Hydrophilicity matching - a potential prerequisite for the formation of protein-protein complexes in the cell

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Determinants of Macromolecular Specificity from Proteomics-Derived Peptide Substrate Data

BACKGROUND

Recent advances in proteomics methodologies allow for high throughput profiling of proteolytic cleavage events. The resulting substrate peptide distributions provide deep insights in the underlying macromolecular recognition events, as determinants of biomolecular specificity identified by proteomics approaches may be compared to structure-based analysis of corresponding protein-protein interfaces.

METHOD

Here, we present an overview of experimental and computational methodologies and tools applied in the area and provide an outlook beyond the protein class of proteases.

RESULTS AND CONCLUSION

We discuss here future potential, synergies and needs of the emerging overlap disciplines of proteomics and structure-based modelling.

Analogue encoding of physicochemical properties of proteins in their cognate messenger RNAs

Being related by the genetic code, mRNAs and their cognate proteins exhibit mutually interdependent compositions, which implies the possibility of a direct connection between their general physicochemical properties. Here we probe the general potential of the cell to encode information about proteins in the average characteristics of their cognate mRNAs and decode it in a ribosome-independent manner. We show that average protein hydrophobicity, calculated from either sequences or 3D structures, can be encoded in an analogue fashion by many different average mRNA sequence properties with the only constraint being that pyrimidine and purine bases be clearly distinguishable on average. Moreover, average characteristics of mRNA sequences enable discrimination between cytosolic and membrane proteins even in the absence of topogenic signal-based mechanisms. Our results suggest that protein and mRNA localization may be partly determined by basic physicochemical rationales and interdependencies between the two biomolecules.

mRNA transport contributes to the proper localization of its cognate proteins. Here the authors report a correlation between the physicochemical properties of mRNAs and their cognate proteins, suggesting that these properties of mRNAs can predict the subcellular localization of their cognate proteins.

A systematic framework for molecular dynamics simulations of protein post-translational modifications

By directly affecting structure, dynamics and interaction networks of their targets, post-translational modifications (PTMs) of proteins play a key role in different cellular processes ranging from enzymatic activation to regulation of signal transduction to cell-cycle control. Despite the great importance of understanding how PTMs affect proteins at the atomistic level, a systematic framework for treating post-translationally modified amino acids by molecular dynamics (MD) simulations, a premier high-resolution computational biology tool, has never been developed. Here, we report and validate force field parameters (GROMOS 45a3 and 54a7) required to run and analyze MD simulations of more than 250 different types of enzymatic and non-enzymatic PTMs. The newly developed GROMOS 54a7 parameters in particular exhibit near chemical accuracy in matching experimentally measured hydration free energies (RMSE = 4.2 kJ/mol over the validation set). Using this tool, we quantitatively show that the majority of PTMs greatly alter the hydrophobicity and other physico-chemical properties of target amino acids, with the extent of change in many cases being comparable to the complete range spanned by native amino acids.

Post-translational modifications, i.e. chemical changes of protein amino acids, play a key role in different cellular processes, ranging from enzymatic activation to transcription and translation regulation to disease development and aging. However, our understanding of their effects on protein structure, dynamics and interaction networks at the atomistic level is still largely incomplete. In particular, molecular dynamics simulations, despite their power to provide a high-resolution insight into biomolecular function and underlying mechanisms, have been limited to unmodified, native proteins due to a surprising deficiency of suitable tools and systematically developed parameters for treating modified proteins. To fill this gap, we develop and validate force field parameters, an essential part of the molecular dynamics method, for more than 250 different types of enzymatic and non-enzymatic post-translational modifications. Additionally, using this tool, we quantitatively show that microscopic properties of target amino acids, such as hydrophobicity, are greatly affected by the majority of modifications. The parameters presented in this study greatly expand the range of applicability of computational methods, and in particular molecular dynamics simulations, to a large set of new systems with utmost biological and biomedical importance.

Protein Electrostatic Properties Predefining the Level of Surface Hydrophobicity Change upon Phosphorylation

We use explicit-solvent, molecular dynamics simulations to study the change in polar properties of a solvent-accessible surface for proteins undergoing phosphorylation. We analyze eight different pairs of proteins representing different structural classes in native and phosphorylated states and estimate the polarity of their surface using the molecular hydrophobicity potential approach. Whereas the phosphorylation-induced hydrophobicity change in the vicinity of phosphosites does not vary strongly among the studied proteins, the equivalent change for complete proteins covers a surprisingly wide range of effects including even an increase in the overall hydrophobicity in some cases. Importantly, the observed changes are strongly related to electrostatic properties of proteins, such as the net charge per residue, the distribution of charged side-chain contacts, and the isoelectric point. These features predefine the level of surface hydrophobicity change upon phosphorylation and may thus contribute to the phosphorylation-induced alteration of the interactions between a protein and its environment.

Exploration of conformational transition in the aryl-binding site of human FXa using molecular dynamics simulations

Human coagulation Factor X (FX), a member of the vitamin K-dependent serine protease family, is a crucial component of the human coagulation cascade. Activated FX (FXa) participates in forming the prothrombinase complex on activated platelets to convert prothrombin to thrombin in coagulation reactions. In the current study, 30-ns MD simulations were performed on both the open and closed states of human FXa. Root mean squares (RMS) fluctuations showed that structural fluctuations concentrated on the loop regions of FXa, and the presence of a ligand in the closed system resulted in larger fluctuations of the gating residues. The open system had a gating distance from 9.23 to 11.33 Å, i.e., significantly larger than that of the closed system (4.69-6.35 Å), which allows diversified substrates of variable size to enter. Although the solvent accessible surface areas (SASA) of FXa remained the same in both systems, the open system generally had a larger total SASA or hydrophobic SASA (or both) for residues surrounding the S4 pocket. Additionally, more hydrogen bonds were formed in the closed state than in the open state of FXa, which is believed to play a significant role in maintaining the closed confirmation of the aryl-binding site. Based on the results of MD simulations, we propose that an induced-fit mechanism governs the functioning of human coagulation FX, which helps provide a better understanding of the interactions between FXa and its substrate, and the mechanism of the conformational changes involved in human coagulation.