Machine learning-assisted directed protein evolution with combinatorial libraries
Zachary Wu, S. B. Jennifer Kan, Russell D. Lewis, Bruce J. Wittmann, Frances H. Arnold
备注：
Corrected best S-selective variant sequence in Figure 4. Corrected less R-selective variant sequences from Round II Input library in Table 2 and Supp Table 4. Corrections may also be found on PNAS version https://www.pnas.org/content/early/2019/12/26/1921770117
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning in the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine learning models trained on tested variants provide a fast method for testing sequence space computationally. We validate this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (stereodivergence) of a new-to-nature carbene Si-H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee. By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries
CUHK（香港中文大学）；College of Chemistry and Chemical Engineering, Lanzhou University（兰州大学化学化工学院）；HKUST（香港科技大学）；College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院）
针对 DEL 筛选读取计数噪声问题，提出结合排序损失、自训练与一致性损失的 DEL-Ranking 去噪框架，并发布三个多表征数据集，显著提升结合亲和力预测与零样本泛化能力。
Improving homology-directed repair by small molecule agents for genetic engineering in unconventional yeast? -- Learning from the engineering of mammalian systems
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Bioengineering, University of Pennsylvania（宾夕法尼亚大学生物工程系）；Department of Computer and Information Science, University of Pennsylvania（宾夕法尼亚大学计算机与信息科学系）；Centre for Computational Biology, Duke-NUS Medical School（杜克-新加坡国立大学医学学校计算生物学中心）
ProteinGuide: On-the-fly property guidance for protein sequence generative models
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学系，加州大学伯克利分校）；Center for Computational Biology, University of California, Berkeley（计算生物学中心，加州大学伯克利分校）；Department of Molecular and Cell Biology, University of California, Berkeley（分子与细胞生物学系，加州大学伯克利分校）；Innovative Genomics Institute, University of California, Berkeley（创新基因组研究所，加州大学伯克利分校）；Howard Hughes Medical Institute, University of California, Berkeley（霍华德·休斯医学研究所，加州大学伯克利分校）
