Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.     

Sorting through the extensive and confusing roles of sortilin in metabolic disease

Sortilin is a post-Golgi trafficking receptor homologous to the yeast vacuolar protein sorting receptor 10 (VPS10). The VPS10 motif on sortilin is a 10-bladed β-propeller structure capable of binding more than 50 proteins, covering a wide range of biological functions including lipid and lipoprotein metabolism, neuronal growth and death, inflammation, and lysosomal degradation. Sortilin has a complex cellular trafficking itinerary, where it functions as a receptor in the trans-Golgi network, endosomes, secretory vesicles, multivesicular bodies, and at the cell surface. In addition, sortilin is associated with hypercholesterolemia, Alzheimer’s disease, prion diseases, Parkinson’s disease, and inflammation syndromes. The 1p13.3 locus containing SORT1, the gene encoding sortilin, carries the strongest association with LDL-C of all loci in human genome-wide association studies. However, the mechanism by which sortilin influences LDL-C is unclear. Here, we review the role sortilin plays in cardiovascular and metabolic diseases and describe in detail the large and often contradictory literature on the role of sortilin in the regulation of LDL-C levels.     

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State–Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.     

ProMetheusDB: An In-Depth Analysis of the High-Quality Human Methyl-proteome

Protein arginine (R) methylation is a post-translational modification involved in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction, and tumor development. Although several advancements were made in the study of this modification by mass spectrometry, researchers still face the problem of a high false discovery rate. We present a dataset of high-quality methylations obtained from several different heavy methyl stable isotope labeling with amino acids in cell culture experiments analyzed with a machine learning–based tool and show that this model allows for improved high-confidence identification of real methyl-peptides. Overall, our results are consistent with the notion that protein R methylation modulates protein–RNA interactions and suggest a role in rewiring protein–protein interactions, for which we provide experimental evidence for a representative case (i.e., NONO [non-POU domain–containing octamer-binding protein]–paraspeckle component 1 [PSPC1]). Upon intersecting our R-methyl-sites dataset with the PhosphoSitePlus phosphorylation dataset, we observed that R methylation correlates differently with S/T-Y phosphorylation in response to various stimuli. Finally, we explored the application of heavy methyl stable isotope labeling with amino acids in cell culture to identify unconventional methylated residues and successfully identified novel histone methylation marks on serine 28 and threonine 32 of H3. The database generated, named ProMetheusDB, is freely accessible at https://bioserver.ieo.it/shiny/app/prometheusdb.     

Chemical language models for de novo drug design: Challenges and opportunities

Generative deep learning is accelerating de novo drug design, by allowing the generation of molecules with desired properties on demand. Chemical language models – which generate new molecules in the form of strings using deep learning – have been particularly successful in this endeavour. Thanks to advances in natural language processing methods and interdisciplinary collaborations, chemical language models are expected to become increasingly relevant in drug discovery. This minireview provides an overview of the current state-of-the-art of chemical language models for de novo design, and analyses current limitations, challenges, and advantages. Finally, a perspective on future opportunities is provided.     

What does engram encode?: Heterogeneous memory engrams for different aspects of experience

Long-lasting synaptic changes within the neuronal network mediate memory. Neurons bearing such physical traces of memory (memory engram cells) are often equated with neurons expressing immediate early genes (IEGs) during a specific experience. However, past studies observed the expression of different IEGs in non-overlapping neurons or synaptic plasticity in neurons that do not express a particular IEG. Importantly, recent studies revealed that distinct subsets of neurons expressing different IEGs or even IEG negative-(yet active) neurons support different aspects of memory or computation, suggesting a more complex nature of memory engram cells than previously thought. In this short review, we introduce studies revealing such heterogeneous composition of the memory engram and discuss how the memory system benefits from it.     

Deep generative modeling for volume reconstruction in cryo-electron microscopy

Advances in cryo-electron microscopy (cryo-EM) for high-resolution imaging of biomolecules in solution have provided new challenges and opportunities for algorithm development for 3D reconstruction. Next-generation volume reconstruction algorithms that combine generative modelling with end-to-end unsupervised deep learning techniques have shown promise, but many technical and theoretical hurdles remain, especially when applied to experimental cryo-EM images. In light of the proliferation of such methods, we propose here a critical review of recent advances in the field of deep generative modelling for cryo-EM reconstruction. The present review aims to (i) provide a unified statistical framework using terminology familiar to machine learning researchers with no specific background in cryo-EM, (ii) review the current methods in this framework, and (iii) outline outstanding bottlenecks and avenues for improvements in the field.     

MLR-OOD: A Markov Chain Based Likelihood Ratio Method for Out-Of-Distribution Detection of Genomic Sequences

Machine learning or deep learning models have been widely used for taxonomic classification of metagenomic sequences and many studies reported high classification accuracy. Such models are usually trained based on sequences in several training classes in hope of accurately classifying unknown sequences into these classes. However, when deploying the classification models on real testing data sets, sequences that do not belong to any of the training classes may be present and are falsely assigned to one of the training classes with high confidence. Such sequences are referred to as out-of-distribution (OOD) sequences and are ubiquitous in metagenomic studies. To address this problem, we develop a deep generative model-based method, MLR-OOD, that measures the probability of a testing sequencing belonging to OOD by the likelihood ratio of the maximum of the in-distribution (ID) class conditional likelihoods and the Markov chain likelihood of the testing sequence measuring the sequence complexity. We compose three different microbial data sets consisting of bacterial, viral, and plasmid sequences for comprehensively benchmarking OOD detection methods. We show that MLR-OOD achieves the state-of-the-art performance demonstrating the generality of MLR-OOD to various types of microbial data sets. It is also shown that MLR-OOD is robust to the GC content, which is a major confounding effect for OOD detection of genomic sequences. In conclusion, MLR-OOD will greatly reduce false positives caused by OOD sequences in metagenomic sequence classification.     

Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression

Mass spectrometry(MS)-based omics technologies are now widely used to profile small molecules in multiple matrices to confer comprehensive snapshots of cellular metabolic phenotypes.The metabolomes of cells,tissues,and organisms comprise a variety of molecules including lipids,amino acids,sugars,organic acids,and so on.Metabolomics mainly focus on the hydrophilic classes,while lipidomics has emerged as an independent omics owing to the complexities of the organismal lipidomes.The potential roles of lipids and small metabolites in disease pathogenesis have been widely investigated in various human diseases,but system-level understanding is largely lacking,which could be partly attributed to the insufficiency in terms of metabolite coverage and quantitation accuracy in current analytical technologies.While scientists are continuously striving to develop high-coverage omics approaches,integration of metabolomics and lipidomics is becoming an emerging approach to mechanistic investigation.Integration of metabolome and lipidome offers a complete atlas of the metabolic landscape,enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology,facilitating the study of interconnection between lipids and other metabolites in disease progression.In this review,we summarize omics-based findings on the roles of lipids and metabolites in the pathogenesis of selected major diseases threatening public health.We also discuss the advantages of integrating lipidomics and metabolomics for in-depth understanding of molecular mechanism in disease pathogenesis.     

Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the “in-cell digest.” We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with “waves” of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle–regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.     

The dazzling rise of neurofilaments: Physiological functions and roles as biomarkers

In the last two years, neurofilaments (NFs) have become one of the most blazing topics in clinical neuroscience. NFs are major cytoskeletal constituents of neurons, can be detected in body fluids, and have recently emerged as universal biomarkers of neuronal injury and neurological diseases. This review will examine the evolving landscape of NFs, from their specific cellular functions within neurons to their broad clinical value as biomarkers. Particular attention will be given to the dynamic nature of the NF network and its novel roles in microtubule regulation, neurotransmission, and nanomedicine. Building from the initial evidence of causative mutations in NF genes in Charcot–Marie–Tooth diseases, the latest advances at the frontiers of basic and clinical sciences have expanded the scope and relevance of NFs for human health remarkably and have poised to fuel innovation in cell biology and neuroscience.     

Crumbs proteins regulate layered retinal vascular development required for vision

Crumbs proteins are transmembrane proteins that regulate cellular apico-basal polarity. Animals carrying mutated crb1 present retinal vascular abnormalities; this mutation is associated with progressive retinal degeneration with intraretinal cystoid fluid collection in humans. This study aimed to evaluate a potential role of crumbs proteins in retinal vascular development and maintenance. We demonstrated that crumbs homologues (CRBs) were differentially expressed and changed dramatically during mouse retinal vascular development. Intravitreal injection of CRB1 and CRB2 siRNA induced delayed development of the deep capillary plexus and premature development of the intermediate capillary plexus, resulting in disrupted vascular integrity. However, microfluidic chip assay using human retinal endothelial cells revealed that CRBs do not directly affect in vitro retinal angiogenesis. CRBs control retinal angiogenesis by regulating neuroglial vascular endothelial growth factor-A (VEGFA) and matrix metalloproteinase-3 expression. These findings demonstrate a pivotal role of CRBs in providing critical neurotrophic support through normal layered vascular network development and maintenance. This implies that preserving CRBs and restoring layered retinal vascular networks could be novel targets for preventing vision-threatening retinal diseases.     

Predicting novel disease mutations in the cardiac sodium channel

BackgroundVoltage-gated sodium channels Nav1.x mediate the rising phase of action potential in excitable cells. Variations in gene SCN5A, which encodes the hNav1.5 channel, are associated with arrhythmias and other heart diseases. About 1,400 SCN5A variants are listed in public databases, but for more than 30% of these the clinical significance is unknown and can currently only be derived by bioinformatics approaches.Methods and resultsWe used the ClinVar, SwissVar, Humsavar, gnomAD, and Ensembl databases to assemble a dataset of 1392 hNav1.5 variants (370 pathogenic variants, 602 benign variants and 420 variants of uncertain significance) as well as a dataset of 1766 damaging variants in 20 human sodium and calcium channel paralogs. Twelve in silico tools were tested for their ability to predict damaging mutations in hNav1.5. The best performing tool, MutPred, correctly predicted 93% of damaging variants in our hNav1.5 dataset. Among the 86 hNav1.5 variants for which electrophysiological data are also available, MutPred correctly predicted 82% of damaging variants. In the subset of 420 uncharacterized hNav1.5 variants MutPred predicted 196 new pathogenic variants. Among these, 74 variants are also annotated as damaging in at least one hNav1.5 paralog.ConclusionsUsing a combination of sequence-based bioinformatics techniques and paralogous annotation we have substantially expanded the knowledge on disease variants in the cardiac sodium channel and assigned a pathogenic status to a number of mutations that so far have been described as variants of uncertain significance. A list of reclassified hNav1.5 variants and their properties is provided.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

A novel celecoxib analog UTX-121 inhibits HT1080 cell invasion by modulating membrane-type 1 matrix metalloproteinase

We designed and synthesized a celecoxib derivative UTX-121 to enhance its anti-tumor activity. Similar to celecoxib, this compound could also inhibit matrix metalloproteinase (MMP)-9 activity. In addition, UTX-121 suppressed membrane-type 1 MMP (MT1-MMP)-mediated pro-MMP-2 activation by disturbing the cell surface expression of MT1-MMP. UTX-121 also impeded the glycosylation of cell surface proteins, resulting in the suppression of cell attachment to fibronectin. This inhibition by UTX-121 caused the reduction of fibronectin-stimulated focal adhesion kinase activation, Akt activation, and cell migration. Consequently, UTX-121 treatment significantly inhibited fibronectin-induced HT1080 cell invasion into the Matrigel. UTX-121 may be a potent lead compound that can be used to develop a novel anti-tumor drug.     

MicroID2: A Novel Biotin Ligase Enables Rapid Proximity-Dependent Proteomics

Identifying protein–protein and other proximal interactions is central to dissecting signaling and regulatory processes in cells. BioID is a proximity-dependent biotinylation method that uses an “abortive” biotin ligase to detect proximal interactions in cells in a highly reproducible manner. Recent advancements in proximity-dependent biotinylation tools have improved efficiency and timing of labeling, allowing for measurement of interactions on a cellular timescale. However, issues of size, stability, and background labeling of these constructs persist. Here we modified the structure of BioID2, derived from Aquifex aeolicus BirA, to create a smaller, highly active, biotin ligase that we named MicroID2. Truncation of the C terrminus of BioID2 and addition of mutations to alleviate blockage of biotin/ATP binding at the active site of BioID2 resulted in a smaller and highly active construct with lower background labeling. Several additional point mutations improved the function of our modified MicroID2 construct compared with BioID2 and other biotin ligases, including TurboID and miniTurbo. MicroID2 is the smallest biotin ligase reported so far (180 amino acids [AAs] for MicroID2 versus 257 AAs for miniTurbo and 338 AAs for TurboID), yet it demonstrates only slightly less labeling activity than TurboID and outperforms miniTurbo. MicroID2 also had lower background labeling than TurboID. For experiments where precise temporal control of labeling is essential, we in addition developed a MicroID2 mutant, termed lbMicroID2 (low background MicroID2), that has lower labeling efficiency but significantly reduced biotin scavenging compared with BioID2. Finally, we demonstrate utility of MicroID2 in mass spectrometry experiments by localizing MicroID2 constructs to subcellular organelles and measuring proximal interactions.     

High Sensitivity and Wide Linearity LC-MS/MS Method for Oxylipin Quantification in Multiple Biological Samples

Oxylipins are important biological regulators that have received extensive research attention. Due to the extremely low concentrations, large concentration variations, and high structural similarity of many oxylipins, the quantitative analysis of oxylipins in biological samples is always a great challenge. Here, we developed a liquid chromatography-tandem mass spectrometry-based method with high sensitivity, wide linearity, and acceptable resolution for quantitative profiling of oxylipins in multiple biological samples. A total of 104 oxylipins, some with a high risk of detection crosstalk, were well separated on a 150 mm column over 20 min. The method showed high sensitivity with lower limits of quantitation for 87 oxylipins, reaching 0.05–0.5 pg. Unexpectedly, we found that the linear range for 16, 18, and 17 oxylipins reached 10,000, 20,000, and 40,000 folds, respectively. Due to the high sensitivity, while reducing sample consumption to below half the volume of previous methods, 74, 78, and 59 low-abundance oxylipins, among which some were difficult to detect like lipoxins and resolvins, were well quantified in the tested mouse plasma, mouse liver, and human plasma samples, respectively. Additionally, we determined that analytes with multifarious concentrations of over a 1,000-fold difference could be well quantified simultaneously due to the wide linearity. In conclusion, most likely due to the instrumental advancement, this method effectively improves the quantitative sensitivity and linear range over existing methods, which will facilitate and advance the study of the physiological and pathophysiological functions of oxylipins.     

Recent insight into intermediate filament structure

Intermediate filaments (IFs) are key players in multiple cellular processes throughout human tissues. Their biochemical and structural properties are important for understanding filament assembly mechanisms, for interactions between IFs and binding partners, and for developing pharmacological agents that target IFs. IF proteins share a conserved coiled-coil central-rod domain flanked by variable N-terminal ‘head’ and C-terminal ‘tail’ domains. There have been several recent advances in our understanding of IF structure from the study of keratins, glial fibrillary acidic protein, and lamin. These include discoveries of (i) a knob–pocket tetramer assembly mechanism in coil 1B; (ii) a lamin-specific coil 1B insert providing a one-half superhelix turn; (iii) helical, yet flexible, linkers within the rod domain; and (iv) the identification of coil 2B residues required for mature filament assembly. Furthermore, the head and tail domains of some IFs contain low-complexity aromatic-rich kinked segments, and structures of IFs with binding partners show electrostatic surfaces are a major contributor to complex formation. These new data advance the connection between IF structure, pathologic mutations, and clinical diseases in humans.