Scale-free behaviour of amino acid pair interactions in folded proteins

The protein structure is a cumulative result of interactions between amino acid residues interacting with each other through space and/or chemical bonds. Despite the large number of high resolution protein structures, the "protein structure code" has not been fully identified. Our manuscript presents a novel approach to protein structure analysis in order to identify rules for spatial packing of amino acid pairs in proteins. We have investigated 8706 high resolution non-redundant protein chains and quantified amino acid pair interactions in terms of solvent accessibility, spatial and sequence distance, secondary structure, and sequence length. The number of pairs found in a particular environment is stored in a cell in an 8 dimensional data tensor. When plotting the cell population against the number of cells that have the same population size, a scale free organization is found. When analyzing which amino acid paired residues contributed to the cells with a population above 50, pairs of Ala, Ile, Leu and Val dominate the results. This result is statistically highly significant. We postulate that such pairs form "structural stability points" in the protein structure. Our data shows that they are in buried α-helices or β-strands, in a spatial distance of 3.8-4.3Å and in a sequence distance >4 residues. We speculate that the scale free organization of the amino acid pair interactions in the 8D protein structure combined with the clear dominance of pairs of Ala, Ile, Leu and Val is important for understanding the very nature of the protein structure formation. Our observations suggest that protein structures should be considered as having a higher dimensional organization

Danish Foundation Models

Large language models, sometimes referred to as foundation models, have transformed multiple fields of research. However, smaller languages risk falling behind due to high training costs and small incentives for large companies to train these models. To combat this, the Danish Foundation Models project seeks to provide and maintain open, well-documented, and high-quality foundation models for the Danish language. This is achieved through broad cooperation with public and private institutions, to ensure high data quality and applicability of the trained models. We present the motivation of the project, the current status, and future perspectives.Comment: 4 pages, 2 table

Low birthweight is associated with a higher incidence of type 2 diabetes over two decades independent of adult BMI and genetic predisposition

Aims/hypothesis: Low birthweight is a risk factor for type 2 diabetes. Most previous studies are based on cross-sectional prevalence data, not designed to study the timing of onset of type 2 diabetes in relation to birthweight. We aimed to examine associations of birthweight with age-specific incidence rate of type 2 diabetes in middle-aged to older adults over two decades. Methods: Adults aged 30–60 years enrolled in the Danish Inter99 cohort in 1999–2001 (baseline examination), with information on birthweight from original birth records from 1939–1971 and without diabetes at baseline, were eligible. Birth records were linked with individual-level data on age at diabetes diagnosis and key covariates. Incidence rates of type 2 diabetes as a function of age, sex and birthweight were modelled using Poisson regression, adjusting for prematurity status at birth, parity, polygenic scores for birthweight and type 2 diabetes, maternal and paternal diabetes history, socioeconomic status and adult BMI. Results: In 4590 participants there were 492 incident type 2 diabetes cases during a mean follow-up of 19 years. Type 2 diabetes incidence rate increased with age, was higher in male participants, and decreased with increasing birthweight (incidence rate ratio [95% CI per 1 kg increase in birthweight] 0.60 [0.48, 0.75]). The inverse association of birthweight with type 2 diabetes incidence was statistically significant across all models and in sensitivity analysis. Conclusions/interpretation: A lower birthweight was associated with increased risk of developing type 2 diabetes independent of adult BMI and genetic risk of type 2 diabetes and birthweight

Science for a wilder Anthropocene: synthesis and future directions for trophic rewilding research

Trophic rewilding is an ecological restoration strategy that uses species introductions to restore top-down trophic interactions and associated trophic cascades to promote self-regulating biodiverse ecosystems. Given the importance of large animals in trophic cascades and their widespread losses and resulting trophic downgrading, it often focuses on restoring functional megafaunas. Trophic rewilding is increasingly being implemented for conservation, but remains controversial. Here, we provide a synthesis of its current scientific basis, highlighting trophic cascades as the key conceptual framework, discussing the main lessons learned from ongoing rewilding projects, systematically reviewing the current literature, and highlighting unintentional rewilding and spontaneous wildlife comebacks as underused sources of information. Together, these lines of evidence show that trophic cascades may be restored via species reintroductions and ecological replacements. It is clear, however, that megafauna effects may be affected by poorly understood trophic complexity effects and interactions with landscape settings, human activities, and other factors. Unfortunately, empirical research on trophic rewilding is still rare, fragmented, and geographically biased, with the literature dominated by essays and opinion pieces. We highlight the need for applied programs to include hypothesis testing and science-based monitoring, and outline priorities for future research, notably assessing the role of trophic complexity, interplay with landscape settings, land use, and climate change, as well as developing the global scope for rewilding and tools to optimize benefits and reduce human–wildlife conflicts. Finally, we recommend developing a decision framework for species selection, building on functional and phylogenetic information and with attention to the potential contribution from synthetic biology