A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use.

Unsupervised learning of progress coordinates during weighted ensemble simulations: Application to millisecond protein folding.

Our method identifies outliers in a latent space model of the system's sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate a millisecond protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model.

Senescent cells inhibit mouse myoblast differentiation via the SASP-lipid 15d-PGJ mediated modification and control of HRas.

Senescent cells are characterized by multiple features such as increased expression of senescence-associated β-galactosidase activity (SA β-gal) and cell cycle inhibitors such as p21 or p16. They accumulate with tissue damage and dysregulate tissue homeostasis. In the context of skeletal muscle, it is known that agents used for chemotherapy such as Doxorubicin (Doxo) cause buildup of senescent cells, leading to the inhibition of tissue regeneration.

Cold-shock proteome of myoblasts reveals role of RBM3 in promotion of mitochondrial metabolism and myoblast differentiation.

Adaptation to hypothermia is important for skeletal muscle cells under physiological stress and is used for therapeutic hypothermia (mild hypothermia at 32  °C). We show that hypothermic preconditioning at 32  °C for 72 hours improves the differentiation of skeletal muscle myoblasts using both C2C12 and primary myoblasts isolated from 3 month and 18-month-old mice. We analyzed the cold-shock proteome of myoblasts exposed to hypothermia (32  °C for 6 and 48 h) and identified significant changes in pathways related to RNA processing and central carbon, fatty acid, and redox metabolism.

Epitopes recognition of SARS-CoV-2 nucleocapsid RNA binding domain by human monoclonal antibodies.

Coronavirus nucleocapsid protein (NP) of SARS-CoV-2 plays a central role in many functions important for virus proliferation including packaging and protecting genomic RNA. The protein shares sequence, structure, and architecture with nucleocapsid proteins from betacoronaviruses. The N-terminal domain (NP) binds RNA and the C-terminal domain is responsible for dimerization.

Robotic pendant drop: containerless liquid for μs-resolved, AI-executable XPCS.

The dynamics and structure of mixed phases in a complex fluid can significantly impact its material properties, such as viscoelasticity. Small-angle X-ray Photon Correlation Spectroscopy (SA-XPCS) can probe the spontaneous spatial fluctuations of the mixed phases under various in situ environments over wide spatiotemporal ranges (10-10 s /10-10 m). Tailored material design, however, requires searching through a massive number of sample compositions and experimental parameters, which is beyond the bandwidth of the current coherent X-ray beamline.

Model certainty in cellular network-driven processes with missing data.

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms.

Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery.

Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits -e.g. differences in cell function, disease, drug resistance and others.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates.

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes.

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.

Efficacy and Outcomes of CYP3A5 Genotype-Based Tacrolimus Dosing Compared to Conventional Body Weight-based Dosing in Living Donor Kidney Transplant Recipients.

Introduction: Clinical use of tacrolimus has been challenging due to its narrow therapeutic index and highly variable pharmacokinetics. In this study, we compared patients who received body weight-based tacrolimus dosing pre-transplant (transplanted from 2016 to 2018) with those who received CYP3A5 genotype-based dosing (2018 to 2020).

Methods: Eighty-two renal transplant recipients were non-randomly assigned to genotype-adapted or bodyweight-based tacrolimus dosing groups.

Aryl hydrocarbon receptor blocks aging-induced senescence in the liver and fibroblast cells.

Aging impairs organismal homeostasis leading to multiple pathologies. Yet, the mechanisms and molecular intermediates involved are largely unknown. Here, we report that aged aryl hydrocarbon receptor-null mice () had exacerbated cellular senescence and more liver progenitor cells.

A microRNA binding site polymorphism in the 3' UTR region of VEGF-A gene modifies colorectal cancer risk based on ethnicity: a meta-analysis.

Background: Vascular endothelial growth factor A (VEGF-A) plays an integral role in angiogenesis by contributing to growth, development, and metastasis of solid tumors. Recently, a single-nucleotide polymorphism +936C/T located in the VEGF-A 3' untranslated region (UTR) facilitated the susceptibility of colorectal cancer. The association between VEGF-A gene polymorphism +936C/T and colorectal cancer risk has been widely studied in the last decade, but presently, the results furnished remain enigmatic.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques.

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers.

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods.

Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity.

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose a significant hazard because of their widespread industrial uses, environmental persistence, and bioaccumulation. A growing, increasingly diverse inventory of PFAS, including 8163 chemicals, has recently been updated by the U.S.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol.

Unlabelled: We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes.