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ABSTRACT The thermal environment that organisms experience can affect many aspects of their phenotype. As global temperatures become more unpredictable, it is imperative that we understand the molecular mechanisms by which organisms respond to variable, and often transient, thermal environments. Beyond deciphering the mechanisms through which organisms respond to temperature, we must also appreciate the underlying variation in temperature-dependent processes, as this variation is essential for understanding the potential to adapt to changing climates. In this Commentary, we use temperature-dependent sex determination as an example to explore the mechanistic processes underlying the development of temperature-sensitive phenotypes. We synthesize the current literature on how variable thermal conditions affect these processes and address factors that may limit or allow organisms to respond to variable environments. From these examples, we posit a framework for how the field might move forward in a more systematic way to address three key questions: (1) which genes directly respond to temperature-sensitive changes in protein function and which genes are downstream, indirect responders?; (2) how long does it take different proteins and genes to respond to temperature?; and (3) are the experimental temperature manipulations relevant to the climate the organism experiences or to predicted climate change scenarios? This approach combines mechanistic questions (questions 1 and 2) with ecologically relevant conditions (question 3), allowing us to explore how organisms respond to transient thermal environments and, thus, cope with climate change.
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Adapting Protein Language Models for Explainable Fine-Grained Evolutionary Pattern Discovery
Organisms that live in different environments face different evolutionary pressures. As such, organisms that have more successful phenotypes reproduce more frequently, but differing selective pressures acting at the organismal level can influence genes, and thus proteins. Understanding how proteins adapt across environments may therefore be useful in engineering proteins for specific environments as well as to improve our understanding of basic biology. In this work, we explicitly compare homologous (read: paired) proteins from different environments. While previous studies have explored the relevant evolutionary pressures in one of these environments [11], [17] and genomic responses to those pressures [1], [28], no prior computational study of their proteins has been performed. We apply ESM-2 [20] and although there is no signal in our negative control (two divergent yeast strains) as expected, we obtain near perfect prediction accuracy for our selected environmental gradient–the well-established subsurface vs. surface biome. We further show that ESM-2 is able to capture relevant fine-grained biological patterns in its embedding space, even in its smallest model. Significantly, we demonstrate that these embeddings can be interpreted using a novel visualization pipeline built using explainable AI techniques.
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- Award ID(s):
- 2145434
- PAR ID:
- 10492130
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- ISBN:
- 979-8-3503-3748-8
- Page Range / eLocation ID:
- 2609 to 2616
- Format(s):
- Medium: X
- Location:
- Istanbul, Turkiye
- Sponsoring Org:
- National Science Foundation
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