Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions.

Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating the solubility of a model glycoprotein molecule, the carbohydrate-binding module (CBM), through a two-stage process. In the first stage, an approach involving chemical synthesis, comparative analysis, and molecular dynamics simulations of a library of glycoforms was employed to elucidate the effect of different glycosylation patterns on solubility and the key factors responsible for the effect. In the second stage, a predictive mathematical formula, innovatively harnessing machine learning algorithms, was derived to relate solubility to the identified key factors and accurately predict the solubility of the newly designed glycoforms. Demonstrating feasibility and effectiveness, this two-stage approach offers a valuable strategy for advancing glycosylation research, especially for the discovery of glycoforms with increased solubility.

Susceptibilities of Human ACE2 Genetic Variants in Coronavirus Infection.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in more than 235 million cases worldwide and 4.8 million deaths (October 2021), with various incidences and mortalities among regions/ethnicities. The coronaviruses SARS-CoV, SARS-CoV-2, and HCoV-NL63 utilize the angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells. We hypothesized that the genetic variability in ACE2 may contribute to the variable clinical outcomes of COVID-19. To test this hypothesis, we first conducted an in silico investigation of single-nucleotide polymorphisms (SNPs) in the coding region of ACE2. We then applied an integrated approach of genetics, biochemistry, and virology to explore the capacity of select ACE2 variants to bind coronavirus spike proteins and mediate viral entry. We identified the ACE2 D355N variant that restricts the spike protein-ACE2 interaction and consequently limits infection both in vitro and in vivo. In conclusion, ACE2 polymorphisms could modulate susceptibility to SARS-CoV-2, which may lead to variable disease severity. IMPORTANCE There is considerable variation in disease severity among patients infected with SARS-CoV-2, the virus that causes COVID-19. Human genetic variation can affect disease outcome, and the coronaviruses SARS-CoV, SARS-CoV-2, and HCoV-NL63 utilize human ACE2 as the receptor to enter cells. We found that several missense ACE2 single-nucleotide variants (SNVs) that showed significantly altered binding with the spike proteins of SARS-CoV, SARS-CoV-2, and NL63-HCoV. We identified an ACE2 SNP, D355N, that restricts the spike protein-ACE2 interaction and consequently has the potential to protect individuals against SARS-CoV-2 infection. Our study highlights that ACE2 polymorphisms could impact human susceptibility to SARS-CoV-2, which may contribute to ethnic and geographical differences in SARS-CoV-2 spread and pathogenicity.

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.