Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants

vaccinees. For the majority of participants, further dissecting the heteroge- neity of avidities of the R21 immunogen-induced antibodies revealed two k d components, 10 (cid:1) 4 s (cid:1) 1 (higher avidity component) and > 10 (cid:1) 2 s (cid:1) 1 (lower avidity component). Interestingly, the median antigen occupancy of higher avidity component for CSP, (NANP)6 and (NPNA)3 binding, i.e

vaccinees. For the majority of participants, further dissecting the heterogeneity of avidities of the R21 immunogen-induced antibodies revealed two k d components, 10 À4 s À1 (higher avidity component) and >10 À2 s À1 (lower avidity component). Interestingly, the median antigen occupancy of higher avidity component for CSP, (NANP)6 and (NPNA)3 binding, i.e., the dominant fraction (82-99% occupancy, mean value 95%), was greater in the protected than non-protected vaccinees. These results suggest that high magnitude and avidity pAbs contribute to the R21 vaccine protection against Pf sporozoite infection.

708-Pos
Analysis of the conserved and mutated amino acid sequences in the spike protein enhances the understanding of phylognetic relationship among coronavirus variants from the wild type Asmaa Awan, Roshan Paudel. School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD, USA. In the light of the COVID-19 pandemic, an elaborative computational analysis was conducted regarding coronaviruses, their phylogeny, and the different strains of the pathogen responsible for the disease, SARS-CoV-2. In the front, this disease looks like the common cold. However, it can lead to acute respiratory failure, septic shock, and organ failure. Like the Spanish flu pandemic in 1918, the COVID-19 pandemic also became the cause of millions of lives lost across the globe. Coronaviruses consist of four basic proteins -spike, nucleocapsid, membrane, and envelope proteins. Spike proteins (S) are structures protruding from the surface of the virus and facilitate its entry into the the host cells via interaction with the ACE-2 receptors who are also found on the surface of the host cells. For this research work, the entire sequence of the spike protein was considered, and a table was created carrying out the comparison between the spike protein sequences of the WT and 13 different variants (Alpha, Beta, Gamma, Kappa, Delta, Mu, Epsilon, Lambda, Omicron, Mu, Eta, Zeta, and Theta). This table helped detect the conserved parts of spike protein sequence across all variants, conserved mutations as well as mutations unique to certain variants. Findings from the table are then used to study phylogenetic trees which explain the emergence of new coronavirus variants and their genetic distances from the WT.

709-Pos
Morphodynamic and motility feature-based deep learning classification for subtypes of cancer-associated fibroblasts Minwoo Kang, D. Somayadineshraj, Chanhong Min, Jennifer H. Shin. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. The cancer-associated fibroblasts (CAFs) have risen to prominence as key players in cancer progression. It is now widely appreciated that CAFs are composed of heterogeneous populations, either pro-tumorigenic or antitumorigenic. In addition, a growing body of evidence supports the multifaceted nature of CAFs. Therefore, researchers in the field have been trying to identify subtypes of CAFs with molecular markers. However, those markers cannot accurately classify the subpopulations and can be co-expressed in different subtypes. In order to utilize CAFs as a target for cancer treatment, issues with subtypes of CAFs must be resolved such that specific protumorigenic subtypes can be suppressed or reprogrammed to antitumorigenic ones. The morphology and the motile characteristics of CAFs result from gene expression combinations. Thus, those characteristics can be holistic readouts of CAFs. Unlike biomolecular analysis, a fixed cellbased end-point assay, morphology or motility features of cells can be traced with live-cell imaging. Those features can provide information on the dynamic changes of CAFs. Here, in order to comprehensively identify subtypes CAFs, we adopt a deep learning-based cell classification strategy. Using multiple unsupervised and supervised machine learning algorithms, we extract the morphodynamic and motility features of cells from labelfree live-cell imaging data of CAFs. To this end, we established in vitro breast CAFs, which were fibroblasts cocultured with two different breast cancer cell lines with different aggressiveness. As a result, we show that the morphodynamic and motility features can successfully classify heterogeneous subpopulations of cells.

710-Pos
Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants Eric Wang, Arup K. Chakraborty. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2' cleavage site and fusion peptide as potential pan-coronavirus vaccine targets.

711-Pos
Towards generalizable prediction of antibody thermostability using machine learning on sequence and structure features Ameya Uddhav Harmalkar 1 , Jeffrey J. Gray 1 , Kathy Wei 2 . 1 Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA, 2 Department of Biochemistry, University of Washington, Seattle, WA, USA. Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious, and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches -one with pre-trained language models (PTLM), and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features -to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (blind) sequences, we show that a sufficiently simple CNN model (r = 0.40) performs better than general pre-trained language (r = 0.15) and as well as an antibodyspecific language model (r = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physico-chemical properties of scFvs can have potential applications in optimizing large-scale production of msAbs.