Mengchen Pu , Kaiyang Cheng, Xiaorong Li , Yucui Xin, Lanying Wei, Sutong Jin, Weisheng Zheng, Gongxin Peng, Qihong Tang, Jielong Zhou, Yingsheng Zhang. Volume 21P5099-51102023 DOI: https://doi.org/10.1016/j.csbj.2023.10.011 Abstract： Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell’s survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanist...

Wenzhi Ma, Yuan Le, Xiaoxuan Shi, Qingbo Xu, Yang Xiao, Yueying Dou, Xiaoman Wang, Wenbiao Zhou, Hongbo Zhang, Bo Huang.  22 August 2023. DOI: https://doi.org/10.1038/s42004-023-00984-5 Abstract: The quest for effective virtual screening algorithms is hindered by the scarcity of training data, calling for innovative approaches. This study presents the use of experimental electron density (ED) data for improving active compound enrichment in virtual screening, supported by ED’s ability to reflect the time-averaged behavior of ligands and solvents in the binding pocket. Experimental ED-based grid matching score (ExptGMS) was developed to score compounds by measuring the degree of matching between their binding conformations and a series of multi-resolution experimental ED grids. The efficiency of ExptGMS was validated using both ...

Yucui Xin, Yingsheng Zhang   07 June 2023  DOI: https://doi.org/10.3389/fonc.2023.1168143 Abstract: Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell’s survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of ta...

Weisheng Zheng, Mengchen Pu, Xiaorong Li， Zhaolan Du, Sutong Jin, Xingshuai Li, Jielong Zhou, Yingsheng Zhang 30 May 2023  DOI: https://doi.org/10.1038/s41598-023-35842-w Abstract： Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from pri...

Feng Zhou, Shiqiu Yin, Yi Xiao, Zaiyun Lin, Weiqiang Fu, and Yingsheng J. Zhang*  May 12, 2023 DOI: https://doi.org/10.1021/acsomega.3c02294 Abstract： Drug design based on kinetic properties is growing in application. Here, we applied retrosynthesis-based pre-trained molecular representation (RPM) in machine learning (ML) to train 501 inhibitors of 55 proteins and successfully predicted the dissociation rate constant (koff) values of 38 inhibitors from an independent dataset for the N-terminal domain of heat shock protein 90α (N-HSP90). Our RPM molecular representation outperforms other pre-trained molecular representations such as GEM, MPG, and general molecular descriptors from RDKit. Furthermore, we optimized the accelerated molecular dynamics to calculate the relative retention time (RT) for the 128 inhibitors of N-HSP90 a...

Zaiyun Lin,* Shiqiu Yin, Lei Shi, Wenbiao Zhou, and Yingsheng John Zhang. March 22, 2023. DOI: https://doi.org/10.1021/acs.jcim.2c01302 Abstract: Retrosynthesis prediction, the task of identifying reactant molecules that can be usedto synthesize product molecules, is a fundamental challenge in organic chemistry and related fields.To address this challenge, we propose a novel graph-to-graph transformation model, G2GT. Themodel is built on the standard transformer structure and utilizes graph encoders and decoders.Additionally, we demonstrate the effectiveness of self-training, a data augmentation technique thatutilizes unlabeled molecular data, in improving the performance of the model. To further enhancediversity, we propose a weak ensemble method, inspired by reaction-type labels and ensemblelearning. This method incorporates beam search, nucleus sampling, and t...