Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (AF), this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning (ML) model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences. We tested the model's capabilities on 100 proteins whose structures had not yet been predicted by AF and found agreement with our local prediction in 92% cases. From the point of view of structural biology, we found that all potentially knotted proteins predicted by AF can be classified only into 17 families. This allows us to discover the presence of unknotted proteins in families with a highly conserved knot. We found only three new protein families: UCH, DUF4253, and DUF2254, that contain both knotted and unknotted proteins, and demonstrate that deletions within the knot core could potentially account for the observed unknotted (trivial) topology. Finally, we have shown that in the majority of knotted families (11 out of 15), the knotted topology is strictly conserved in functional proteins with very low sequence similarity. We have conclusively demonstrated that proteins AF predicts as unknotted are structurally accurate in their unknotted configurations. However, these proteins often represent nonfunctional fragments, lacking significant portions of the knot core (amino acid sequence).

Assessment of Exposure of Elementary Schools to Traffic Pollution by GIS Methods.

AIM: The susceptibility of children to polluted air has been pointed out several times in the past. Generally, children suffer from higher exposure to air pollutants than adults because of their higher physical activity, higher metabolic rate and the resultant increase in minute ventilation. The aim of this study was to examine the exposure characteristics of public elementary schools in Prague (the capital of the Czech Republic). METHODS: The exposure was examined by two different methods: by the proximity of selected schools to major urban roads and their location within the modeled urban PM10 concentration fields. We determined average daily traffic counts for all roads within 300 m of 251 elementary schools using the national road network database and geographic information system and calculated by means of GIS tools the proximity of the schools to the roads. In the second method we overlapped the GIS layer of predicted annual urban PM10 concentration field with that of geocoded school addresses. RESULTS: The results showed that 208 Prague schools (almost 80%) are situated in a close proximity (<300 m) of roads exhibiting high traffic loads. Both methods showed good agreement in the proportion of highly exposed schools at risk; however, we found significant differences in the locations of schools at risk determined by the two methods. CONCLUSION: We argue that results of similar proximity studies should be treated with caution before they are used in risk based decision-making process, since different methods may provide different outcomes.