Studies on the inference of protein binding regions across fold space based on structural similarities

A novel computational strategy for defining the minimal protein molecular surface representation

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

ATP Can Efficiently Stabilize Protein through a Unique Mechanism

Recent experiments suggested that ATP can effectively stabilize protein structure and inhibit protein aggregation when its concentration is less than 10 mM, which is significantly lower than cosolvent concentrations required in conventional mechanisms. The ultrahigh efficiency of ATP suggests a unique mechanism that is fundamentally different from previous models of cosolvents. In this work, we used molecular dynamics simulation and experiments to study the interactions of ATPs with three proteins: lysozyme, ubiquitin, and malate dehydrogenase. ATP tends to bind to the surface regions with high flexibility and high degree of hydration. These regions are also vulnerable to thermal perturbations. The bound ATPs further assemble into ATP clusters mediated by Mg2+ and Na+ ions. More interestingly, in Mg2+-free ATP solution, Na+ at higher concentration (150 mM under physiological conditions) can similarly mediate the formation of the ATP cluster on protein. The ATP cluster can effectively reduce the fluctuations of the vulnerable region and thus stabilize the protein against thermal perturbations. Both ATP binding and the considerable improvement of thermal stability of ATP-bound protein were verified by experiments.

2D Zernike polynomial expansion: Finding the protein-protein binding regions

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order.

Rational Structure-Based Rescaffolding Approach to De Novo Design of Interleukin 10 (IL-10) Receptor-1 Mimetics

Tackling protein interfaces with small molecules capable of modulating protein-protein interactions remains a challenge in structure-based ligand design. Particularly arduous are cases in which the epitopes involved in molecular recognition have a non-structured and discontinuous nature. Here, the basic strategy of translating continuous binding epitopes into mimetic scaffolds cannot be applied, and other innovative approaches are therefore required. We present a structure-based rational approach involving the use of a regular expression syntax inspired in the well established PROSITE to define minimal descriptors of geometric and functional constraints signifying relevant functionalities for recognition in protein interfaces of non-continuous and unstructured nature. These descriptors feed a search engine that explores the currently available three-dimensional chemical space of the Protein Data Bank (PDB) in order to identify in a straightforward manner regular architectures containing the desired functionalities, which could be used as templates to guide the rational design of small natural-like scaffolds mimicking the targeted recognition site. The application of this rescaffolding strategy to the discovery of natural scaffolds incorporating a selection of functionalities of interleukin-10 receptor-1 (IL-10R1), which are relevant for its interaction with interleukin-10 (IL-10) has resulted in the de novo design of a new class of potent IL-10 peptidomimetic ligands.

How a spatial arrangement of secondary structure elements is dispersed in the universe of protein folds

It has been known that topologically different proteins of the same class sometimes share the same spatial arrangement of secondary structure elements (SSEs). However, the frequency by which topologically different structures share the same spatial arrangement of SSEs is unclear. It is important to estimate this frequency because it provides both a deeper understanding of the geometry of protein folds and a valuable suggestion for predicting protein structures with novel folds. Here we clarified the frequency with which protein folds share the same SSE packing arrangement with other folds, the types of spatial arrangement of SSEs that are frequently observed across different folds, and the diversity of protein folds that share the same spatial arrangement of SSEs with a given fold, using a protein structure alignment program MICAN, which we have been developing. By performing comprehensive structural comparison of SCOP fold representatives, we found that approximately 80% of protein folds share the same spatial arrangement of SSEs with other folds. We also observed that many protein pairs that share the same spatial arrangement of SSEs belong to the different classes, often with an opposing N- to C-terminal direction of the polypeptide chain. The most frequently observed spatial arrangement of SSEs was the 2-layer α/β packing arrangement and it was dispersed among as many as 27% of SCOP fold representatives. These results suggest that the same spatial arrangements of SSEs are adopted by a wide variety of different folds and that the spatial arrangement of SSEs is highly robust against the N- to C-terminal direction of the polypeptide chain.

Low-resolution structural modeling of protein interactome

Structural characterization of protein-protein interactions across the broad spectrum of scales is key to our understanding of life at the molecular level. Low-resolution approach to protein interactions is needed for modeling large interaction networks, given the significant level of uncertainties in large biomolecular systems and the high-throughput nature of the task. Since only a fraction of protein structures in interactome are determined experimentally, protein docking approaches are increasingly focusing on modeled proteins. Current rapid advancement of template-based modeling of protein-protein complexes is following a long standing trend in structure prediction of individual proteins. Protein-protein templates are already available for almost all interactions of structurally characterized proteins, and about one third of such templates are likely correct.

SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds

Background

Protein interactions are essential for coordinating cellular functions. Proteomic studies have already elucidated a huge amount of protein-protein interactions that require detailed functional analysis. Understanding the structural basis of each individual interaction through their structural determination is necessary, yet an unfeasible task. Therefore, computational tools able to predict protein binding regions and recognition modes are required to rationalize putative molecular functions for proteins. With this aim, we previously created SCOWLP, a structural classification of protein binding regions at protein family level, based on the information obtained from high-resolution 3D protein-protein and protein-peptide complexes.

Description

We present here a new version of SCOWLP that has been enhanced by the inclusion of protein-nucleic acid and protein-saccharide interactions. SCOWLP takes interfacial solvent into account for a detailed characterization of protein interactions. In addition, the binding regions obtained per protein family have been enriched by the inclusion of predicted binding regions, which have been inferred from structurally related proteins across all existing folds. These inferences might become very useful to suggest novel recognition regions and compare structurally similar interfaces from different families.

Conclusions

The updated SCOWLP has new functionalities that allow both, detection and comparison of protein regions recognizing different types of ligands, which include other proteins, peptides, nucleic acids and saccharides, within a solvated environment. Currently, SCOWLP allows the analysis of predicted protein binding regions based on structure-based inferences across fold space. These predictions may have a unique potential in assisting protein docking, in providing insights into protein interaction networks, and in guiding rational engineering of protein ligands. The newly designed SCOWLP web application has an improved user-friendly interface that facilitates its usage, and is available at http://www.scowlp.org.