Assessment and significance of protein–protein interactions during development of protein biopharmaceuticals

Early development of protein biotherapeutics using recombinant DNA technology involved progress in the areas of cloning, screening, expression and recovery/purification. As the biotechnology industry matured, resulting in marketed products, a greater emphasis was placed on development of formulations and delivery systems requiring a better understanding of the chemical and physical properties of newly developed protein drugs. Biophysical techniques such as analytical ultracentrifugation, dynamic and static light scattering, and circular dichroism were used to study protein–protein interactions during various stages of development of protein therapeutics. These studies included investigation of protein self-association in many of the early development projects including analysis of highly glycosylated proteins expressed in mammalian CHO cell cultures. Assessment of protein–protein interactions during development of an IgG1 monoclonal antibody that binds to IgE were important in understanding the pharmacokinetics and dosing for this important biotherapeutic used to treat severe allergic IgE-mediated asthma. These studies were extended to the investigation of monoclonal antibody–antigen interactions in human serum using the fluorescent detection system of the analytical ultracentrifuge. Analysis by sedimentation velocity analytical ultracentrifugation was also used to investigate competitive binding to monoclonal antibody targets. Recent development of high concentration protein formulations for subcutaneous administration of therapeutics posed challenges, which resulted in the use of dynamic and static light scattering, and preparative analytical ultracentrifugation to understand the self-association and rheological properties of concentrated monoclonal antibody solutions.     

Peptides and peptidomimetics as regulators of protein–protein interactions

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Systems for the detection and analysis of protein–protein interactions

The analysis of protein–protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein–protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein–protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein–protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein–protein interactions can be determined. Systems that permit this to be achieved are described in this review.K. Kuroda, M. Kato and J. Mima contributed equally to this work.     

Analysis of secondary structural and physicochemical changes in protein–protein complexes

Conformation switching in protein–protein complexes is considered important for the molecular recognition process. Overall analysis of 123 protein–protein complexes in a benchmark data-set showed that 6.8% of residues switched over their secondary structure conformation upon complex formation. Amino acid residue-wise preference for conformation change has been analyzed in binding and non-binding site residues separately. In this analysis, residues such as Ser, Leu, Glu, and Lys had higher frequency of secondary structural conformation change. The change of helix to coil and sheet to coil conformation and vice versa has been observed frequently, whereas the conformation change of helix to extended sheet occurred rarely in the studied complexes. Influence of conformation change toward the N and C terminal on either side of the binding site residues has been analyzed. Further, analysis on φ and ψ angle variation, conservation, stability, and solvent accessibility have been performed on binding site residues. Knowledge obtained from the present study could be effectively employed in the protein–protein modeling and docking studies.     

Characterization of protein–protein interactions between rice viruses and vector insects

Planthoppers are the most notorious rice pests, because they transmit various rice viruses in a persistent-propagative manner. Protein–protein interactions (PPIs) between virus and vector are crucial for virus transmission by vector insects. However, the number of known PPIs for pairs of rice viruses and planthoppers is restricted by low throughput research methods. In this study, we applied DeNovo, a virus-host sequence-based PPI predictor, to predict potential PPIs at a genome-wide scale between three planthoppers and five rice viruses. PPIs were identified at two different confidence thresholds, referred to as low and high modes. The number of PPIs for the five planthopper-virus pairs ranged from 506 to 1985 in the low mode and from 1254 to 4286 in the high mode. After eliminating the “one-too-many” redundant interacting information, the PPIs with unique planthopper proteins were reduced to 343–724 in the low mode and 758–1671 in the high mode. Homologous analysis showed that 11 sets and 31 sets of homologous planthopper proteins were shared by all planthopper-virus interactions in the two modes, indicating that they are potential conserved vector factors essential for transmission of rice viruses. Ten PPIs between small brown planthopper and rice stripe virus (RSV) were verified using glutathione-S-transferase (GST)/His-pull down or co-immunoprecipitation assay. Five of the ten PPIs were proven positive, and three of the five SBPH proteins were confirmed to interact with RSV. The predicted PPIs provide new clues for further studies of the complicated relationship between rice viruses and their vector insects.     

Impact of protein and ligand impurities on ITC-derived protein–ligand thermodynamics

Background

The thermodynamic characterization of protein–ligand interactions by isothermal titration calorimetry (ITC) is a powerful tool in drug design, giving valuable insight into the interaction driving forces. ITC is thought to require protein and ligand solutions of high quality, meaning both the absence of contaminants as well as accurately determined concentrations.

Methods

Ligands synthesized to deviating purity and protein of different pureness were titrated by ITC. Data curation was attempted also considering information from analytical techniques to correct stoichiometry.

Results and conclusions

We used trypsin and tRNA-guanine transglycosylase (TGT), together with high affinity ligands to investigate the effect of errors in protein concentration as well as the impact of ligand impurities on the apparent thermodynamics. We found that errors in protein concentration did not change the thermodynamic properties obtained significantly. However, most ligand impurities led to pronounced changes in binding enthalpy. If protein binding of the respective impurity is not expected, the actual ligand concentration was corrected for and the thus revised data compared to thermodynamic properties obtained with the respective pure ligand. Even in these cases, we observed differences in binding enthalpy of about 4 kJ ⋅ mol^− 1, which is considered significant.

General significance

Our results indicate that ligand purity is the critical parameter to monitor if accurate thermodynamic data of a protein–ligand complex are to be recorded. Furthermore, artificially changing fitting parameters to obtain a sound interaction stoichiometry in the presence of uncharacterized ligand impurities may lead to thermodynamic parameters significantly deviating from the accurate thermodynamic signature.     

Sugar recognition and protein–protein interaction of mammalian lectins conferring diverse functions

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Structural and mechanistic commonalities of amyloid-β and the prion protein

Amyloid beta (Aβ) is a major causative agent of Alzheimer disease (AD). This neurotoxic peptide is generated as a result of the cleavage of the Amyloid-Precursor-Protein (APP) by the action of β-secretase and γ-secretase. The neurotoxicity was previously thought to be the result of aggregation. However, recent studies suggest that the interaction of Aβ with numerous cell surface receptors such as N-methyl-D-aspartate (NMDA), receptor for advanced glycosylation end products (RAGE), P75 neurotrophin receptor (P75^NTR) as well as cell surface proteins such as the cellular prion protein (PrP^c) and heparan sulfate proteoglycans (HSPG) strongly enhances Aβ induced apoptosis and thereby contributes to neurotoxicity. This review focuses on the molecular mechanism resulting in Aβ-shedding as well as Aβ-induced apoptotic processes, genetic risk factors for familial AD and interactions of Aβ with cell surface receptors and proteins, with particular emphasis on the cellular prion protein. Furthermore, comparisons are drawn between AD and prion disorders and the role of laminin, an extracellular matrix protein, glycosaminoglycans and the 37 kDa/67 kDa laminin receptor (LRP/LR) have been highlighted with regards to both neurodegenerative diseases.Key words: Alzheimer disease, amyloid β, apoptosis, 37 kDa/67 kDa laminin receptor, prion proteinsAlzheimer disease (AD), primarily defined by psychiatrist Alois Alzheimer in 1906, is a neurodegenerative disorder and currently exhibits a prevalence that “doubles approximately every five years from 0.5% at the common age of onset-65 years old.”¹ This disease is the most common form of dementia afflicting the elderly and at present affects in excess of 37 million people globally² and it is predicted that 100 million people will be living with the disease by 2050.³AD has received mounting scientific interest and has stimulated tireless research endeavours not only due to the complex mechanism by which it is caused; the multitude of contributing factors and contradictions which have arisen between hypotheses and acquired results, but also due to the rise in life expectancies⁴ owing to the advent of modern medicine, which has socio-economic implications particularly in terms of strain placed upon national health systems.     

Inhibition of dynamin-dependent endocytosis increases shedding of the amyloid precursor protein ectodomain and reduces generation of amyloid β protein

Background  

The amyloid precursor protein (APP) is transported via the secretory pathway to the cell surface, where it may be cleaved within its ectodomain by α-secretase, or internalized within clathrin-coated vesicles. An alternative proteolytic pathway occurs within the endocytic compartment, where the sequential action of β- and γ-secretases generates the amyloid β protein (Aβ). In this study, we investigated the effects of modulators of endocytosis on APP processing.     

Effect of hydrolytic lignin on formation of protein–lignin complexes and protein degradation by rumen microbes

Several methods have been developed to protect feed protein from rumen microbial degradation. The current study aimed to evaluate the potential use of an industrial lignin, namely hydrolytic lignin, to protect protein from rumen microbial degradation. The hydrolytic lignins assessed in this study were extracted from wheat straw previously subjected to various steam treatment conditions (pressure: 15, 17 and 19 bar; reaction time: 0, 5 and 10 min; use of acidic catalyst: without and with 2% H₂SO₄ on DM basis). Results indicated that hydrolytic lignin can precipitate protein when measured by a standard bovine serum albumin assay. It was also observed that protein-precipitating capacity of lignin increased with increasing harshness of steam treatment until a point from which no further effect was observed. The effect of lignin upon protein degradation in vitro was clearly detected. Both ammonia nitrogen and iso-acid concentration in vitro were significantly decreased (P<0.01) when lignin was added to fermentation flask containing casein. Unlike tannins, hydrolytic lignins do not inhibit rumen microbial activity. Additionally, it was observed that lignin’s ability to bind and protect protein is a pH-dependent reaction. Protein binding to lignin is markedly reduced at pH<3.0.     

How similar are protein folding and protein binding nuclei? Examination of vibrational motions of energy hot spots and conserved residues

The underlying physico-chemical principles of the interactions between domains in protein folding are similar to those between protein molecules in binding. Here we show that conserved residues and experimental hot spots at intermolecular binding interfaces overlap residues that vibrate with high frequencies. Similarly, conserved residues and hot spots are found in protein cores and are also observed to vibrate with high frequencies. In both cases, these residues contribute significantly to the stability. Hence, these observations validate the proposition that binding and folding are similar processes. In both packing plays a critical role, rationalizing the residue conservation and the experimental alanine scanning hot spots. We further show that high-frequency vibrating residues distinguish between protein binding sites and the remainder of the protein surface.     

Purification and characterization of HIV–human protein complexes

To fully understand how pathogens infect their host and hijack key biological processes, systematic mapping of intra-pathogenic and pathogen–host protein–protein interactions (PPIs) is crucial. Due to the relatively small size of viral genomes (usually around 10–100 proteins), generation of comprehensive host–virus PPI maps using different experimental platforms, including affinity tag purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) approaches, can be achieved. Global maps such as these provide unbiased insight into the molecular mechanisms of viral entry, replication and assembly. However, to date, only two-hybrid methodology has been used in a systematic fashion to characterize viral–host protein–protein interactions, although a deluge of data exists in databases that manually curate from the literature individual host–pathogen PPIs. We will summarize this work and also describe an AP-MS platform that can be used to characterize viral-human protein complexes and discuss its application for the HIV genome.     

Variation of protein binding cavity volume and ligand volume in protein–ligand complexes

We have systematically analyzed the variation of protein binding cavity volume of 200 protein–ligand complexes belonging to eight protein families. Wide variation in protein binding cavity volume for the same protein is observed on binding different ligands. Analysis of individual protein families shows high correlation between atom–atom interactions in binding site and ligand volume. This study implies the significance of protein flexibility in docking small molecule inhibitors on the basis of protein binding cavity volume with respect to ligand volume.     

A reliability measure of protein–protein interactions and a reliability measure-based search engine

Many methods developed for estimating the reliability of protein–protein interactions are based on the topology of protein–protein interaction networks. This paper describes a new reliability measure for protein–protein interactions, which does not rely on the topology of protein interaction networks, but expresses biological information on functional roles, sub-cellular localisations and protein classes as a scoring schema. The new measure is useful for filtering many spurious interactions, as well as for estimating the reliability of protein interaction data. In particular, the reliability measure can be used to search protein–protein interactions with the desired reliability in databases. The reliability-based search engine is available at http://yeast.hpid.org. We believe this is the first search engine for interacting proteins, which is made available to public. The search engine and the reliability measure of protein interactions should provide useful information for determining proteins to focus on.     

Intensive protein synthesis in neurons and phosphorylation of beta-amyloid precursor protein and tau-protein are triggering factors of neuronal amyloidosis and Alzheimer’s disease

Studies of Alzheimer’s disease have become particularly important and attract now much attention of scientists all over the world due to worldwide dissemination of this dangerous disorder. Causes of this pathology still remain unknown, while the final image, originally obtained on microscopic brain sections from patients with this disease more than a hundred years ago, is well familiar to clinicians. This includes deposition of amyloid-β (Aβ) in the brain tissue of senile plaques and fibrils. Many authors believe that the deposition of Aβ provokes secondary neuronal changes, responsible for death of neurons. Other authors associate the death of neurons with hyperphosphorylation of tau-proteins, which form neurofibrillar tangles inside nerve cells and cause their death. Creation of methods of preclinical diagnostics and effective treatment of Alzheimer’s disease requires novel knowledge: on the nature of triggering factors of sporadic forms of Alzheimer’s disease, on cause-effect relationships of phosphorylation of amyloid precursor protein with formation of pathogenic beta-amyloids, on the relationship between these factors underlying tau-protein hyperphosphorylation and neuron death. In this review we have analyzed reports describing increased intensity of protein synthesis in neurons under normal and various stress conditions, possibility of development of energy imbalance of neurons and activation of their protective systems. Phosphorylation and hyperphosphorylation of tau-proteins is also tightly associated with protective mechanisms of cells and with processes of evacuation of phosphates, adenosine monophosphates and pyrophosphates from the region of protein synthesis. Prolonged highly intensive protein synthesis causes overload of protective mechanisms and impairments in concerted metabolic processes. This leads to neuronal dysfunction, transport collapse, and death of neurons.     

Prediction of protein–protein interactions based on PseAA composition and hybrid feature selection

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein–protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR–KNNs–wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.