Protein surface representation and analysis by dimension reduction

Mixed mechanism of conformational selection and induced fit as a molecular recognition process in the calreticulin family of proteins

The fundamental question on the mechanism of molecular recognition during ligand binding has attracted a lot of scientific scrutiny. The two competing theories of ligand binding-"induced fit" and "conformational selection" have been proposed to explain biomolecular recognition. Since exploring a family of proteins with similar structural architectures and conserved functional roles can provide valuable insight into the significance of molecular structure and function, we performed molecular dynamics simulations on the calreticulin family of proteins, which specifically recognize monoglucosylated N-glycan during the protein folding process. Atomistic simulations of lectins in free and bound forms demonstrated that they exist in several conformations spanning from favorable to unfavorable for glycan binding. Our analysis was confined to the carbohydrate recognition domain (CRD) of these lectins to demonstrate the degree of conservation in protein sequence and structure and relate them with their function. Furthermore, we computed the lectin-glycan binding affinity using the mmPBSA approach to identify the most favorable lectin conformation for glycan binding and compared the molecular interaction fields in terms of noncovalent bond interactions. We also demonstrated the involvement of Tyr and Trp residues in the CRD with the non-reducing end glucose and central mannose residues, which contribute to some of the specific interactions. Furthermore, we analyzed the conformational changes in the CRD through SASA, RMSFs and protein surface topography mapping of electrostatic and hydrophobic potentials. Our findings demonstrate a hybrid mechanism of molecular recognition, initially driven by conformational selection followed by glycan-induced fluctuations in the key residues to strengthen the glycan binding interactions.

Probing the Surfaces of Biomacromolecules with Polymer-Scaffolded Dynamic Combinatorial Libraries

Methods to analyze and compare biomacromolecular surfaces are still in their relative infancy on account of the challenges involved in comparing surfaces computationally. We describe a systems chemistry approach that utilizes polymer-scaffolded dynamic combinatorial libraries to experimentally probe biomacromolecular surfaces in aqueous solution which provides feedback as to the nature of the surfaces, allowing the comparison of three globular proteins and a nucleic acid.

Structuprint: a scalable and extensible tool for two-dimensional representation of protein surfaces

BACKGROUND

The term 'molecular cartography' encompasses a family of computational methods for two-dimensional transformation of protein structures and analysis of their physicochemical properties. The underlying algorithms comprise multiple manual steps, whereas the few existing implementations typically restrict the user to a very limited set of molecular descriptors.

RESULTS

We present Structuprint, a free standalone software that fully automates the rendering of protein surface maps, given - at the very least - a directory with a PDB file and an amino acid property. The tool comes with a default database of 328 descriptors, which can be extended or substituted by user-provided ones. The core algorithm comprises the generation of a mould of the protein surface, which is subsequently converted to a sphere and mapped to two dimensions, using the Miller cylindrical projection. Structuprint is partly optimized for multicore computers, making the rendering of animations of entire molecular dynamics simulations feasible.

CONCLUSIONS

Structuprint is an efficient application, implementing a molecular cartography algorithm for protein surfaces. According to the results of a benchmark, its memory requirements and execution time are reasonable, allowing it to run even on low-end personal computers. We believe that it will be of use - primarily but not exclusively - to structural biologists and computational biochemists.

GECluster: a novel protein complex prediction method

Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein–protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analysis of protein complex evolution has been poorly studied, partly due to the lack of high-precision protein complex datasets. In this paper, a combined PPI network is introduced to predict protein complexes which integrate both GO and expression value of relevant protein-coding genes. A novel protein complex prediction method GECluster (Gene Expression Cluster) was proposed based on a seed node expansion strategy, in which a combined PPI network was utilized. GECluster was applied to a training combined PPI network and it predicted more credible complexes than peer methods. The results indicate that using a combined PPI network can efficiently improve protein complex prediction accuracy. In order to study protein complex evolution within cells due to changes in the living environment surrounding cells, GECluster was applied to seven combined PPI networks constructed using the data of a test set including yeast response to stress throughout a wine fermentation process. Our results showed that with the rise of alcohol concentration, protein complexes within yeast cells gradually evolve from one state to another. Besides this, the number of core and attachment proteins within a protein complex both changed significantly.

3D structural analysis of proteins using electrostatic surfaces based on image segmentation

Herein, we present a novel strategy to analyse and characterize proteins using protein molecular electro-static surfaces. Our approach starts by calculating a series of distinct molecular surfaces for each protein that are subsequently flattened out, thus reducing 3D information noise. RGB images are appropriately scaled by means of standard image processing techniques whilst retaining the weight information of each protein's molecular electrostatic surface. Then homogeneous areas in the protein surface are estimated based on unsupervised clustering of the 3D images, while performing similarity searches. This is a computationally fast approach, which efficiently highlights interesting structural areas among a group of proteins. Multiple protein electrostatic surfaces can be combined together and in conjunction with their processed images, they can provide the starting material for protein structural similarity and molecular docking experiments.