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A Chinese research team has made a groundbreaking discovery using an artificial intelligence (AI) protein language model to explain convergent evolution; the process where different organisms independently develop similar traits to adapt to comparable environments.
Scientists from the Institute of Zoology at the Chinese Academy of Sciences identified a key mechanism behind convergent evolution, revealing the crucial role of high-order protein features in adaptive convergence.
Convergent evolution occurs when separate species evolve similar characteristics despite being distantly related. For example, bats and toothed whales have independently developed echolocation abilities to navigate their surroundings, despite their significant evolutionary differences.
Led by researcher Zou Zhengting, the team introduced a new computational framework named “ACEP.” The innovation lies in its use of a pre-trained AI protein language model, which can interpret deep structural and functional patterns in amino acid sequences.
“A protein language model can understand the deeper structural and functional characteristics behind amino acid sequences,” Zou explained.
According to Zou, the study not only enhances understanding of evolutionary biology but also highlights AI’s growing role in addressing complex biological challenges. The researchers hope their approach will pave the way for wider use of AI in decoding the mysteries of evolution.
