Online bioinformatics teaching practice: Comparison of popular docking programs using SARS-CoV-2 spike RBD-ACE2 complex as a benchmark

Best practices for machine learning in antibody discovery and development

In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Phage-Displayed Mimotopes of SARS-CoV-2 Spike Protein Targeted to Authentic and Alternative Cellular Receptors

The evolution of the SARS-CoV-2 virus during the COVID-19 pandemic was accompanied by the emergence of new heavily mutated viral variants with increased infectivity and/or resistance to detection by the human immune system. To respond to the urgent need for advanced methods and materials to empower a better understanding of the mechanisms of virus’s adaptation to human host cells and to the immuno-resistant human population, we suggested using recombinant filamentous bacteriophages, displaying on their surface foreign peptides termed “mimotopes”, which mimic the structure of viral receptor-binding sites on the viral spike protein and can serve as molecular probes in the evaluation of molecular mechanisms of virus infectivity. In opposition to spike-binding antibodies that are commonly used in studying the interaction of the ACE2 receptor with SARS-CoV-2 variants in vitro, phage spike mimotopes targeted to other cellular receptors would allow discovery of their role in viral infection in vivo using cell culture, tissue, organs, or the whole organism. Phage mimotopes of the SARS-CoV-2 Spike S1 protein have been developed using a combination of phage display and molecular mimicry concepts, termed here “phage mimicry”, supported by bioinformatics methods. The key elements of the phage mimicry concept include: (1) preparation of a collection of p8-type (landscape) phages, which interact with authentic active receptors of live human cells, presumably mimicking the binding interactions of human coronaviruses such as SARS-CoV-2 and its variants; (2) discovery of closely related amino acid clusters with similar 3D structural motifs on the surface of natural ligands (FGF1 and NRP1), of the model receptor of interest FGFR and the S1 spike protein; and (3) an ELISA analysis of the interaction between candidate phage mimotopes with FGFR3 (a potential alternative receptor) in comparison with ACE2 (the authentic receptor).