TTIC Professor Xu Jinbo gives lecture at PKU on Artificial Intelligence and Protein Folding
Nov 05, 2021
Peking University, November 5, 2021:
On October 27, Professor Xu Jinbo from Toyota Technological Institute
at Chicago (TTIC) gave an academic lecture on Artificial Intelligence
and Protein Folding as invited by the Peking University (PKU) Global
Fellowship program.
Hosted by Xiaoliang Sunney Xie, a Lee Shau-kee Professor at PKU, the
lecture was held in the Yenching Academy lecture hall and was
simultaneously live streamed on several online platforms receiving a
total of 145,000 video views.
Xu Jinbo
Xiaoliang Sunney Xie hosting
Xu briefly introduced the background knowledge of the use of artificial
intelligence to predict protein structure at the beginning of the
lecture. He said that there are great challenges faced by traditional
prediction methods, because the protein contains thousands of atoms with
high degrees of freedom and the energy picture is not smooth, making it
difficult to optimize.
Xu also reviewed the development history of protein structure
prediction. He noted that the prediction approach before 2016 was
inefficient and consumed a large amount of computing resources. To
promote the efficiency, the scientists changed their research thinking:
starting from the protein amino acid sequence that needs to be
predicted, searching related databases to obtain multiple-sequence
alignments (MSAs), and then obtaining the relationship matrix of amino
acid residue pairs (such as contact matrix and distance matrix), and
finally predict the structure.
In 2016, Xu’s team developed the RaptorX-Contact method based on ResNet.
This method, for the first time, proved the feasibility of deep
learning method on protein structure prediction. It can also be used to
predict the structure of membrane protein and protein interaction.
From 2017 to 2019, Xu’s team successfully achieved a leap from contact
matrix prediction to distance matrix prediction, making protein
structure prediction more accurate.
Regarding the future development trend of artificial intelligence
predicting protein structure, Xu believes that it will mainly focus on
the better use of sequence and structural information and new deep
learning network architectures. He also believes that the newly released
AlphaFold2 is superior to other algorithms at the residue level. But
for proteins with high molecular weight and multiple domains, it is
still challenging to accurately predict the spatial position
relationship between their domains.
The Q&A session
After the lecture, Xu and the on-site teachers and students had a lively
exchange and discussion on issues related to artificial intelligence
prediction of protein structure.
Written by: See Tianai
Edited by: Ye Yimeng
Source: PKU News (Chinese)
