More efficient screening of protein-protein complex model structures for reducing the number of candidates

An immunoinformatic approach to assessing the immunogenic capacity of alpha-neurotoxins in elapid snake venoms

Introduction: Most elapid snakes produce venoms that contain alpha-neurotoxins (α-NTXs), which are proteins that cause post-synaptic blockade and paralysis in snakebite envenoming. However, existing elapid antivenoms are known for their low potency in neutralizing the neurotoxic activity of α-NTXs, while the immunological basis has not been elucidated. Methods: In this study, a structure-based major histocompatibility complex II (MHCII) epitope predictor of horse (Equus caballus), complemented with DM-editing determinant screening algorithm was adopted to assess the immunogenicity of α-NTXs in the venoms of major Asiatic elapids (Naja kaouthia, Ophiophagus hannah, Laticauda colubrina, Hydrophis schistosus, Hydrophis curtus). Results: The scoring metric M₂R, representing the relative immunogenic performance of respective α-NTXs, showed all α-NTXs have an overall low M₂R of <0.3, and most of the predicted binders feature non-optimal P1 anchor residues. The M₂R scores correlate strongly (R² = 0.82) with the potency scores (p-score) generated based on the relative abundances of α-NTXs and the neutralization potency of commercial antivenoms. Discussion: The immunoinformatic analysis indicates that the inferior antigenicity of α-NTXs is not only due to their small molecular size but also the subpar immunogenicity affected by their amino acid composition. Structural modification with conjugation and synthetic epitope as immunogen may potentially enhance the immunogenicity for improved antivenom potency against α-NTXs of elapid snakes.

Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction

The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).