Grottesi, A. | Search bioRxiv

bioRxiv2026

Molecular determinants of differential substrate selection between the Src family kinases Lck and Src

Src family kinases (SFKs) share highly conserved catalytic domains yet display distinct biological functions, raising the question of how substrate specificity is achieved. Here, we investigate the molecular basis of differential ITAM recognition by Lck and Src, combining cellular assays with structural analysis and docking simulations. In-cell assays demonstrated that, contrary to Lck, Src was completely incapable of phosphorylating the TCR ITAMs when ectopically expressed in a T cell environment. Domain-swapping experiments further revealed that substitution of the Src kinase domain with that of Lck was sufficient to confer ITAM phosphorylation and trigger downstream TCR signaling responses, whereas exchange of adaptor domains had minimal effect. Comparative structural analysis revealed that, despite their overall conserved fold, Lck exhibits a more open and solvent accessible pocket located between the N- and C-lobes of the kinase domain, adjacent to the activation loop, compared to Src. Consistent with this, docking simulations showed that Lck accommodates ITAM peptides in multiple favourable conformations, whereas Src displays a markedly reduced number of non-productive binding poses. Residue-level contact analysis identified a defined interaction surface in Lck, spanning the inter-lobal regions and activation loop. Our results highlight the importance of kinase domain conformational landscape in shaping substrate selectivity and have implications for the rational design of selective SFK inhibitors.

Cell Biology↗

bioRxiv2022

A role of Lck annular lipids in the steady upkeep of active Lck in T cells

Theoretical work suggests that collective spatiotemporal behaviour of integral membrane proteins (IMPs) can be modulated by annular lipids sheathing their hydrophobic moiety. Here, we present evidence for this prediction in a natural membrane by investigating the mechanism that maintains steady amount of active isoform of Lck kinase (LckA) by Lck trans-autophosphorylation offset by the phosphatase CD45. We gauged experimental suitability by quantitation of CD45 and LckA subcellular localisation, LckA generation as a function of Lck and pharmacological perturbation. Steady LckA was challenged by swapping Lck membrane anchor with structurally divergent ones expected to substantially modify Lck annular lipids, such as that of Src or the transmembrane domains of LAT, CD4, palmitoylation-defective CD4 and CD45, respectively. The data showed only small alteration of LckA, except for CD45 hydrophobic anchor that thwarted LckA, due to excessive lateral proximity to CD45. The data are best explained by annular lipids facilitating or penalising IMPs lateral proximity, hence modulating IMPs protein-protein functional interactions. Our findings can contribute to improve the understanding of biomembranes organisation.

cell biology↗

bioRxiv2019

Revisiting the \"satisfaction of spatial restraints\" approach of MODELLER for protein homology modeling

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the \"modeling by satisfaction of spatial restraints\" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of{sigma} values profoundly influences 3D modeling. We show that the{sigma} values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the programs predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLERs objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance.\n\nAuthor summaryProteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most successful computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLERs scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.

bioinformatics↗