Biology subjects

Gan, J. L.

Publications and source records attributed to Gan, J. L..

4 recordsLinked to original sources

bioRxiv2020

Identification of Potential Antibody Epitopes in MMP-15

Monoclonal antibody therapy is a well-established cancer treatment paradigm that often targets cancers-pecific cell surface proteins. Matrix Metalloproteinase 15 (MMP-15) is a surface protein implicated in metastasis and angiogenesis, however it is not well characterized. Here we use bioinformatics tools to identify epitopes for drug or diagnostic targeting and elucidate features of this potentially important protein in metastatic processes. We identified conserved regions in MMP-15 as well as unique, variable peptide regions compared to other Membrane Type MMPs (MT-MMP). Conservation of catalytic and hemopexin domains in MMP-15 imply functional importance, however, their similarity to other MT-MMPs discourage use as target epitopes. Our analyses also identified an MMP-15 peptide that was highly divergent from other MMPs, suggesting that it may serve as an appropriate specific epitope for a specific antibody drug. Thus, we were able to elucidate features and potential unique epitopes of MMP-15 for use in further antibody discovery and targeting.

molecular biology↗

bioRxiv2020

Diversity of ACE2 and its interaction with SARS-CoV-2 receptor binding domain

COVID-19, the clinical syndrome caused by the SARS-CoV-2 virus, has rapidly spread globally causing tens of millions of infections and over a million deaths. The potential animal reservoirs for SARS-CoV-2 are currently unknown, however sequence analysis has provided plausible potential candidate species. SARS-CoV-2 binds to the angiotensin I converting enzyme 2 (ACE2) to enable its entry into host cells and establish infection. We analyzed the binding surface of ACE2 from several important animal species to begin to understand the parameters for the ACE2 recognition by the SARS-CoV-2 spike protein receptor binding domain (RBD). We employed Shannon entropy analysis to determine the variability of ACE2 across its sequence and particularly in its RBD interacting region, and assessed differences between various species ACE2 and human ACE2. As cattle are a known reservoir for coronaviruses with previous human zoonotic transfer, and has a relatively divergent ACE2 sequence, we compared the binding kinetics of bovine and human ACE2 to SARS-CoV-2 RBD. This revealed a nanomolar binding affinity for bovine ACE2 but an approximate ten-fold reduction of binding compared to human ACE2. Since cows have been experimentally infected by SARS-CoV-2, this lower affinity sets a threshold for sequences with lower homology to human ACE2 to be able to serve as a productive viral receptor for SARS-CoV-2.

molecular biology↗

bioRxiv2020

Benchmarking ensemble docking methods as a scientific outreach project

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that CatS is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

scientific communication and education↗

bioRxiv2020

Predicting Metastasis with a Novel Biophysical Cell-Adhesion Force Technique

Metastasis is widely accepted to be responsible for approximately 90% of all cancer deaths. Current research on metastasis prediction often centers on gene sequencing; however, these analyses must account for the complexity of gene regulation and rely on comprehensive datasets. To investigate the process from a simpler, non-genomic angle, some studies indicate differences in cell adhesion force, an important physical process in metastasizing cells. However, cell adhesion force methods tend to focus on cell population approaches and therefore have their drawbacks in cost or efficiency, rendering them impractical outside a research setting. In this work, we test a novel and inexpensive bead-pipette assay to investigate the adhesion forces of non-metastatic NIH3T3 cells and mutated RasV12 cells, a metastatic model cell line. Control cells and RasV12 cells were evaluated with wound healing, spreading area, and focal adhesion (FA) analysis assays. Then cells were tested by the novel bead-pipette assay, which uses a fibronectin-coated bead and a glass micropipette to measure cell adhesion force using Hookes law. The RasV12 cells had faster migration, polarized cell shape, and smaller FA area than control cells. The RasV12 cells also exerted higher adhesion forces than control cells and a potential force threshold was determined for distinguishing metastatic cells through a Receiver Operating Characteristic (ROC) curve. An ROC curve was computed for all other assays and the bead-pipette assay was shown to perform higher as a classifier than other assays. The RasV12 cells had increased metastatic potential compared to control. The novel bead-pipette assay showed potential as a classifier for determining metastasizing cells from non-metastatic cells. With further work, it may serve as a clinical diagnostic tool for cancer patients or as a testbed to be used in the development of anti-metastatic drugs.

biophysics↗