Dynamic property is a key determinant for protein-protein interactions

Million-atom molecular dynamics simulations reveal the interfacial interactions and assembly of plant PSII-LHCII supercomplex

Protein-protein interface interactions dictate efficient excitation energy transfer from light-harvesting antennas to the photosystem II (PSII) core. In this work, we construct a 1.2 million atom-scale model of plant C₂S₂-type PSII-LHCII supercomplex and perform microsecond-scale molecular dynamics (MD) simulations to explore the interactions and assembly mechanisms of the sizeable PSII-LHCII supercomplex. We optimize the nonbonding interactions of the PSII-LHCII cryo-EM structure using microsecond-scale MD simulations. Binding free energy calculations with component decompositions reveal that hydrophobic interactions predominantly drive antenna-core association and the antenna-antenna interactions are relatively weak. Despite the positive electrostatic interaction energies, hydrogen bonds and salt bridges mainly provide directional or anchoring forces for interface binding. Analysis of the roles of small intrinsic subunits of PSII suggests that LHCII and CP26 first interact with small intrinsic subunits and then bind to the core proteins, whereas CP29 adopts a one-step binding process to the PSII core without the assistance of other factors. Our study provides insights into the molecular underpinnings of the self-organization and regulation of plant PSII-LHCII. It lays the framework for deciphering the general assembly principles of photosynthetic supercomplexes and possibly other macromolecular structures. The finding also has implications for repurposing photosynthetic systems to enhance photosynthesis.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Rigid-body motions of interacting proteins dominate multispecific binding of ubiquitin in a shape-dependent manner

To understand the dynamic aspects of multispecificity of ubiquitin, we studied nine ubiquitin-ligand (partner protein) complexes by normal mode analysis based on an elastic network model. The coupling between ubiquitin and ligand motions was analyzed by decomposing it into rigid-body (external) and vibrational (internal) motions of each subunit. We observed that in total the external motions in one of the subunits largely dominated the coupling. The combination of external motions of ubiquitin and the ligands showed general trends of rotations and translations. Moreover, we observed that the rotational motions of ubiquitin were correlated to the ligand orientations. We also identified ubiquitin atomic vibrations that differentiated the orientation of the ligand molecule. We observed that the extents of coupling were correlated to the shapes of the ligands, and this trend was more pronounced when the coupling involved vibrational motions of the ligand. In conclusion, an intricate interplay between internal and external motions of ubiquitin and the ligands help understand the dynamics of multispecificity, which is mostly guided by the shapes of the ligands and the complex.

Counterbalance of ligand- and self-coupled motions characterizes multispecificity of ubiquitin

Date hub proteins are a type of proteins that show multispecificity in a time-dependent manner. To understand dynamic aspects of such multispecificity we studied Ubiquitin as a typical example of a date hub protein. Here we analyzed 9 biologically relevant Ubiquitin-protein (ligand) heterodimer structures by using normal mode analysis based on an elastic network model. Our result showed that the self-coupled motion of Ubiquitin in the complex, rather than its ligand-coupled motion, is similar to the motion of Ubiquitin in the unbound condition. The ligand-coupled motions are correlated to the conformational change between the unbound and bound conditions of Ubiquitin. Moreover, ligand-coupled motions favor the formation of the bound states, due to its in-phase movements of the contacting atoms at the interface. The self-coupled motions at the interface indicated loss of conformational entropy due to binding. Therefore, such motions disfavor the formation of the bound state. We observed that the ligand-coupled motions are embedded in the motions of unbound Ubiquitin. In conclusion, multispecificity of Ubiquitin can be characterized by an intricate balance of the ligand- and self-coupled motions, both of which are embedded in the motions of the unbound form.

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

A single-molecule study of the inhibition effect of Naringenin on transforming growth factor-β ligand-receptor binding

With single-molecule fluorescence imaging and single-molecule force measurement, we have found that the natural compound Naringenin exerts an inhibition effect on TGF-β ligand-receptor interaction, the initial step of TGF-β signaling.

Anchoring intrinsically disordered proteins to multiple targets: lessons from N-terminus of the p53 protein

Anchor residues, which are deeply buried upon binding, play an important role in protein–protein interactions by providing recognition specificity and facilitating the binding kinetics. Up to now, studies on anchor residues have been focused mainly on ordered proteins. In this study, we investigated anchor residues in intrinsically disordered proteins (IDPs) which are flexible in the free state. We identified the anchor residues of the N-terminus of the p53 protein (Glu17–Asn29, abbreviated as p53N) which are involved in binding with two different targets (MDM2 and Taz2), and analyzed their side chain conformations in the unbound states. The anchor residues in the unbound p53N were found to frequently sample conformations similar to those observed in the bound complexes (i.e., Phe19, Trp23, and Leu26 in the p53N-MDM2 complex, and Leu22 in the p53N-Taz2 complex). We argue that the bound-like conformations of the anchor residues in the unbound state are important for controlling the specific interactions between IDPs and their targets. Further, we propose a mechanism to account for the binding promiscuity of IDPs in terms of anchor residues and molecular recognition features (MoRFs).

Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein-protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure-based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein-protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure-based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure-based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k(off), k(on), and K(d), respectively, in leave-one-out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k(on) was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k(off) was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design.