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Abstract According to the Principle of Minimal Frustration, folded proteins can only have a minimal number of strong energetic conflicts in their native states. However, not all interactions are energetically optimized for folding but some remain in energetic conflict, i.e. they are highly frustrated. This remaining local energetic frustration has been shown to be statistically correlated with distinct functional aspects such as protein-protein interaction sites, allosterism and catalysis. Fuelled by the recent breakthroughs in efficient protein structure prediction that have made available good quality models for most proteins, we have developed a strategy to calculate local energetic frustration within large protein families and quantify its conservation over evolutionary time. Based on this evolutionary information we can identify how stability and functional constraints have appeared at the common ancestor of the family and have been maintained over the course of evolution. Here, we present FrustraEvo, a web server tool to calculate and quantify the conservation of local energetic frustration in protein families. 

Computational thinking has widely been recognized as a crucial skill for engineers engaged in problem-solving. Multidisciplinary learning environments such as integrated STEM courses are powerful spaces where computational thinking skills can be cultivated. However, it is not clear the best ways to integrate computational thinking instruction or how students develop computational thinking in those spaces. Thus, we wonder: To what extent does engaging students in integrated engineering design and physics labs impact their development of computational thinking? We have incorporated engineering design within a traditional introductory calculus-based physics lab to promote students’ conceptual understanding of physics while fostering scientific inquiry, mathematical modeling, engineering design, and computational thinking. Using a generic qualitative research approach, we explored the development of computational thinking for six teams when completing an engineering design challenge to propose an algorithm to remotely control an autonomous guided vehicle throughout a warehouse. Across five consecutive lab sessions, teams represented their algorithms using a flowchart, completing four iterations of their initial flowchart. 24 flowcharts were open coded for evidence of four computational thinking facets: decomposition, abstraction, algorithms, and debugging. Our results suggest that students’ initial flowcharts focused on decomposing the problem and abstracting aspects that teams initially found to be more relevant. After each iteration, teams refined their flowcharts using pattern recognition, algorithm design, efficiency, and debugging. The teams would benefit from having more feedback about their understanding of the problem, the relevant physics concepts, and the logic and efficiency of the flowcharts 

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, ɑ and β globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens. 

Abstract Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein–protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions. 

ABSTRACT The SEDIGISM (Structure, Excitation and Dynamics of the Inner Galactic Interstellar Medium) survey used the APEX telescope to map 84 deg2 of the Galactic plane between ℓ = −60° and +31° in several molecular transitions, including 13CO (2 – 1) and C18O (2 – 1), thus probing the moderately dense (∼103 cm−3) component of the interstellar medium. With an angular resolution of 30 arcsec and a typical 1σ sensitivity of 0.8–1.0 K at 0.25 km s−1 velocity resolution, it gives access to a wide range of structures, from individual star-forming clumps to giant molecular clouds and complexes. The coverage includes a good fraction of the first and fourth Galactic quadrants, allowing us to constrain the large-scale distribution of cold molecular gas in the inner Galaxy. In this paper, we provide an updated overview of the full survey and the data reduction procedures used. We also assess the quality of these data and describe the data products that are being made publicly available as part of this First Data Release (DR1). We present integrated maps and position–velocity maps of the molecular gas and use these to investigate the correlation between the molecular gas and the large-scale structural features of the Milky Way such as the spiral arms, Galactic bar and Galactic Centre. We find that approximately 60 per cent of the molecular gas is associated with the spiral arms and these appear as strong intensity peaks in the derived Galactocentric distribution. We also find strong peaks in intensity at specific longitudes that correspond to the Galactic Centre and well-known star-forming complexes, revealing that the 13CO emission is concentrated in a small number of complexes rather than evenly distributed along spiral arms.