AI Article Synopsis

  • Artificial intelligence has greatly advanced protein structure prediction, notably through DeepMind's AlphaFold2, which can accurately predict 3D structures of proteins comparable to experimental methods.
  • This progress allows for a deeper understanding of proteins, enhancing drug discovery and various biological applications.
  • However, challenges remain in areas like predicting complex protein structures and their folding pathways, highlighting the need for continued improvements in the field.

Article Abstract

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

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Source
http://dx.doi.org/10.1021/acs.jcim.3c01324DOI Listing

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