Molecular Views Research Articles

Global profiling of protein complex dynamics with an experimental library of protein interaction markers.

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study.

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.

Heterosis in horticultural crop breeding: combining old theoretical bases with modern genomic views

Heterosis in plants has been among the challenging topics for plant scientists worldwide. The production of F1 hybrid varieties of seed-propagated horticultural species is one of the most successful applications of plant breeding techniques. The exploitation of the heterosis phenomenon promotes homogeneity and maximizes crop yields and is a way for breeders to legally control and protect their commercial products. In the past heterosis has been largely studied and explored in cereal crop systems, considering maize as a model for understanding the genetic bases of this phenomenon. To date, crossbreeding in horticultural vegetables has also rapidly progressed. F1 hybrid varieties are available for many horticultural crops, including both allogamous and autogamous species. Several genetic and nongenetic mechanisms have been applied to facilitate the large-scale production of F1 hybrid seeds in vegetable crops to prevent undesirable selfing. Although the development and commercialization of F1 hybrids is currently common in agriculture, this phenomenon is still being investigated at different levels. With the rapid accumulation of knowledge on plant genome structures and gene activities and the advancement of new genomics platforms and methodologies, significant progress has been achieved in recent years in the study of the genetic and molecular bases of heterosis. This paper provides a brief overview of current theoretical advances and practical predictions of the molecular mechanisms underlying heterosis in plants. The aim is to carefully summarize the fundamental mechanisms of heterosis in plants, focusing on horticultural plant breeding, to improve the existing knowledge in this research area. We describe the quantitative genetic model of phenotypic variation and combine evolutionary, phenotypic and molecular genetic views to explain the origin and manifestation of heterosis and its significance for breeding F1 hybrid varieties in horticultural crops. The principles of genomic prediction and its applications in genomic selection are then covered.

HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.

Molecular property prediction by semantic-invariant contrastive learning.

Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL.

Molecular Views on Mechanisms of Brønsted Acid-Catalyzed Reactions in Zeolites.

The Brønsted acidity of proton-exchanged zeolites has historically led to the most impactful applications of these materials in heterogeneous catalysis, mainly in the fields of transformations of hydrocarbons and oxygenates. Unravelling the mechanisms at the atomic scale of these transformations has been the object of tremendous efforts in the last decades. Such investigations have extended our fundamental knowledge about the respective roles of acidity and confinement in the catalytic properties of proton exchanged zeolites. The emerging concepts are of general relevance at the crossroad of heterogeneous catalysis and molecular chemistry. In the present review, emphasis is given to molecular views on the mechanism of generic transformations catalyzed by Brønsted acid sites of zeolites, combining the information gained from advanced kinetic analysis, in situ, and operando spectroscopies, and quantum chemistry calculations. After reviewing the current knowledge on the nature of the Brønsted acid sites themselves, and the key parameters in catalysis by zeolites, a focus is made on reactions undergone by alkenes, alkanes, aromatic molecules, alcohols, and polyhydroxy molecules. Elementary events of C-C, C-H, and C-O bond breaking and formation are at the core of these reactions. Outlooks are given to take up the future challenges in the field, aiming at getting ever more accurate views on these mechanisms, and as the ultimate goal, to provide rational tools for the design of improved zeolite-based Brønsted acid catalysts.

Molecular Views on Fischer-Tropsch Synthesis.

For nearly a century, the Fischer-Tropsch (FT) reaction has been subject of intense debate. Various molecular views on the active sites and on the reaction mechanism have been presented for both Co- and Fe-based FT reactions. In the last 15 years, the emergence of a surface-science- and molecular-modeling-based bottom-up approach has brought this molecular picture a step closer. Theoretical models provided a structural picture of the Co catalyst particles. Recent surface science experiments and density functional theory (DFT) calculations highlighted the importance of realistic surface coverages, which can induce surface reconstruction and impact the stability of reaction intermediates. For Co-based FTS, detailed microkinetic simulations and mechanistic experiments are moving toward a consensus about the active sites and the reaction mechanism. The dynamic phase evolution of Fe-based catalysts under the reaction conditions complicates identification of the surface structure and the active sites. New techniques can help tackle the combinatorial complexity in these systems. Experimental and DFT studies have addressed the mechanism for Fe-based catalysts; the absence of a clear molecular picture of the active sites, however, limits the development of a molecular view of the mechanism. Finally, direct CO2 hydrogenation to long-chain hydrocarbons could present a sustainable pathway for FT synthesis.

Chemistry Takes Center Stage: Stories and Narratives as Tools for Instruction

Chemistry provides a constant interplay among the macroscopic, molecular, and atomistic views of matter requiring chemical educators to devise effective strategies for merging these attributes. Often, educators are challenged on how to progress towards conducive and active learning where students can see vividly through the lens of conceptual understanding. Chemistry has stories to tell; through narratives, we can convey meaning and create synchrony with the world. This viewpoint offers a glimpse into the utility of narrative thinking and storytelling as instructional tools to enable students’ conceptual understanding of chemistry. A non-exhaustive outlook on learning difficulties is given along with critical aspects of how active narration touches on human psychological perception and, thus, embeds instructions by synchronizing concepts with the world.

Lipid Raft Facilitated Receptor Organization and Signaling: A Functional Rheostat in Embryonic Development, Stem Cell Biology and Cancer.

Molecular views of plasma membrane organization and dynamics are gradually changing over the past fifty years. Dynamics of plasma membrane instigate several signaling nexuses in eukaryotic cells. The striking feature of plasma membrane dynamics is that, it is internally transfigured into various subdomains of clustered macromolecules. Lipid rafts are nanoscale subdomains, enriched with cholesterol and sphingolipids, reside as floating entity mostly on the exoplasmic leaflet of the lipid bilayer. In terms of functionality, lipid rafts are unique among other membrane subdomains. Herein, advances on the roles of lipid rafts in cellular physiology and homeostasis are discussed, precisely, on how rafts dynamically harbor signaling proteins, including GPCRs, catalytic receptors, and ionotropic receptors within it and orchestrate multiple signaling pathways. In the developmental proceedings signaling are designed for patterning of overall organism and they differ from the somatic cell physiology and signaling of fully developed organisms. Some of the developmental signals are characteristic in maintenance of stemness and activated during several types of tumor development and cancer progression. The harmony between extracellular signaling and lineage specific transcriptional programs are extremely important for embryonic development. The roles of plasma membrane lipid rafts mediated signaling in lineage specificity, early embryonic development, stem cell maintenance are emerging. In view of this, we have highlighted and analyzed the roles of lipid rafts in receptor organization, cell signaling, and gene expression during embryonic development; from pre-implantation through the post-implantation phase, in stem cell and cancer biology.

Prognostic Values of BolA Family Member Expression in Hepatocellular Carcinoma.

The BolA gene family member (BOLA1–3) plays an important role in regulating normal and pathological biological processes including liver tumorigenesis. However, their expression patterns as prognostic factors in hepatocellular carcinoma (HCC) patients have not to be elucidated. We examined the transcriptional expressions and survival data of BolA family member in patients with HCC from online databases including ONCOMINE, TCGA, UALCAN, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, SurvExpress, cBioPortal, and Exobase. Network molecular interaction views of BolA family members and their neighborhoods were constructed by the IntAct web server. In our research, we had found that the expression levels of BolA /2/3 mRNA were higher in HCC tissue than in normal liver tissues from TGCA databases. Moreover, the BolA family gene expression level is significantly associated with distinct tumor pathological grade, TMN stage, and overall survival (OS). The BolA family can be considered as prognostic risk biomarkers of HCC. A small number of BolA gene-mutated samples were detected in the HCC tissue. IntAct analysis revealed that BolA1/2/3 was closely associated with the GLRX3 expression in HCC, which is implicated in the regulation of the cellular iron homeostasis and tumor growth. Furthermore, prognostic values of altered BolAs and their neighbor GLRX3 gene in HCC patients were validated by SurvExpress analysis. In conclusion, the membrane BolA family identified in this study provides very useful information for the mechanism of hepatic tumorigenesis.

Surface Science in the Research and Development of Hydrate-Based Sustainable Technologies

Compact gas inclusion in hydrates presents not only fascinating science but also wide-ranging applications in sustainable energy and environmental technologies. The surface of hydrates dictates many essential phenomena underlying hydrate-based processes such as hydrate nucleation and growth, mass transfer, heat transfer, agglomeration, adhesion, and adsorption. Thus, surface science falls within the core of hydrate science and engineering. This paper covers an insight perspective on surface science related to the research and development of hydrate-based sustainable technologies. We present insightful molecular views of hydrate surfaces with a focus on the interfacial premelting, and discuss how new fundamental advances benefit the sustainable engineering in the areas of greener and chemical-free flow assurance, energy-efficient gas storage, carbon capture and storage, and recovery of methane (low-carbon energy) from natural hydrates. We highlight the prospects and challenges as well as stimulate future studies on this important topic.

Editorial 2022

Editorial 2022

Molecular views into cellular functions by in-cell cryo-electron tomography

Molecular views into cellular functions by in-cell cryo-electron tomography

Real-time in-situ 1H NMR of reactions in peptide solution: preaggregation of amyloid-β fragments prior to fibril formation

Abstract In-situ analytical methods are essential for the reliable observation of peptide reactions without perturbation of the system. In this work, a real-time in-situ NMR analysis was performed to gain insight into the initial stage of the aggregation of amyloid-beta (Aβ) 8–25 monomers, S8GY10EVHHQKLVFF20AEDVG25, in solution prior to the fibril formation. NMR chemical shift and intensity changes in combination with the CD spectra revealed no changes in Aβ secondary structure, but the presence of soluble, oligomeric intermediates followed by the appearance of insoluble and non-structured aggregates before β-fibril formation. Molecular views of intermediates and aggregation mechanisms were proposed in comparison with NMR spectral changes in wild-type Aβ 8–25 and its two mutants, A21G and E22G. The mutation of just one amino acid modified the aggregation properties of Aβ 8–25; it slowed or accelerated the fibril formation by controlling the progress of conversion from monomer to aggregate via a soluble, small oligomer.

The Protein Imager: a full-featured online molecular viewer interface with server-side HQ-rendering capabilities.

Molecular viewers' long learning curve is hindering researchers in approaching the field of structural biology for the first time. Herein, we present 'The Protein Imager', a lightweight, powerful and easy-to-use interface as a next-gen online molecular viewer. Furthermore, the interface is linked to an automated server-side rendering system able to generate publication-quality molecular illustrations. The Protein Imager interface has been designed for easy usage for beginners and experts in the field alike. The interface allows the preparation of very complex molecular views maintaining a high level of responsiveness even on mobile devices. The Protein Imager interface is freely available online at https://3dproteinimaging.com/protein-imager. Supplementary data are available at Bioinformatics online.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases.

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

State of the Art and Prospects in Metal-Organic Framework (MOF)-Based and MOF-Derived Nanocatalysis.

Metal-organic framework (MOF) nanoparticles, also called porous coordination polymers, are a major part of nanomaterials science, and their role in catalysis is becoming central. The extraordinary variability and richness of their structures afford engineering synergies between the metal nodes, functional linkers, encapsulated substrates, or nanoparticles for multiple and selective heterogeneous interactions and activations in these MOF-based nanocatalysts. Pyrolysis of MOF-nanoparticle composites forms highly porous N- or P-doped graphitized MOF-derived nanomaterials that are increasingly used as efficient catalysts especially in electro- and photocatalysis. This review first briefly summarizes this background of MOF nanoparticle catalysis and then comprehensively reviews the fast-growing literature reported during the last years. The major parts are catalysis of organic and molecular reactions, electrocatalysis, photocatalysis, and views of prospects. Major challenges of our society are addressed using these well-defined heterogeneous catalysts in the fields of synthesis, energy, and environment. In spite of the many achievements, enormous progress is still necessary to improve our understanding of the processes involved beyond the proof-of-concept, particularly for selective methane oxidation, hydrogen production, water splitting, CO2 reduction to methanol, nitrogen fixation, and water depollution.

Molecular Views into Cellular Function by In Situ Cryo-Electron Tomography

Molecular Views into Cellular Function by In Situ Cryo-Electron Tomography

Biochemical Aspects of the Hepatic Microsomal Ethanol-oxidizing System (MEOS): Resolved Initial Controversies and Updated Molecular Views

The hepatic microsomal ethanol-oxidizing system (MEOS) was initially confronted with much uncertainty, skepticism, scientific antagonism, and heavy discussions. Viewed as scientific challenges, this stimulated further research, and led to its successful separation from both, alcohol dehydrogenase and catalase, and its reconstitution that allowed defining the individual components of MEOS: cytochrome P450 (CYP), reductase, and phospholipids. Subsequently, it was challenging to elucidate the molecular basis of the microsomal ethanol oxidation. Unlike a usual dehydrogenation or simple oxidation process, ethanol oxidation via MEOS proceeds via reactive intermediates, commonly known as reactive oxygen species (ROS) and generated by various microsomal CYP isoenzymes including CYP 2E1, all of which are established components of MEOS. Due to its radical scavenging properties, ethanol combines with a small fraction of hydroxyl radicals and undergoes oxidation while the remaining radicals attack phospholipids of liver cell membranes. Chronic alcohol use enhances MEOS activity by upregulating CYP 2E1 combined with ROS generation, and thereby increases the metabolism of ethanol to acetaldehyde, its first metabolite with a high hepatotoxic potential. Considering the involvement of various CYP isoenzymes as constituents, MEOS is now best defined as a multi-CYP isoenzyme system, participating in ethanol metabolism and responsible for the molecular-based alcoholic liver disease.

1343 Altered metabolism of elastic fibers and collagen fibers derived from TGF-β1 mediated inflammation in atrophic acne scarring

This study was conducted to reveal the process of atrophic acne scar formation in histological and molecular views. Thirty subjects with acne were divided into two groups; fifteen were prone to acne scar (APS) and the others were not prone to acne scar (ANS). Skin samples of acne lesions were obtained on day 1, 3 and 7 after their onset. Elastic fibers, collagen 1 and collagen 3 were significantly decreased and their recovery was delayed in APS compared to those in ANS. On the other hand, the production of neutrophil elastase, matrix metalloproteinases (MMP)-1, MMP-2 and MMP-3 was substantially increased in APS compared to that in ANS. More severe inflammation with elevated various proinflammatory cytokines including IL-1α, IL-1β, IL-6 and TNF-α was observed in APS than in ANS, implicating that excessive inflammation contributes to the aberrant ECM degradation and healing. In this process, it was found that increased TGF-β1 in APS may play a crucial role in aggravating inflammation via activating phospho-TAK1 and NF-κB p65. These results propose the detailed pathogenesis of atrophic acne scarring and ultimately provide a basis of a new therapeutic approach in atrophic acne scar.