2'3'-cGAMP interactome identifies 2'3'-cGAMP/Rab18/FosB signaling in cell migration control independent of innate immunity.

c-di-GAMP was first identified in bacteria to promote colonization, while mammalian 2'3'-cGAMP is synthesized by cGAS to activate STING for innate immune stimulation. However, 2'3'-cGAMP function beyond innate immunity remains elusive. Here, we report that 2'3'-cGAMP promotes cell migration independent of innate immunity. 2'3'-cGAMP interactome analysis identifies the small GTPase Rab18 as a 2'3'-cGAMP binding partner and effector in cell migration control. Mechanistically, 2'3'-cGAMP binds Rab18 to facilitate GTP loading and subsequent Rab18 activation, which further promotes FosB transcription in facilitating cell migration. Induced synthesis of endogenous 2'3'-cGAMP by intrabreast tumor bacterium S. aureus infection or low-dose doxorubicin treatment facilitates cell migration depending on the cGAS/cGAMP/Rab18/FosB signaling. We find that lovastatin induces Rab18 deprenylation that abolishes 2'3'-cGAMP recognition therefore suppressing cell migration. Together, our study reveals a previously unidentified 2'3'-cGAMP function in cell migration control via the 2'3'-cGAMP/Rab18/FosB signaling that provides additional insights into clinical applications of 2'3'-cGAMP.

Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions.

A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 4 proteins in a typical human cell offer a plausible 10 8 interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete datasets (10 3 -10 4 protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small datasets. Yuel 2 is pre-trained on a large-scale dataset to learn intricate structural features and then fine-tuned on specialized datasets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics - Kd, Ki, IC50, and EC50 - between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.

Comprehensive mapping of the Interaction of levodopa and iron metabolism in Parkinson's disease.

Levodopa remains the primary treatment for Parkinson's disease (PD), yet its long-term use has been associated with iron accumulation in the brain, a phenomenon linked to neurodegeneration. We utilize deep machine learning to determine plausible molecular mechanisms that may underlie the effects of levodopa on iron metabolism. Using the DRIFT platform, we performed a proteome-wide target identification of levodopa and uncovered significant interactions potentially involved in cellular iron transport. Pathway analysis revealed that levodopa may influence critical iron-related pathways, including the response of EIF2AK1 to heme deficiency, heme signaling, and ABC-family protein-mediated transport. These findings suggest that levodopa may contribute to iron dysregulation in PD by interacting with iron transporters and modulating iron-related pathways. Because levodopa is used at relatively high doses in PD, our findings provide new insight into secondary effects unrelated to being a precursor of dopamine. This highlights the need for careful consideration of its effects on iron metabolism as a consequence of use in the long-term management of PD. Further experimental validation is required to confirm these interactions, and also to explore potential strategies to mitigate iron-related side effects while preserving therapeutic efficacy.

Quantifying network behavior in the rat prefrontal cortex.

The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, specifically the rodent spike train and local field potential recordings during the T-maze alternation task, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. To approach these theoretical limits, we applied dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials. The goal was to quantify neural network synchronicity and the correlation of neuroelectrophysiology with rat behavior. The results show the statistical limitations of existing data, and the fact that making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis is impossible until larger and cleaner datasets are available.

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells.

Misfolded species of superoxide dismutase 1 (SOD1) are associated with increased death in amyotrophic lateral sclerosis (ALS) models compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may drive disease pathology. Here, we uncovered the SOD1 trimer interactome-a map of potential tissue-selective protein-binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers and found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We discovered SOD1 trimer-selective enrichment of genes. We performed detailed computational and biochemical characterization of SOD1 trimer protein binding for septin-7. Our investigation highlights key proteins and pathways within distinct tissues, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers.

CANDI: A Web Server for Predicting Molecular Targets and Pathways of Cannabis-Based Therapeutics.

Background: Cannabis sativa with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. Methods: To investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. Results: Our analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. Conclusions: By combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities.

Identification of a Novel Protein Phosphatase 2A Activator, PPA24, as a Potential Therapeutic for FOLFOX-Resistant Colorectal Cancer.

A series of compounds were designed utilizing molecular modeling and fragment-based design based upon the known protein phosphatase 2A (PP2A) activators, NSC49L and iHAP1, and evaluated for their ability to inhibit the viability of colorectal cancer (CRC) and folinic acid, 5-fluorouracil, and oxaliplatin (FOLFOX)-resistant CRC cells. PPA24 (19a) was identified as the most cytotoxic compound with IC50 values in the range of 2.36-6.75 µM in CRC and FOLFOX-resistant CRC cell lines. It stimulated PP2A activity to a greater extent, displayed lower binding energies through molecular docking, and showed higher binding affinity through surface plasmon resonance for PP2A catalytic subunit α than the known PP2A activators. PPA24 dose-dependently induced apoptosis and oxidative stress, decreased the level of c-Myc expression, and synergistically potentiated cytotoxicity when combined with gemcitabine and cisplatin. Furthermore, a PPA24-encapsulated nanoformulation significantly inhibited the growth of CRC xenografts without systemic toxicities. Together, these results signify the potential of PPA24 as a novel PP2A activator and a prospective therapeutic for CRC and FOLFOX-resistant CRC.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology.

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

APOE regulates the transport of GM1.

Apolipoprotein E (APOE) is responsible for lipid transport, including cholesterol transport and clearance. While the ε4 allele of APOE (APOE4) is associated with a significant genetic risk factor for late-onset Alzheimer's disease (AD), no mechanistic understanding of its contribution to AD etiology has been established yet. In addition to cholesterol, monosialotetrahexosylganglioside (GM1) is a crucial lipid component in cell membranes and has been implicated in promoting the aggregation of amyloid beta protein (Aß), a key protein associated with AD. Here, we ask whether there are direct interactions between APOE and GM1 that further impact AD pathology. We find that both APOE3 and APOE4 exhibit superior binding affinity to GM1 compared to cholesterol and have an enhanced cellular uptake to GM1 lipid structures than cholesterol lipid structures. APOE regulates the transport process of GM1 depending on the cell type, which is influenced by the expression of APOE receptors in different cell lines and alters GM1 contents in cell membranes. We also find that the presence of GM1 alters the secondary structure of APOE3 and APOE4 and enhances the binding affinity between APOE and its receptor low-density lipoprotein receptor (LDLR), consequently promoting the cellular uptake of lipid structures in the presence of APOE. To understand the enhanced cellular uptake observed in lipid structures containing 20% GM1, we determined the distribution of GM1 on the membrane and found that GM1 clustering in lipid rafts, thereby supporting the physiological interaction between APOE and GM1. Overall, we find that APOE plays a regulatory role in GM1 transport, and the presence of GM1 on the lipid structures influences this transport process. Our studies introduce a plausible direct link between APOE and AD etiology, wherein APOE regulates GM1, which, in turn, promotes Aß oligomerization and aggregation.

Systematic review and meta-analysis of dysregulated microRNAs derived from liquid biopsies as biomarkers for amyotrophic lateral sclerosis.

The discovery of disease-specific biomarkers, such as microRNAs (miRNAs), holds the potential to transform the landscape of Amyotrophic Lateral Sclerosis (ALS) by facilitating timely diagnosis, monitoring treatment response, and accelerating drug discovery. Such advancement could ultimately improve the quality of life and survival rates for ALS patients. Despite more than a decade of research, no miRNA biomarker candidate has been translated into clinical practice. We conducted a systematic review and meta-analysis to quantitatively synthesize data from original studies that analyzed miRNA expression from liquid biopsies via PCR and compared them to healthy controls. Our analysis encompasses 807 miRNA observations from 31 studies, stratified according to their source tissue. We identified consistently dysregulated miRNAs in serum (hsa-miR-3665, -4530, -4745-5p, -206); blood (hsa-miR-338-3p, -183-5p); cerebrospinal fluid (hsa-miR-34a-3p); plasma (hsa-miR-206); and neural-enriched extracellular vesicles from plasma (hsa-miR-146a-5p, -151a-5p, -10b-5p, -29b-3p, and -4454). The meta-analyses provided further support for the upregulation of hsa-miR-206, hsa-miR-338-3p, hsa-miR-146a-5p and hsa-miR-151a-5p, and downregulation of hsa-miR-183-5p, hsa-miR-10b-5p, hsa-miR-29b-3p, and hsa-miR-4454 as consistent indicators of ALS across independent studies. Our findings provide valuable insights into the current understanding of miRNAs' dysregulated expression in ALS patients and on the researchers' choices of methodology. This work contributes to the ongoing efforts towards discovering disease-specific biomarkers.

Immunostimulation of Fibrous Nucleic Acid Nanoparticles Can be Modulated through Aptamer-Based Functional Moieties: Unveiling the Structure-Activity Relationship and Mechanistic Insights.

Fibrous nanomaterials containing silica, titanium oxide, and carbon nanotubes are notoriously known for their undesirable inflammatory responses and associated toxicities that have been extensively studied in the environmental and occupational toxicology fields. Biopersistance and inflammation of "hard" nanofibers prevent their broader biomedical applications. To utilize the structural benefits of fibrous nanomaterials for functionalization with moieties of therapeutic significance while preventing undesirable immune responses, researchers employ natural biopolymers─RNA and DNA─to design "soft" and biodegradable nanomaterials with controlled immunorecognition. Nucleic acid nanofibers have been shown to be safe and efficacious in applications that do not require their delivery into the cells such as the regulation of blood coagulation. Previous studies demonstrated that unlike traditional therapeutic nucleic acids (e.g., CpG DNA oligonucleotides) nucleic acid nanoparticles (NANPs), when used without a carrier, are not internalized by the immune cells and, as such, do not induce undesirable cytokine responses. In contrast, intracellular delivery of NANPs results in cytokine responses that are dependent on the physicochemical properties of these nanomaterials. However, the structure-activity relationship of innate immune responses to intracellularly delivered fibrous NANPs is poorly understood. Herein, we employ the intracellular delivery of model RNA/DNA nanofibers functionalized with G-quadruplex-based DNA aptamers to investigate how their structural properties influence cytokine responses. We demonstrate that nanofibers' scaffolds delivered to the immune cells using lipofectamine induce interferon response via the cGAS-STING signaling pathway activation and that DNA aptamers incorporation shields the fibers from recognition by cGAS and results in a lower interferon response. This structure-activity relationship study expands the current knowledge base to inform future practical applications of intracellularly delivered NANPs as vaccine adjuvants and immunotherapies.

Rational peptide design for targeting cancer cell invasion.

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Differentiable rotamer sampling with molecular force fields.

Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks.

Molecular basis and cellular functions of vinculin-actin directional catch bonding.

The ability of cells and tissues to respond differentially to mechanical forces applied in distinct directions is mediated by the ability of load-bearing proteins to preferentially maintain physical linkages in certain directions. However, the molecular basis and biological consequences of directional force-sensitive binding remain unclear. Vinculin (Vcn) is a load-bearing linker protein that exhibits directional catch bonding due to interactions between the Vcn tail domain (Vt) and filamentous (F)-actin. We developed a computational approach to predict Vcn residues involved in directional catch bonding and produced a set of associated Vcn variants with unaltered Vt structure, actin binding, or phospholipid interactions. Incorporation of the variants did not affect Vcn activation but reduced Vcn loading and altered exchange dynamics, consistent with the loss of directional catch bonding. Expression of Vcn variants perturbed the coordination of subcellular structures and cell migration, establishing key cellular functions for Vcn directional catch bonding.

REAP-2: An interactive quantitative tool for robust and efficient dose-response curve estimation.

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison.

Allosteric regulation of kinase activity in living cells.

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities, making it difficult to specifically target one kinase, and allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss the methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or 'sensors,' are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Septins Enable T Cell Contact Guidance via Amoeboid-Mesenchymal Switch.

Lymphocytes exit circulation and enter in-tissue guided migration toward sites of tissue pathologies, damage, infection, or inflammation. By continuously sensing and adapting to the guiding chemo-mechano-structural properties of the tissues, lymphocytes dynamically alternate and combine their amoeboid (non-adhesive) and mesenchymal (adhesive) migration modes. However, which mechanisms guide and balance different migration modes are largely unclear. Here we report that suppression of septins GTPase activity induces an abrupt amoeboid-to-mesenchymal transition of T cell migration mode, characterized by a distinct, highly deformable integrin-dependent immune cell contact guidance. Surprisingly, the T cell actomyosin cortex contractility becomes diminished, dispensable and antagonistic to mesenchymal-like migration mode. Instead, mesenchymal-like T cells rely on microtubule stabilization and their non-canonical dynein motor activity for high fidelity contact guidance. Our results establish septin's GTPase activity as an important on/off switch for integrin-dependent migration of T lymphocytes, enabling their dynein-driven fluid-like mesenchymal propulsion along the complex adhesion cues.

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology.

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.