Computational-Driven Epitope Verification and Affinity Maturation of TLR4-Targeting Antibodies

Intra-articular hydrogel formulation prolongs the in vivo stability of Toll-like receptor antagonistic peptides for rheumatoid arthritis treatment

Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation that primarily affects joint tissue and requires frequent medication. Recently, we developed cyclic phage-display-derived inhibitory peptides (CPs), which act as Toll-like Receptor 4 antagonists. These CPs exhibited therapeutic efficacy against joint diseases by alleviating inflammatory factors. Nonetheless, CP exhibits in vivo instability and a short half-life. Therefore, this study sought to improve the in vivo stability of CP, thereby reducing the frequency of CP administration through the development of an injectable hydrogel depot formulation. To improve in vivo stability, CP was chemically conjugated to hyaluronic acid (HA-CP) and subsequently mixed into a temperature-sensitive hydrogel [methoxy polyethylene glycol-b-poly(ε-caprolactone)-ran-poly(lactide) (PC)] as an injectable depot (PC+(HA-CP)). For comparison, CP was physically mixed with HA and PC (PC+(HA+CP)). Both PC+(HA-CP) and PC+(HA+CP) were found to rapidly form depots upon injection into the joint space. Cell viability assays confirmed the non-toxic nature of PC+(HA-CP) and PC+(HA+CP), whereas both formulations exhibited inhibition of inflammatory factors. Furthermore, PC+(HA-CP) retained CP for a longer duration compared to PC+(HA+CP) in the presence of hyaluronidase and within the RA joint space. Following intra-articular injection, both the PC+(HA-CP) and PC+(HA+CP) depots exhibited reductions in RA symptoms, cartilage regeneration, and suppression of pro-inflammatory cytokine levels. Specifically, by extending the in vivo retention of CP, PC+(HA-CP) demonstrated superior RA treatment efficacy compared to PC+(HA+CP). In conclusion, intra-articular injection of PC+(HA-CP) was validated as an effective strategy for treating RA, owing to its ability to prolong the in vivo retention of CP. This approach holds promise for improving RA management and patient outcomes.

Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations

Anomalous NLRP3 inflammasome responses have been linked to multiple health issues, including but not limited to atherosclerosis, diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disease. Thus, targeting NLRP3 and modulating its associated immune response might be a promising strategy for developing new anti-inflammatory drugs. Herein, we report a computational method for de novo peptide design for targeting NLRP3 inflammasomes. The described method leverages a long-short-term memory (LSTM) network based on a recurrent neural network (RNN) to model a valuable latent space of molecules. The resulting classifiers are utilized to guide the selection of molecules generated by the model based on circular dichroism spectra and physicochemical features derived from high-throughput molecular dynamics simulations. Of the experimentally tested sequences, 60% of the peptides showed NLRP3-mediated inhibition of IL-1β and IL-18. One peptide displayed high potency against NLRP3-mediated IL-1β inhibition. However, NLRC4 and AIM2 inflammasome-mediated IL-1β secretion was uninterrupted by this peptide, demonstrating its selectivity toward the NLRP3 inflammasome. Overall, these results indicate that deep learning and molecular dynamics can accelerate the discovery of NLRP3 inhibitors with potent and selective activity.

Interfacial water molecules contribute to antibody binding to the receptor-binding domain of SARS-CoV-2 spike protein

Antibodies that recognize the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), especially the neutralizing antibodies, carry great hope in the treatment and final elimination of COVID-19. Driven by a synchronized global effort, thousands of antibodies against the spike protein have been identified during the past two years, with the structural information available at atomistic detail for hundreds of these antibodies. We developed an improved molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method including explicitly treated interfacial water to calculate the binding free energy between representative antibodies and the receptor binding domain (RBD) domain of SARS-COV-2 spike proteins. We discovered that explicit treatment of water molecules located at the interface between RBD and antibody effectively improves the results for the WT and variants of concern (VOC) systems. Interfacial water molecules, together with surface and internal water molecules, behave drastically from bulk water and exert peculiar impacts on protein dynamics and energy, and thus warrant explicit treatment to complement implicit solvent models. Our results illustrate the importance of including interfacial water molecules to approach efficient and reliable prediction of binding free energy.Communicated by Ramaswamy H. Sarma.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning

Tomaralimab (OPN-305) is the first humanized immunoglobulin G4 monoclonal antibody against TLR2 and is designed to prevent inflammation that is driven by inappropriate or excessive activation of innate immune pathways. Here, we constructed a homology model of Tomaralimab and its complex with TLR2 at different mapped epitopes and unraveled their behavior at the atomistic level. Furthermore, we predicted a novel epitope (leucine-rich region 9-12) near the lipopeptide-binding site that can be targeted and studied for the utility of therapeutic antibodies. A geometric deep learning algorithm was used to envisage Tomaralimab binding affinity changes upon mutation. There was a significant difference in binding affinity for Tomaralimab following epitope-mutated alanine substitutions of Val266, Pro294, Arg295, Asn319, Pro326, and His372. Using deep learning-based ΔΔG prediction, we computationally contrasted human TLR2-TLR2, TLR2-TLR1, and TLR2-TLR6 dimerization. These results reveal the mechanism that underlies Tomaralimab binding to TLR2 and should help to design structure-based mimics or bispecific antibodies that can be used to inhibit both lipopeptide-binding and TLR2 dimerization.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.