UROP Proceedings 2021-22

School of Science Department of Chemistry 6 The Impact of Spike Mutations on SARS-CoV-2 Neutralization Supervisor: SU Haibin / CHEM Student: LEUNG Cheuk Fung Alvin / SSCI Course: UROP1100, Summer The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for cell entry by binding to human angiotensin-converting enzyme 2 (hACE2). Despite being distal from the S-hACE2 interface, the two subunit 1 (S1) subdomains (SD1 and SD2) and subunit 2 (S2) play important roles in viral functions via allosteric modulation of hACE2-binding and mediation of membrane fusion respectively. Hence, mutations in these regions confer significant impacts to SARS-CoV-2 functions. Here, the time-resolved dynamic expedition of leading mutations (deLemus) method is applied to screen for sites of interest (SOIs) in aforementioned domains based on their weighted L-index scores derived from single amino acid polymorphism (SAP) analyses. These findings would allow the potential projection of S evolution trajectory. The Impact of Spike Mutations on SARS-CoV-2 Neutralization Supervisor: SU Haibin / CHEM Student: TSANG Kwok Kiu / CHEM Course: UROP1100, Spring Amidst the CoVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has undergone rapid, ongoing genomic evolution, forming numerous variants that have developed increased transmissibility and antibody evasion. The spike glycoprotein of the virus is crucial for antibody development and is also integral in the receptor recognition and cell membrane fusion process, hence monitoring and prediction of mutations for this protein is of importance. Here, we propose a method of identifying new active sites of mutation for the spike glycoprotein by employing a direct counting method, used in conjunction with statistic coupling analysis (SCA), for continuous monitoring of its mutation patterns. The identified new active sites of interest (SOIs) show promise in predicting the emergence of future variants of concern. Text Mining of Synthesis Methods of Metal Organic Framework Supervisor: SU Haibin / CHEM Student: SIU Chun Hey / SSCI Course: UROP1000, Summer Metal Organic Frameworks (MOFs) has gained rising interests in recent studies for their huge internal surface area and astonishingly high porosity which, coupled with the extremely flexible nature of its organic and inorganic constituents, revealed countless possibilities for a plethora of novel applications. However, there is still a lack of understanding on the specifications of the synthesis of MOFs. This UROP project works on overcoming this limitation by extracting and evaluating synthesis methods and conditions from thousands of studies. Via an automation software that allows customization of document digitization workflow, Text Mining and Graph Mining are performed extensively on selected studies to gather a wide range of data for MOFs.