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We present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 s and perform a complete search in less than 1 min. Moreover, the development of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software with high maintainability. Finally, the software is well documented to make it suitable for beginners. The software is available at http://www.github.com/MolecularAI/aizynthfinder .

Background Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to entry for non-domain experts. While data sets of protein sequence and structure exist, they lack certain components critical for machine learning, including high-quality multiple sequence alignments and insulated training/validation splits that account for deep but only weakly detectable homology across protein space. Results We created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships. ProteinNet integrates sequence, structure, and evolutionary information in programmatically accessible file formats tailored for machine learning frameworks. Multiple sequence alignments of all structurally characterized proteins were created using substantial high-performance computing resources. Standardized data splits were also generated to emulate the difficulty of past CASP (Critical Assessment of protein Structure Prediction) experiments by resetting protein sequence and structure space to the historical states that preceded six prior CASPs. Utilizing sensitive evolution-based distance metrics to segregate distantly related proteins, we have additionally created validation sets distinct from the official CASP sets that faithfully mimic their difficulty. Conclusion ProteinNet represents a comprehensive and accessible resource for training and assessing machine-learned models of protein structure.

This inquiry examined whether social networks are associated with wellbeing among older-old people in the same way that they are among younger-old persons. The study focused on family respondents, aged 60 and older, from the second wave of the Survey of Health, Ageing and Retirement in Europe (N=14,728). The statistical analysis regressed two wellbeing measures (the CASP quality of life scale and life satisfaction) on a range of social network variables from three domains: family structure and interaction, social exchange and social engagement. In addition, the inquiry viewed these associations through the lens of age-based interaction terms, controlling for background characteristics, health status and region. The analysis revealed that the associations between subjective wellbeing and social network vary according to age. Among younger-old respondents, aged 60–79, more significant associations were found between social network variables and wellbeing outcomes in comparison to older-old respondents, aged 80 or older. Differences between age groups also emerged with the direction of the associations between social network variables and subjective wellbeing. The study results reveal that social networks do matter in very old age, but not in the same way as among younger-old persons. This finding is one indication of the differences that may emerge between third-age adults and those approaching the fourth age.

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.

Suberin is a specialized cell wall modifying polymer comprising both phenolic-derived and fatty acid-derived monomers, which is deposited in below-ground dermal tissues (epidermis, endodermis, periderm) and above-ground periderm (i.e., bark). Suberized cells are largely impermeable to water and provide a critical protective layer preventing water loss and pathogen infection. The deposition of suberin is part of the skin maturation process of important tuber crops such as potato and can affect storage longevity. Historically, the term “suberin” has been used to describe a polyester of largely aliphatic monomers (fatty acids, ω-hydroxy fatty acids, α,ω-dioic acids, 1-alkanols), hydroxycinnamic acids, and glycerol. However, exhaustive alkaline hydrolysis, which removes esterified aliphatics and phenolics from suberized tissue, reveals a core poly(phenolic) macromolecule, the depolymerization of which yields phenolics not found in the aliphatic polyester. Time course analysis of suberin deposition, at both the transcriptional and metabolite levels, supports a temporal regulation of suberin deposition, with phenolics being polymerized into a poly(phenolic) domain in advance of the bulk of the poly(aliphatics) that characterize suberized cells. In the present review, we summarize the literature describing suberin monomer biosynthesis and speculate on aspects of suberin assembly. In addition, we highlight recent advances in our understanding of how suberization may be regulated, including at the phytohormone, transcription factor, and protein scaffold levels.

The recent series 5 of the Answer Set Programming (ASP) system clingo provides generic means to enhance basic ASP with theory reasoning capabilities. We instantiate this framework with different forms of linear constraints and elaborate upon its formal properties. Given this, we discuss the respective implementations, and present techniques for using these constraints in a reactive context. More precisely, we introduce extensions to clingo with difference and linear constraints over integers and reals, respectively, and realize them in complementary ways. Finally, we empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp] on common language fragments and contrast them to related ASP systems.

Synthesizing complex natural products is a grand challenge in organic chemistry. We present DeepRetro, a significant advancement in computational retrosynthesis that discovers viable synthetic routes for molecules previously considered too complex for automated methods. DeepRetro is a novel, open-source framework that tightly integrates large language models (LLMs), traditional retrosynthetic engines, and expert human feedback into an iterative design loop. Unlike prior approaches that rely on either template-based methods or unconstrained LLMs, our hybrid system combines the precision of templates with the generative flexibility of LLMs, governed by rigorous chemical validity checks and recursive refinement. This system dynamically explores and revises synthetic pathways, guided by algorithmic checks and expert input through an interactive interface. While DeepRetro shows strong performance on standard benchmarks, its main strength is its ability to propose novel, viable pathways for highly complex natural products. Through case studies, we demonstrate how this approach facilitates new total synthesis routes and enhances human-machine collaboration. DeepRetro serves as a working model for applying LLMs to scientific discovery, and we release it as an open-source tool to accelerate progress in drug discovery and materials design.

Exponentially growing bacteria are rarely found in the wild, as microorganisms tend to spend most of their lifetime at stationary phase. Despite this general prevalence of stationary-phase bacteria, they are as yet poorly characterized. Our goal was to quantitatively study this phase by direct observation of single bacteria as they enter into stationary phase and by monitoring their activity over several days during growth arrest. For this purpose, we devised an experimental procedure for starving single Escherichia coli bacteria in microfluidic devices and measured their activity by monitoring the production rate of fluorescent proteins. When amino acids were the sole carbon source, the production rate decreased by an order of magnitude upon entry into stationary phase. We found that, even while growth-arrested, bacteria continued to produce proteins at a surprisingly constant rate over several days. Our identification of this newly observed period of constant activity in nongrowing cells, designated as constant activity stationary phase, makes possible the conduction of assays that require constant protein expression over time, and are therefore difficult to perform under exponential growth conditions. Moreover, we show that exogenous protein expression bears no fitness cost on the regrowth of the population when starvation ends. Further characterization of constant activity stationary phase—a phase where nongrowing bacteria can be quantitatively studied over several days in a reproducible manner—should contribute to a better understanding of this ubiquitous but overlooked physiological state of bacteria in nature.

PurposeA systematic review of the use of the CASP Quality of Life (QoL) scale in older adults was carried out.MethodsArticles were searched using PsycINFO, Web of Science (WOS), Scopus and Medline databases. Observational or experimental studies using any version of the CASP to analyze QoL in adults aged 50 and over and studies focusing on the psychometric properties of the CASP instrument or identifying factors associated with QoL scores. The quality of the studies was assessed using COSMIN and STROBE.ResultsA total of 519,339 participants were considered in the 51 studies selected. The 19- and 12-item CASP versions showed high internal consistency and low-to-moderate convergent validity. Best construct validity was reported for the 12-item short version generating a three-factor model (control/autonomy, pleasure & self-realization) and only modest evidence is provided for their cross-cultural validity. Longitudinal and cross-sectional evidence showed (1) a significant decrease in CASP scores at very old ages; (2) an absence of relationship with gender, which, however, may play a moderating role between QoL and health; (3) significant associations between CASP scores and health, psychosocial and socio-economic outcomes.ConclusionThe quality of the results was hindered by the lack of relevant information in some studies as well as by the proliferation of versions of the instrument. Nevertheless, we conclude that the CASP scale can capture the complex and multidimensional nature of QoL in older adults by reporting satisfaction of needs that go beyond that go beyond those related to health.