Binding site plasticity regulation of the FimH catch-bond mechanism

Load-based divergence in the dynamic allostery of two TCRs recognizing the same pMHC

Increasing evidence suggests that mechanical load on the αβ T-cell receptor (TCR) is crucial for recognizing the antigenic peptide-bound major histocompatibility complex (pMHC) molecule. Our recent all-atom molecular dynamics (MD) simulations revealed that the inter-domain motion of the TCR is responsible for the load-induced catch bond behavior of the TCR-pMHC complex and peptide discrimination (Chang-Gonzalez et al., 2024). To further examine the generality of the mechanism, we perform all-atom MD simulations of the B7 TCR under different conditions for comparison with our previous simulations of the A6 TCR. The two TCRs recognize the same pMHC and have similar interfaces with pMHC in crystal structures. We find that the B7 TCR-pMHC interface stabilizes under ∼15 pN load using a conserved dynamic allostery mechanism that involves the asymmetric motion of the TCR chassis. However, despite forming comparable contacts with pMHC as A6 in the crystal structure, B7 has fewer high-occupancy contacts with pMHC and exhibits higher mechanical compliance during the simulation. These results indicate that the dynamic allostery common to the TCRαβ chassis can amplify slight differences in interfacial contacts into distinctive mechanical responses and nuanced biological outcomes.

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Simulation-Guided Conformational Space Exploration to Assess Reactive Conformations of a Ribozyme

Self-splicing ribozymes are small ribonucleic acid (RNA) enzymes that catalyze their own cleavage through a transphosphoesterification reaction. While this process is involved in some specific steps of viral RNA replication and splicing, it is also of importance in the context of the (putative) first autocatalytic RNA-based systems that could have preceded the emergence of modern life. The uncatalyzed phosphoester bond formation is thermodynamically very unfavorable, and many experimental studies have focused on understanding the molecular features of catalysis in these ribozymes. However, chemical reaction paths are short-lived and not easily characterized by experimental approaches, so molecular simulation approaches appear as an ideal tool to unveil the molecular details of the reaction. Here, we focus on the model hairpin ribozyme. We show that identifying a relevant initial conformation for reactivity studies, which is frequently overlooked in mixed quantum-classical studies that predominantly concentrate on the chemical reaction itself, can be highly challenging. These challenges stem from limitations in both available experimental structures (which are chemically altered to prevent self-cleavage) and the accuracy of force fields, together with the necessity for comprehensive sampling. We show that molecular dynamics simulations, combined with extensive conformational phase space exploration with Hamiltonian replica-exchange simulations, enable us to characterize the relevant conformational basins of the minimal hairpin ribozyme in the ligated state prior to self-cleavage. We find that what is usually considered a canonical reactive conformation with active site geometries and hydrogen-bond patterns that are optimal for the addition-elimination reaction with general acid/general base catalysis is metastable and only marginally populated. The thermodynamically stable conformation appears to be consistent with the expectations of a mechanism that does not require the direct participation of ribozyme residues in the reaction. While these observations may suffer from forcefield inaccuracies, all investigated forcefields lead to the same conclusions upon proper sampling, contrasting with previous investigations on shorter timescales suggesting that at least one reparametrization of the Amber99 forcefield allowed to stabilize aligned active site conformations. Our study demonstrates that identifying the most pertinent reactant state conformation holds equal importance alongside the accurate determination of the thermodynamics and kinetics of the chemical steps of the reaction.

Dynamics and Structures of Amyloid Aggregates under Fluid Flows

In this work, we investigate how fluid flows impact the aggregation mechanisms of Aβ40 proteins and Aβ16-22 peptides and mechanically perturb their (pre)fibrillar aggregates. We exploit the OPEP coarse-grained model for proteins and the Lattice Boltzmann Molecular Dynamics technique. We show that beyond a critical shear rate, amyloid aggregation speeds up in Couette flow because of the shorter collisions times between aggregates, following a transition from diffusion limited to advection dominated dynamics. We also characterize the mechanical deformation of (pre)fibrillar states due to the fluid flows (Couette and Poiseuille), confirming the capability of (pre)fibrils to form pathological loop-like structures as detected in experiments. Our findings can be of relevance for microfluidic applications and for understanding aggregation in the interstitial brain space.

Molecular mechanisms of catch bonds and their implications for platelet hemostasis

Adhesive molecular bonds between blood cells are essential for thrombosis and hemostasis as they provide means for platelet adhesion, aggregation, and signaling in flowing blood. According to the nowadays conventional definition, a "catch" bond is a type of non-covalent bio-molecular bridge, whose dissociation lifetime counter-intuitively increases with applied tensile force. Following recent experimental findings, such receptor-ligand protein bonds are vital to the blood cells involved in the prevention of bleeding (hemostatic response) and infection (immunity). In this review, we examine the up-to-date experimental discoveries and theoretical insights about catch bonds between the blood cells, their biomechanical principles at the molecular level, and their role in platelet thrombosis and hemostasis.