols, workflows and informatics pipelines, to enable large scale; DNA sequencing of NH collections.

Graph convolutional networks for computational drug development and discovery.

Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.