Computational Assessment of Protein–Protein Binding Specificity within a Family of Synaptic Surface Receptors

Abstract

Atomic-level information is essential to explain the formation of specific protein complexes in terms of structure and dynamics. The set of Dpr and DIP proteins, which play a key role in the neuromorphogenesis in the nervous system of Drosophila melanogaster, offer a rich paradigm to learn about protein–protein recognition. Many members of the DIP subfamily cross-react with several members of the Dpr family and vice versa. While there exists a total of 231 possible Dpr–DIP heterodimer complexes from the 21 Dpr and 11 DIP proteins, only 57 “cognate” pairs have been detected by surface plasmon resonance (SPR) experiments, suggesting that the remaining 174 pairs have low or unreliable binding affinity. Our goal is to assess the performance of computational approaches to characterize the global set of interactions between Dpr and DIP proteins and identify the specificity of binding between each DIP with their corresponding Dpr binding partners. In addition, we aim to characterize how mutations influence the specificity of the binding interaction. In this work, a wide range of knowledge-based and physics-based approaches are utilized, including mutual information, linear discriminant analysis, homology modeling, molecular dynamics simulations, Poisson–Boltzmann continuum electrostatics calculations, and alchemical free energy perturbation to decipher the origin of binding specificity of the Dpr–DIP complexes examined. Ultimately, the results show that those two broad strategies are complementary, with different strengths and limitations. Biological inter-relations are more clearly revealed through knowledge-based approaches combining evolutionary and structural features, the molecular determinants controlling binding specificity can be predicted accurately with physics-based approaches based on atomic models.

Conserved Biophysical Compatibility Among the Highly Variable Germline-Encoded Regions Shapes TCR-MHC Interactions

“…While our information-theoretic and biophysical analyses suggested limited conserved contacts between TCR α / β CDR1 and CDR2 loops and MHC helices, they did not rule out coevolution at the level of biophysical compatibility over a broader interaction region. To explore the possibility of a coevolution that has not enforced genetic correlations but instead produced such compatibility among the TCR CDR loops and the MHC helices, we utilized a previously-validated [46] interaction scoring metric (see Methods). These interaction scores are calculated between all possible amino acid sequences of HLA alleles and TRAV or TRBV genes, providing a useful quantification of the relative contribution of germline-encoded CDR1 and CDR2 loops to a given TCR-MHC complex.…”

“…The AIMS interaction scoring is based on a matrix that quantifies a basic pairwise interaction scheme (Table S1), whereby productive amino acid interactions (salt bridges, hydrogen bonds) are scored positively while destructive interactions (hydrophilic-hydrophobic and like-charge clashes) are scored negatively. The first version of this interaction scoring matrix has previously been used to classify interacting and non-interacting molecular partners with a distinguishability of nearly 80% [46].…”

An Integrated Approach to the Characterization of Immune Repertoires Using AIMS: An Automated Immune Molecule Separator

“…While a key feature of the AIMS analysis, we will not discuss the application of LDA to immune repertoires further in this manuscript due to space considerations. Instead, we refer the reader to two applications of the AIMS LDA module as seen in Boughter et al [36] and Nandigrami et al [38] Comparisons to Existing Software…”

Free energy perturbation calculations of mutation effects on SARS-CoV-2 RBD::ACE2 binding affinity

“…Free-energy perturbation (FEP) methods have the potential to impact the field as physics-based force fields are, in principle, agnostic to the system being studied. Most current applications have involved the optimization of ligand-protein interaction in the context of small molecule drug design (reviewed in (14)) but recent publications have begun to explore the use of FEP methods to the study of protein-protein interactions (PPIs); specifically, to the effects of interfacial mutations on protein-protein binding free energies (8,9,(15)(16)(17)(18)(19). This is an inherently complex problem since, as opposed to relatively rigid ligand binding pockets, protein-protein interfaces are often quite large and less constrained so that they can more easily undergo conformational change as a result of a mutation.…”

“…Moreover, FEP calculations involve a complex computational infrastructure and are extremely time consuming. However, fast graphical processing units (GPUs) made such calculations feasible and a number of recent publications, involving different software packages, suggest that the methodology has reached the point that good correlation with experiment is to be expected (8,9,(15)(16)(17)(18)(19). Clearly, if FEP methods are capable of providing meaningful results, then in many applications, the computational cost will be worthwhile.…”