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Protein design based on AlphaFold2

Project

Project Details

Program
Computer Science
Field of Study
AI+Bioinformatics
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
https://www.kaust.edu.sa/en/study/faculty/xin-gao
Center Affiliation
Computational Bioscience Research Center

Project Description

AlphaFold2 has made the biggest breakthrough in computational biology and has created the hope to be able to not only solve the forward protein 3D structure prediction problem, but also target a more challenging but more practically useful inverse problem, protein design. Protein design is the core problem in protein engineering and optimization, with a very wide range of applications in enzyme optimization, antibody design, drug development, etc. This project is designed to leverage the power of AlphaFold2 to target the protein design problem through developing AI methods.

About the Researcher

Xin Gao
Professor, Computer Science Program
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • Ph.D. University of Waterloo, Canada, 2009
  • B.S. Tsinghua University, 2004

Research Interests

Gao's research lies at the intersection between computer science and biology. His work has two main focuses: 1) developing theory and methodology in the fields of machine learning and algorithms; and 2) solving key open problems in biological and medical fields through building computational models, developing machine-learning techniques, and designing effective and efficient algorithms. In particular, he aims to solve problems that occur along the path from protein amino acid sequences to their three-dimensional structures and functions that ultimately lead to their undesirable expression in complex biological networks.

Selected Publications

  • A. Abbas, X. Guo, B. Jing, and X. Gao. An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming. Journal of Biomolecular NMR. (2014). 59(2): 75-86.
  • H. Kuwahara, M. Fan, S. Wang, and X. Gao. A framework for scalable parameter estimation of gene circuit models using structural information. Bioinformatics. (2013). 29(13): i98-i107.
  • B. Xie, B. Jankovic, V. Bajic, L. Song, and X. Gao. Poly(A) motif prediction using spectral latent features from human DNA sequences. Bioinformatics. (2013). 29(13): i316-i325.
  • M. Maadooliat, X. Gao, and J. Huang. Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles. Briefings in Bioinformatics. (2013). 14(6): 724-736.
  • Z. Liu, A. Abbas, B. Jing, and X. Gao. WaVPeak: picking NMR peaks through wavelet transform and volume-based filtering. Bioinformatics (2012), 28(7): 914-920.
  • B. Alipanahi, X. Gao, E. Karakoc, L. Donaldson, A. Gutmanas, C. Arrowsmith, and M. Li. PICKY: a novel SVD-based NMR spectra peak picking method. Bioinformatics. (2009). 25(12): i268-i275.
  • X. Gao, D. Bu, J. Xu, and M. Li. Improving consensus contact prediction via server correlation reduction. BMC Structural Biology, 2009, 9:28.

Desired Project Deliverables

An end-to-end learning pipeline for protein design.

Recommended Student Background

AI
Bioinformatics
Machine learning
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