Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Predicting antibody affinity changes upon mutations by combining multiple predictors

Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.

Molecular modeling approaches of selective adenosine receptor type 2A agonists as potential anti-inflammatory drugs

Adenosine A_2A receptor (A_2AR) is the predominant receptor in immune cells, where its activation triggers cAMP-mediated immunosuppressive signaling and the underlying inhibition of T cells activation and T cells-induced effects mediated by cAMP-dependent kinase proteins mechanisms. In this study, were used ADME/Tox, molecular docking and molecular dynamics simulations to investigate selective adenosine A_2AR agonists as potential anti-inflammatory drugs. As a result, we obtained two promising compounds (A and B) that have satisfactory pharmacokinetic and toxicological properties and were able to interact with important residues of the A_2AR binding cavity and during the molecular dynamics simulations were able to keep the enzyme complexed.Communicated by Ramaswamy H. Sarma.

Evolution of protein interfaces in multimers and fibrils

A majority of cellular proteins function as part of multimeric complexes of two or more subunits. Multimer formation requires interactions between protein surfaces that lead to closed structures, such as dimers and tetramers. If proteins interact in an open-ended way, uncontrolled growth of fibrils can occur, which is likely to be detrimental in most cases. We present a statistical physics model that allows aggregation of proteins as either closed dimers or open fibrils of all lengths. We use pairwise amino-acid contact energies to calculate the energies of interacting protein surfaces. The probabilities of all possible aggregate configurations can be calculated for any given sequence of surface amino acids. We link the statistical physics model to a population genetics model that describes the evolution of the surface residues. When proteins evolve neutrally, without selection for or against multimer formation, we find that a majority of proteins remain as monomers at moderate concentrations, but strong dimer-forming or fibril-forming sequences are also possible. If selection is applied in favor of dimers or in favor of fibrils, then it is easy to select either dimer-forming or fibril-forming sequences. It is also possible to select for oriented fibrils with protein subunits all aligned in the same direction. We measure the propensities of amino acids to occur at interfaces relative to noninteracting surfaces and show that the propensities in our model are strongly correlated with those that have been measured in real protein structures. We also show that there are significant differences between amino acid frequencies at isologous and heterologous interfaces in our model, and we observe that similar effects occur in real protein structures.

Crystal Structure of the Labile Complex of IL-24 with the Extracellular Domains of IL-22R1 and IL-20R2

Crystal structure of the ternary complex of human IL-24 with two receptors, IL-22R1 and IL-20R2, has been determined at 2.15 Å resolution. A crystallizable complex was created by a novel approach involving fusing the ligand with a flexible linker to the presumed low-affinity receptor, and coexpression of this construct in Drosophila S2 cells together with the presumed high-affinity receptor. This approach, which may be generally applicable to other multiprotein complexes with low-affinity components, was necessitated by the instability of IL-24 expressed by itself in either bacteria or insect cells. Although IL-24 expressed in Escherichia coli was unstable and precipitated almost immediately upon its refolding and purification, a small fraction of IL-24 remaining in the folded state was shown to be active in a cell-based assay. In the crystal structure presented here, we found that two cysteine residues in IL-24 do not form a predicted disulfide bond. Lack of structural restraint by disulfides, present in other related cytokines, is most likely reason for the low stability of IL-24. Although the contact area between IL-24 and IL-22R1 is larger than between the cytokine and IL-20R2, calculations show the latter interaction to be slightly more stable, suggesting that the shared receptor (IL-20R2) might be the higher-affinity receptor.

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics

Summary

We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data.

Availability and implementation

The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera.

Contact

gromiha@iitm.ac.in.

Supplementary information

Supplementary data are available at Bioinformatics online.