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Applications of nanoparticles (NPs) in the life sciences require control over their properties in protein-rich biological fluids, as an NP quickly acquires a layer of proteins on the surface, forming the so-called “protein corona” (PC). Understanding the composition and kinetics of the PC at the molecular level is of considerable importance for controlling NP interaction with cells. Here, we present a systematic study of hard PC formation on the surface of upconversion nanoparticles (UCNPs) coated with positively-charged polyethyleneimine (PEI) and negatively-charged poly (acrylic acid) (PAA) polymers in serum-supplemented cell culture medium. The rationale behind the choice of UCNP is two-fold: UCNP represents a convenient model of NP with a size ranging from 5 nm to >200 nm, while the unique photoluminescent properties of UCNP enable direct observation of the PC formation, which may provide new insight into this complex process. The non-linear optical properties of UCNP were utilised for direct observation of PC formation by means of fluorescence correlation spectroscopy. Our findings indicated that the charge of the surface polymer coating was the key factor for the formation of PC on UCNPs, with an ensuing effect on the NP–cell interactions.

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant ( K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Crop leaves in full sunlight dissipate damaging excess absorbed light energy as heat. When sunlit leaves are shaded by clouds or other leaves, this protective dissipation continues for many minutes and reduces photosynthesis. Calculations have shown that this could cost field crops up to 20% of their potential yield. Here, we describe the bioengineering of an accelerated response to natural shading events in Nicotiana (tobacco), resulting in increased leaf carbon dioxide uptake and plant dry matter productivity by about 15% in fluctuating light. Because the photoprotective mechanism that has been altered is common to all flowering plants and crops, the findings provide proof of concept for a route to obtaining a sustainable increase in productivity for food crops and a much-needed yield jump.

The second phase of the Fifth International Ice Nucleation Workshop (FIN-02) involved the gathering of a large number of researchers at the Karlsruhe Institute of Technology's Aerosol Interactions and Dynamics of the Atmosphere (AIDA) facility to promote characterization and understanding of ice nucleation measurements made by a variety of methods used worldwide. Compared to the previous workshop in 2007, participation was doubled, reflecting a vibrant research area. Experimental methods involved sampling of aerosol particles by direct processing ice nucleation measuring systems from the same volume of air in separate experiments using different ice nucleating particle (INP) types, and collections of aerosol particle samples onto filters or into liquid for sharing amongst measurement techniques that post-process these samples. In this manner, any errors introduced by differences in generation methods when samples are shared across laboratories were mitigated. Furthermore, as much as possible, aerosol particle size distribution was controlled so that the size limitations of different methods were minimized. The results presented here use data from the workshop to assess the comparability of immersion freezing measurement methods activating INPs in bulk suspensions, methods that activate INPs in condensation and/or immersion freezing modes as single particles on a substrate, continuous flow diffusion chambers (CFDCs) directly sampling and processing particles well above water saturation to maximize immersion and subsequent freezing of aerosol particles, and expansion cloud chamber simulations in which liquid cloud droplets were first activated on aerosol particles prior to freezing. The AIDA expansion chamber measurements are expected to be the closest representation to INP activation in atmospheric cloud parcels in these comparisons, due to exposing particles freely to adiabatic cooling. The different particle types used as INPs included the minerals illite NX and potassium feldspar (K-feldspar), two natural soil dusts representative of arable sandy loam (Argentina) and highly erodible sandy dryland (Tunisia) soils, respectively, and a bacterial INP (Snomax®). Considered together, the agreement among post-processed immersion freezing measurements of the numbers and fractions of particles active at different temperatures following bulk collection of particles into liquid was excellent, with possible temperature uncertainties inferred to be a key factor in determining INP uncertainties. Collection onto filters for rinsing versus directly into liquid in impingers made little difference. For methods that activated collected single particles on a substrate at a controlled humidity at or above water saturation, agreement with immersion freezing methods was good in most cases, but was biased low in a few others for reasons that have not been resolved, but could relate to water vapor competition effects. Amongst CFDC-style instruments, various factors requiring (variable) higher supersaturations to achieve equivalent immersion freezing activation dominate the uncertainty between these measurements, and for comparison with bulk immersion freezing methods. When operated above water saturation to include assessment of immersion freezing, CFDC measurements often measured at or above the upper bound of immersion freezing device measurements, but often underestimated INP concentration in comparison to an immersion freezing method that first activates all particles into liquid droplets prior to cooling (the PIMCA-PINC device, or Portable Immersion Mode Cooling chAmber–Portable Ice Nucleation Chamber), and typically slightly underestimated INP number concentrations in comparison to cloud parcel expansions in the AIDA chamber; this can be largely mitigated when it is possible to raise the relative humidity to sufficiently high values in the CFDCs, although this is not always possible operationally. Correspondence of measurements of INPs among direct sampling and post-processing systems varied depending on the INP type. Agreement was best for Snomax® particles in the temperature regime colder than −10 ∘C, where their ice nucleation activity is nearly maximized and changes very little with temperature. At temperatures warmer than −10 ∘C, Snomax® INP measurements (all via freezing of suspensions) demonstrated discrepancies consistent with previous reports of the instability of its protein aggregates that appear to make it less suitable as a calibration INP at these temperatures. For Argentinian soil dust particles, there was excellent agreement across all measurement methods; measures ranged within 1 order of magnitude for INP number concentrations, active fractions and calculated active site densities over a 25 to 30 ∘C range and 5 to 8 orders of corresponding magnitude change in number concentrations. This was also the case for all temperatures warmer than −25 ∘C in Tunisian dust experiments. In contrast, discrepancies in measurements of INP concentrations or active site densities that exceeded 2 orders of magnitude across a broad range of temperature measurements found at temperatures warmer than −25 ∘C in a previous study were replicated for illite NX. Discrepancies also exceeded 2 orders of magnitude at temperatures of −20 to −25 ∘C for potassium feldspar (K-feldspar), but these coincided with the range of temperatures at which INP concentrations increase rapidly at approximately an order of magnitude per 2 ∘C cooling for K-feldspar. These few discrepancies did not outweigh the overall positive outcomes of the workshop activity, nor the future utility of this data set or future similar efforts for resolving remaining measurement issues. Measurements of the same materials were repeatable over the time of the workshop and demonstrated strong consistency with prior studies, as reflected by agreement of data broadly with parameterizations of different specific or general (e.g., soil dust) aerosol types. The divergent measurements of the INP activity of illite NX by direct versus post-processing methods were not repeated for other particle types, and the Snomax® data demonstrated that, at least for a biological INP type, there is no expected measurement bias between bulk collection and direct immediately processed freezing methods to as warm as −10 ∘C. Since particle size ranges were limited for this workshop, it can be expected that for atmospheric populations of INPs, measurement discrepancies will appear due to the different capabilities of methods for sampling the full aerosol size distribution, or due to limitations on achieving sufficient water supersaturations to fully capture immersion freezing in direct processing instruments. Overall, this workshop presents an improved picture of present capabilities for measuring INPs than in past workshops, and provides direction toward addressing remaining measurement issues.

Predicting protein–ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein–ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein–ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and analyze the protein surface information to predict potential binding sites. PGpocket is trained on the scPDB dataset, and its performance is verified on two independent test sets, Coach420 and HOLO4K. The results show that PGpocket achieves a 58% success rate on the Coach420 dataset and a 56% success rate on the HOLO4K dataset. These results surpass competing algorithms, demonstrating PGpocket’s advancement and practicality for protein–ligand binding site prediction.

Earth has had oceans for nearly four billion years 1 and Mars had lakes and rivers 3.5–3.8 billion years ago 2 . However, it is still unknown whether water has ever condensed on the surface of Venus 3 , 4 because the planet—now completely dry 5 —has undergone global resurfacing events that obscure most of its history 6 , 7 . The conditions required for water to have initially condensed on the surface of Solar System terrestrial planets are highly uncertain, as they have so far only been studied with one-dimensional numerical climate models 3 that cannot account for the effects of atmospheric circulation and clouds, which are key climate stabilizers. Here we show using three-dimensional global climate model simulations of early Venus and Earth that water clouds—which preferentially form on the nightside, owing to the strong subsolar water vapour absorption—have a strong net warming effect that inhibits surface water condensation even at modest insolations (down to 325 watts per square metre, that is, 0.95 times the Earth solar constant). This shows that water never condensed and that, consequently, oceans never formed on the surface of Venus. Furthermore, this shows that the formation of Earth’s oceans required much lower insolation than today, which was made possible by the faint young Sun. This also implies the existence of another stability state for present-day Earth: the ‘steam Earth’, with all the water from the oceans evaporated into the atmosphere. Global climate model simulations of early Venus and Earth show that differences in the cloud regimes prevented ocean formation on Venus but not on Earth.

Eighteen free amino acids (FAAs) were quantified in cloud water sampled at the Puy de Dôme station (PUY – France) during 13 cloud events. This quantification has been performed without concentration or derivatization, using liquid chromatography hyphened to mass spectrometry (LC-MS) and the standard addition method to correct for matrix effects. Total concentrations of FAAs (TCAAs) vary from 1.2 to 7.7 µM, Ser (serine) being the most abundant AA (23.7 % on average) but with elevated standard deviation, followed by glycine (Gly) (20.5 %), alanine (Ala) (11.9 %), asparagine (Asn) (8.7 %), and leucine/isoleucine (Leu/I)​​​​​​​ (6.4 %). The distribution of AAs among the cloud events reveals high variability. TCAA constitutes between 0.5 and 4.4 % of the dissolved organic carbon measured in the cloud samples. AA quantification in cloud water is scarce, but the results agree with the few studies that investigated AAs in this aqueous medium. The environmental variability is assessed through a statistical analysis. This work shows that AAs are correlated with the time spent by the air masses within the boundary layer, especially over the sea surface before reaching the PUY. The cloud microphysical properties' fluctuation does not explain the AA variability in our samples, confirming previous studies at the PUY. We finally assessed the sources and the atmospheric processes that potentially explain the prevailing presence of certain AAs in the cloud samples. The initial relative distribution of AAs in biological matrices (proteins extracted from bacterial cells or mammalian cells, for example) could explain the dominance of Ala, Gly, and Leu/I. AA composition of aquatic organisms (i.e., diatom species) could also explain the high concentrations of Ser in our samples. The analysis of the AA hygroscopicity also indicates a higher contribution of AAs (80 % on average) that are hydrophilic or neutral, revealing the fact that other AAs (hydrophobic) are less favorably incorporated into cloud droplets. Finally, the atmospheric aging of AAs has been evaluated by calculating atmospheric lifetimes considering their potential transformation in the cloud medium by biotic or abiotic (mainly oxidation) processes. The most concentrated AAs encountered in our samples present the longest atmospheric lifetimes, and the less dominant ones are clearly efficiently transformed in the atmosphere, potentially explaining their low concentrations. However, this cannot fully explain the relative contribution of several AAs in the cloud samples. This reveals the high complexity of the bio-physico-chemical processes occurring in the multiphase atmospheric environment.

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.

Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.

Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β 2 AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design. Two milliseconds of molecular dynamics simulations of a major drug-target G-protein-coupled receptor (GPCR) has been carried out using Google's Exacycle cloud computing platform. Markov state models were used to aggregate independent simulations into a statistical model that provides an atomistic description of GPCR ligand-modulated activation pathways.