Recognition of protein complexation based on hydrophobicity distribution

The identification of the surface area able to generate the protein-protein complexation ligand and ion ligation is critical for the recognition of the biological function of particular proteins. The technique based on the analysis of the irregularity of hydrophobicity distribution is used as the criterion for the recognition of the interaction regions. Particularly, the exposure of hydrophobic residues on the surface of protein as well as the localization of the hydrophilic residues in the hydrophobic core is treated as potential area ready to interact with external molecules. The model based on the “fuzzy oil drop” approach treating the protein molecule as the drop of hydrophobicity concentrated in the central part of structure with the hydrophobicity close to zero on the surface according to 3-dimensional Gauss function. The comparison with the observed hydrophobicy in particular protein reveals some irregularities. These irregularities seem to represent the aim-oriented localization.     

Sequence-structure-function relation characterized in silico

Methods for biological function recognition in silico appeared to be useful also for identifying characteristics of structure-to-function relations. The introduction of a three-dimensional Gauss function was assumed to represent the hydrophobic core in a protein molecule. The discrepancy between idealized "fuzzy oil-drop" and the observed one in real proteins appeared to be localized in the ligation site or in the area of biological function related part of protein molecule. The examples of proteins presented in this paper reveal that the structure-function relation can be evaluated and characterized also using the profile of the difference in value between idealized and real hydrophobicity distribution along the polypeptide chain. The specificity of particular polypeptide chain fragments in respect to their biological function and their specific participation in active site creation is discussed in this paper. The scale allowing comparison of different proteins in respect to their ligand-binding sites characteristics is introduced.     

Ligation site in proteins recognized in silico

Recognition of a ligation site in a protein molecule is important for identifying its biological activity. The model for in silico recognition of ligation sites in proteins is presented. The idealized hydrophobic core stabilizing protein structure is represented by a three-dimensional Gaussian function. The experimentally observed distribution of hydrophobicity compared with the theoretical distribution reveals differences. The area of high differences indicates the ligation site. AVAILABILITY: http://bioinformatics.cm-uj.krakow.pl/activesite.     

“Fuzzy oil drop” model applied to individual small proteins built of 70 amino acids

The proteins composed of short polypeptides (about 70 amino acid residues) representing the following functional groups (according to PDB notation): growth hormones, serine protease inhibitors, antifreeze proteins, chaperones and proteins of unknown function, were selected for structural and functional analysis. Classification based on the distribution of hydrophobicity in terms of deficiency/excess as the measure of structural and functional specificity is presented. The experimentally observed distribution of hydrophobicity in the protein body is compared to the idealized one expressed by a three-dimensional Gauss function. The differences between these two distributions reveal the specificity of structural/functional characteristics of the protein. The residues of hydrophobicity deficiency versus the idealized distribution are assumed to indicate cavities with the potential to bind ligands, while the residues of hydrophobicity excess are interpreted as potentially participating in protein-protein complexation. The distribution of hydrophobicity irregularity seems to be specific for particular structures and functions of proteins. A comparative analysis of such profiles is carried out to identify the potential biological activity of proteins of unknown function.     

Resonant recognition model and protein topography. Model studies with myoglobin, hemoglobin and lysozyme

This study describes the further extension of the resonant recognition model for the analysis and prediction of protein--protein and protein--DNA structure/function dependencies. The model is based on the significant correlation between spectra of numerical presentations of the amino acid or nucleotide sequences of proteins and their coded biological activity. According to this physico-mathematical method, it is possible to define amino acids in the sequence which are predicted to be the most critical for protein function. Using sperm whale myoglobin, human hemoglobin and hen egg white lysozyme as model protein examples, sets of predicted amino acids, or so-called 'hot spots', have been identified within the tertiary structure. It was found for each protein that the predicted 'hot spots', which are distributed along the primary sequence, are spatially grouped in a dome-like arrangement over the active site. The identified amino acids did not correspond to the amino acid residues which are involved in the chemical reaction site of these proteins. It is thus proposed that the resonant recognition model helps to identify amino acid residues which are important for the creation of the molecular structure around the catalytic active site and also the associated physical field conditions required for biorecognition, docking of the specific substrate and full biological activity.     

Role of invariant water molecules in retaining the active site geometry of β-lactamase: a molecular dynamics simulation study

Water molecules play a critical role in stabilising the three-dimensional architecture, dynamics and function of biological macromolecules. Comparative analysis of structurally similar proteins has shown that there are water molecules conserved in the same relative positions and make similar hydrogen bonds with proteins in all crystal structures. These invariant water molecules are essential for the maintenance of the native structure of proteins. The present study explores the role of invariant water molecules to maintain the active site geometry of β-lactamase enzyme. Thirteen crystal structures of class-A β-lactamase from Staphylococcus aureus have been used in this study. Molecular dynamics simulations of the protein structures were performed in hydrated as well as in dehydrated conditions. The analysis showed that significant changes occur in the active site geometry due to dehydration. These changes can be attributed to the removal of water molecules at the active site.     

Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking

A major problem in genome annotation is whether it is valid to transfer the function from a characterised protein to a homologue of unknown activity. Here, we show that one can employ a strategy that uses a structure-based prediction of protein functional sites to assess the reliability of functional inheritance. We have automated and benchmarked a method based on the evolutionary trace approach. Using a multiple sequence alignment, we identified invariant polar residues, which were then mapped onto the protein structure. Spatial clusters of these invariant residues formed the predicted functional site. For 68 of 86 proteins examined, the method yielded information about the observed functional site. This algorithm for functional site prediction was then used to assess the validity of transferring the function between homologues. This procedure was tested on 18 pairs of homologous proteins with unrelated function and 70 pairs of proteins with related function, and was shown to be 94 % accurate. This automated method could be linked to schemes for genome annotation. Finally, we examined the use of functional site prediction in protein-protein and protein-DNA docking. The use of predicted functional sites was shown to filter putative docked complexes with a discrimination similar to that obtained by manually including biological information about active sites or DNA-binding residues.     

Computational active site analysis of molecular pathways to improve functional classification of enzymes

This study describes a method to computationally assess the function of homologous enzymes through small molecule binding interaction energy. Three experimentally determined X-ray structures and four enzyme models from ornithine cyclo-deaminase, alanine dehydrogenase, and mu-crystallin were used in combination with nine small molecules to derive a function score (FS) for each enzyme-model combination. While energy values varied for a single molecule-enzyme combination due to differences in the active sites, we observe that the binding energies for the entire pathway were proportional for each set of small molecules investigated. This proportionality of energies for a reaction pathway appears to be dependent on the amino acids in the active site and their direct interactions with the small molecules, which allows a function score (FS) to be calculated to assess the specificity of each enzyme. Potential of mean force (PMF) calculations were used to obtain the energies, and the resulting FS values demonstrate that a measurement of function may be obtained using differences between these PMF values. Additionally, limitations of this method are discussed based on: (a) larger substrates with significant conformational flexibility; (b) low homology enzymes; and (c) open active sites. This method should be useful in accurately predicting specificity for single enzymes that have multiple steps in their reactions and in high throughput computational methods to accurately annotate uncharacterized proteins based on active site interaction analysis.     

Identification of an autoinhibitory region in the activation loop of the Mos protein kinase.

The Mos protein is a serine/threonine protein kinase which acts to regulate progression through meiosis in vertebrate oocytes. Although Mos function is dependent on its ability to act as a protein kinase, little is known about the factors which regulate Mos kinase activity. To understand the mechanism by which Mos kinase activity is regulated, we have used molecular modeling to construct a three-dimensional model of Mos based on the crystallographic coordinates of cyclic AMP-dependent kinase (PKA). This model identified a loop in Mos which is positioned near the active site and appears capable of blocking substrate access to the active site. Mutagenesis was used to construct altered forms of the Mos protein with deletions of parts or all of the loop. In vitro kinase assays showed that Mos proteins with the loop removed had up to a fourfold increase in kinase activity compared with the wild-type protein, indicating that the loop acts in an autoinhibitory manner for Mos kinase activity. Point mutations were also made on individual residues of the loop which were determined from the molecular model to be capable of reaching the active site. Determination of the kinase activities of these mutants showed that individual mutations in the loop region are capable of either increasing or decreasing kinase activity with regard to the wild-type protein. These data suggest that the loop identified in Mos acts as an autoinhibitor of kinase activity.     

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR-Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well-defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source .     

Identification of protein functions from a molecular surface database,eF-site

A bioinformatics method was developed to identify the protein surface around the functional site and to estimate the biochemical function, using a newly constructed molecular surface database named the eF-site (electrostatic surface of Functional site. Molecular surfaces of protein molecules were computed based on the atom coordinates, and the eF-site database was prepared by adding the physical properties on the constructed molecular surfaces. The electrostatic potential on each molecular surface was individually calculated solving the Poisson–Boltzmann equation numerically for the precise continuum model, and the hydrophobicity information of each residue was also included. The eF-site database is accessed by the internet (http://pi.protein.osaka-u.ac.jp/eF-site/). We have prepared four different databases, eF-site/antibody, eF-site/prosite, eF-site/P-site, and eF-site/ActiveSite, corresponding to the antigen binding sites of antibodies with the same orientations, the molecular surfaces for the individual motifs in PROSITE database, the phosphate binding sites, and the active site surfaces for the representatives of the individual protein family, respectively. An algorithm using the clique detection method as an applied graph theory was developed to search of the eF-site database, so as to recognize and discriminate the characteristic molecular surfaces of the proteins. The method identifies the active site having the similar function to those of the known proteins.     

Structured disorder and conformational selection

Traditionally, molecular disorder has been viewed as local or global instability. Molecules or regions displaying disorder have been considered inherently unstructured. The term has been routinely applied to cases for which no atomic coordinates can be derived from crystallized molecules. Yet, even when it appears that the molecules are disordered, prevailing conformations exist, with population times higher than those of all alternate conformations. Disordered molecules are the outcome of rugged energy landscapes away from the native state around the bottom of the funnel. Ruggedness has a biological function, creating a distribution of structured conformers that bind via conformational selection, driving association and multimolecular complex formation, whether chain-linked in folding or unlinked in binding. We classify disordered molecules into two types. The first type possesses a hydrophobic core. Here, even if the native conformation is unstable, it still has a large enough population time, enabling its experimental detection. In the second type, no such hydrophobic core exists. Hence, the native conformations of molecules belonging to this category have shorter population times, hindering their experimental detection. Although there is a continuum of distribution of hydrophobic cores in proteins, an empirical, statistically based hydrophobicity function may be used as a guideline for distinguishing the two disordered molecule types. Furthermore, the two types relate to steps in the protein folding reaction. With respect to protein design, this leads us to propose that engineering-optimized specific electrostatic interactions to avoid electrostatic repulsion would reduce the type I disordered state, driving the molten globule (MG) --> native (N) state. In contrast, for overcoming the type II disordered state, in addition to specific interactions, a stronger hydrophobic core is also indicated, leading to the denatured --> MG --> N state.     

Characterization of low affinity Fcγ receptor biotinylation under controlled reaction conditions by mass spectrometry and ligand binding analysis

Chemical protein biotinylation and streptavidin or anti‐biotin‐based capture is regularly used for proteins as a more controlled alternative to direct coupling of the protein on a biosensor surface. On biotinylation an interaction site of interest may be blocked by the biotin groups, diminishing apparent activity of the protein. Minimal biotinylation can circumvent the loss of apparent activity, but still a binding site of interest can be blocked when labeling an amino acid involved in the binding. Here, we describe reaction condition optimization studies for minimal labeling. We have chosen low affinity Fcγ receptors as model compounds as these proteins contain many lysines in their active binding site and as such provide an interesting system for a minimal labeling approach. We were able to identify the most critical parameters (protein:biotin ratio and incubation pH) for a minimal labeling approach in which the proteins of choice remain most active toward analyte binding. Localization of biotinylation by mass spectrometric peptide mapping on minimally labeled material was correlated to protein activity in binding assays. We show that only aiming at minimal labeling is not sufficient to maintain an active protein. Careful fine‐tuning of critical parameters is important to reduce biotinylation in a protein binding site.     

Protein localization with flexible DNA or RNA

Localization of activity is ubiquitous in life, and also within sub-cellular compartments. Localization provides potential advantages as different proteins involved in the same cellular process may supplement each other on a fast timescale. It might also prevent proteins from being active in other regions of the cell. However localization is at odds with the spreading of unbound molecules by diffusion. We model the cost and gain for specific enzyme activity using localization strategies based on binding to sites of intermediate specificity. While such bindings in themselves decrease the activity of the protein on its target site, they may increase protein activity if stochastic motion allows the acting protein to touch both the intermediate binding site and the specific site simultaneously. We discuss this strategy in view of recent suggestions on long non-coding RNA as a facilitator of localized activity of chromatin modifiers.     

Structure based discovery of small molecule suppressors targeting bacterial lysozyme inhibitors

The production of lysozyme inhibitors, competitively binding to the lysozyme active site, is a bacterial strategy to prevent the lytic activity of host lysozymes. Therefore, suppression of the lysozyme–inhibitor interaction is an interesting new approach for drug development since restoration of the bacterial lysozyme sensitivity will support bacterial clearance from the infected sites. Using molecular modelling techniques the interaction of the Salmonella PliC inhibitor with c-type lysozyme was studied and a protein–protein interaction based pharmacophore model was created. This model was used as a query to identify molecules, with potential affinity for the target, and subsequently, these molecules were filtered using molecular docking. The retained molecules were validated as suppressors of lysozyme inhibitory proteins using in vitro experiments revealing four active molecules.     

Models of Monoamine Oxidase A and B Active Sites Obtained by using 3D QSAR with CoMFA Analysis

Abstract

The monoamine oxidase catalyses the oxidative deamination of neuroactive amines. This enzyme exists in two forms A and B, which differ by substrates preference and inhibitors specificity. Investigation of the structures of these enzymes and design new selective inhibitors are of greatly interesting since MAO A inhibitors are used in therapeutic practice as antidepressants and MAO B inhibitors – in the treatment Parkinson's diseases. The three dimension structures of monoamine oxidases are still unknown. Therefore, one of the most perspective approach to define significant features of structure active site is method based on analysis of structure-activity relationship (3D QSAR) with comparison of molecular fields analysis (CoMFA) allowing to get the spatial distribution of important properties affecting the activity.

In present study we investigate the structures of active sites MAO A and B using 16 pyrazinocarbazole derivatives in variant conformation. Majority of pyrazinocarbazole derivatives have a rigit conformation, but three of those is sufficiently flexible. The latters can be in two conformation types: long molecules (substitution accommodate along axis of main structure) and short molecules (substitution accommodate at acute angle about of main structure). Several 3D QSAR and CoMFA models of MAO A and B active sites were design for data sets containing various types of flexible molecules conformation. All obtained models are statistical reliable and have sufficient predictive power for tested compound tetrindole. The best MAO A model that include two flexible molecules in long conformations was obtained, and the longest one of those in short conformation. In contrast, for MAO B model containing all flexible molecules in the short conformations is more preferred.

On the basis of obtained data the schematic models of MAO A and B active sites structures are proposed. According to these models MAO A active site have the narrow long cavity that accommodate long molecules, while MAO B active site is broader and shorter.     

Functional evolution of PLP‐dependent enzymes based on active‐site structural similarities

Families of distantly related proteins typically have very low sequence identity, which hinders evolutionary analysis and functional annotation. Slowly evolving features of proteins, such as an active site, are therefore valuable for annotating putative and distantly related proteins. To date, a complete evolutionary analysis of the functional relationship of an entire enzyme family based on active‐site structural similarities has not yet been undertaken. Pyridoxal‐5′‐phosphate (PLP) dependent enzymes are primordial enzymes that diversified in the last universal ancestor. Using the comparison of protein active site structures (CPASS) software and database, we show that the active site structures of PLP‐dependent enzymes can be used to infer evolutionary relationships based on functional similarity. The enzymes successfully clustered together based on substrate specificity, function, and three‐dimensional‐fold. This study demonstrates the value of using active site structures for functional evolutionary analysis and the effectiveness of CPASS. Proteins 2014; 82:2597–2608. © 2014 Wiley Periodicals, Inc.