1.RNA structural and functional studies

1.     Non-coding RNA is currently an active area of fundamental research because messenger RNAs (mRNAs) used for coding proteins only account for 1.5% of the human genome, whereas non-coding RNAs occupy a remarkable 75%. So far, only a few non-coding RNA functions have been identified. They exist in almost all biological processes and play key roles in many diseases including cancer ^[1]. However, for the vast majority of non-coding RNAs, we know very little, mainly because of the lack of structural information as structures determine functions. Without structures, we are clueless in analyzing their functions. Our research team developed the world’s first end-to-end deep learning technique for predicting RNA secondary structure ^[2], a machine learning method to predict functional long non-coding RNA ^[3], a combined computational and experimental approach for inferring RNA secondary structure by using deep mutations ^[4], and a refinement protocol of model RNA structures ^[5]. The ultimate goal of our research is to develop methods for sequence- and structure-based RNA functional design.

2. 【1】B. Zhou, B. Ji, K. Liu, G. Hu, F. Wang, Q. Chen, R. Yu, P. Huang, J. Ren, C. Guo, H. Zhao, H. Zhang, D. Zhao, Z. Li, Q. Zeng, J. Yu, Y. Bian, Z. Cao, S. Xu, Y. Yang, Y. Zhou*, and J. Wang*, EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments, Nucleic Acids Research (Database Issue) 49, D86–D91 (2021).

3. 【2】J. Singh, J. Hanson, K. Paliwal, and Y. Zhou, RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning, Nature Communications 10, 5407 (2019).

4. 【3】B. Zhou, Y. Yang, J. Zhan, X. Dou, J. Wang*, and Y. Zhou*, Predicting functional long non-coding RNAs validated by low throughput experiments, RNA Biology, 16: 1555-1564 (2019).

5. 【4】Z. Zhang, P. Xiong, T. Zhang, J. Wang, J. Zhan, and Y. Zhou, Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity, Nucleic Acids Research, 48:1451-1465 (2020).

6. 【5】X. Peng, R. Wu, J. Zhan* and Y. Zhou*, Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement，Nature Communications 12, Article number: 2777 (2021)

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2.Biological drug development and nanobody design

   The seemly minor (one-thousandth) difference between individual genomes leads considerable differences in immune responses, disease susceptibility, and drug efficacies. These personal differences require precise diagnosis tools and personalized vaccines or individualized therapeutic drugs. Using artificial intelligence and deep learning methods to mine biological and medical big data is the only way to realize precision medicine. New drug development and precision medicine are one of the priorities of China’s 13th Five-Year National Strategic Emerging Industry Development Plan. Biological drugs (small peptides, RNA, proteins, antibodies, etc.) are becoming more attractive because of having few side effects and high specificity. At present, both small molecules and biological drugs have drug resistance problems in anti-virus, anti-bacterial, anti-fungal, and anti-cancer therapeutics. The main reason is that most drugs used in clinical practice are acting on the surface of their target structures. Random mutations as well as naturally occurring variations on the surface of target structures can lead to drug resistance. Our research group employed computational methods to predict small self-inhibiting peptides that destroy the target structure. These peptides are found difficult for bacteria to develop resistance ^[6]. In addition, protein design including nanobody design has increasingly become a new tool for biopharmaceuticals ^[7]. The goal is to accelerate the discovery and application of new drugs through combining computational and experimental studies.

【6】J. Zhan, H. Jia, E. A. Semchenko, Y. Bian, A. M. Zhou, Z. Li, Y. Yang, J. Wang, S. Sarkar, M. Totsika, H. Blanchard, F. E.-C. Jen, Q. Ye, T. Haselhorst, M. P. Jennings, K. L. Seib, and Y. Zhou, Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria; a new strategy to generate antimicrobial peptides, FASEB J. , 33: 2095–2104 (2019).

【7】Z. Li, Y. Yang, J. Zhan, L. Dai and Y. Zhou, Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects, Ann. Rev. Biophysics 42, 315-335 (2013).

3.Biomarker detection and research instrument development

  Fast, inexpensive, highly sensitive, and highly accurate detection of biomarkers is still a challenging problem. The instruments used in academic and clinical purposes are monopolized by a few international companies. Our research group has experience in designing high-sensitivity sensors ^[8], and has been responsible for software development, data analysis, and AI algorithm projects for many years. Currently, we are interested in the development of a new generation of western blot imaging system.

【8】S. Xu, J. Zhan, B. Man, S. Jiang, W. Yue, S. Gao, C. Guo, H. Liu, Z. Li, J. Wang, and Y. Zhou, Real-time reliable determination of binding kinetics of DNA hybridization using a multi-channel graphene biosensor, Nature Communications 8, 14902 (2017).