Recent Progress in Antibody Epitope Prediction.

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects.

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Design and screening of FAK, CDK 4/6 dual inhibitors by pharmacophore model, molecular docking, and molecular dynamics simulation.

Focal adhesion kinase (FAK) is one kind of tyrosine kinases that modulates integrin and growth factor signaling pathways, which is a promising therapeutic target because of involving in the migration, proliferation and survival of cancer cell. Overexpression and amplification of cyclin-dependent kinase 4/6 (CDK4/6) occur in many cancers and may be the cause of resistance to CDK4/6 inhibitors in preclinical models. The latest research shows that the combination of FAK and CDK4/6 can be dually targeted to enhance the antitumor effects. In this study, FAK and CDK4/6 dual target inhibitors were designed by computer-aided drug design. Seven million molecules were screened by the pharmacophore model and molecular docking. Finally, 6 compounds were obtained. Molecular dynamics simulation of compound 1, 2 and 3 showed that it has good binding stability to both receptors. According to the binding modes of compound 1 with two receptors, corresponding modifications were made, and 7 novel designed compounds were obtained. The docking energy of these novel designed compounds were lower than that of compound 1, and they can be tested in future.Communicated by Ramaswamy H. Sarma.

Identification of inhibitors targeting HIF-2α/c-Myc by molecular docking and MM-GBSA technology.

The treatment of ccRCC by targeting hypoxia-inducible factor HIF-2α is currently a direct and effective method. Studies have shown that HIF-2α and c-Myc cooperate to promote ccRCC tumor progression, and the overexpression of c-Myc is related to the progress and drug resistance of most human cancers. Although HIF-2α and c-Myc are important drug targets, their dual inhibitors are still lacking. We used virtual screening tools (mainly including molecular docking and MM-GBSA technology) to obtain some well-listed compounds that can potentially target HIF-2α and c-Myc and used molecular dynamics simulations to study their binding with these protein systems. Using a structure-based screening scheme, a batch of top-ranking compounds were selected, and their binding affinities were predicted of these compounds were performed. Representative compound C93106, C43257, and C41580 all showed good comprehensive binding score. Our results indicate that the target compounds can all form key interactions with the active site of the protein, and 30 ns molecular dynamic simulation of the complex system indicates a stable binding conformation. This research laid the foundation for the development of more effective and specific HIF-2α and c-Myc dual-target inhibitors.