Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR methodology. They open the way to a host of new investigations on the diversity analysis and prediction of drug–target interactions.     

Highly sensitive detection of daunorubicin based on carbon nanotubes–drug supramolecular interaction

A highly sensitive method for electrochemical detection of daunorubicin (DNR) was proposed on the carbon nanotubes (CNT) modified electrode. The supramolecular interaction between the CNT and the anthracyclin could significantly enhance the electron transferability, which sharply increased the detection sensitivity and lowered the detection limit. Under the optimized conditions, the linear range of the DNR detection was 20–500 nM with a detection sensitivity of 5.9 nA/nM. The detection of the DNR in the serum samples was also attempted. It can be predicted that many more analogues could be monitored on such a platform with high sensitivity.     

Scoring protein–protein docked structures based on the balance and tightness of binding

One main issue in protein-protein docking is to filter or score the putative docked structures. Unlike many popular scoring functions that are based on geometric and energetic complementarity, we present a set of scoring functions that are based on the consideration of local balance and tightness of binding of the docked structures. These scoring functions include the force and moment acting on one component (ligand) imposed by the other (receptor) and the second order spatial derivatives of protein-protein interaction potential. The scoring functions were applied to the docked structures of 19 test targets including enzyme/inhibitor, antibody/antigen and other classes of protein complexes. The results indicate that these scoring functions are also discriminative for the near-native conformation. For some cases, such as antibody/antigen, they show more discriminative efficiency than some other scoring functions, such as desolvation free energy (deltaG(des)) based on pairwise atom-atom contact energy (ACE). The correlation analyses between present scoring functions and the energetic functions also show that there is no clear correlation between them; therefore, the present scoring functions are not essentially the same as energy functions.     

Protein function prediction using neighbor relativity in protein–protein interaction network

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called “Neighbor Relativity Coefficient” (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.     

Thermodynamic study on the interaction between anti-tumor drug tegafur and human serum albumin

The changes of thermodynamic properties of the system on interaction between tegafur and human serum albumin (HSA) and the changes of secondary structure units of HSA in the system at 298.15 K have been investigated by the Nano-Watt-Scale isothermal titration calorimetry (ITC), the Langmuir’s binding model and the circular dichroism (CD) spectrometry.     

A shotgun approach for the identification of platinum–protein complexes

A shotgun approach including peptide-based OFFGEL-isoelectric focusing (IEF) fractionation has been developed with the aim of improving the identification of platinum-binding proteins in biological samples. The method is based on a filter-aided sample preparation (FASP) tryptic digestion under denaturing and reducing conditions of cisplatin–, oxaliplatin–, and carboplatin–protein complexes, followed by OFFGEL-IEF separation of the peptides. Any risk of platinum loss is minimized throughout the procedure due to the removal of the reagents used after each stage of the FASP method and the absence of thiol-based reagents in the focusing buffer employed in the IEF separation. The platinum–peptide complexes stability after the FASP digestion and the IEF separation was confirmed by size exclusion chromatography-inductively coupled plasma-mass spectrometry (SEC-ICP-MS). The suitability of peptide-based OFFGEL-IEF fractionation for reducing the sample complexity for further nano-liquid chromatography-electrospray ionization-tandem mass spectrometry (nLC-ESI-MS/MS) analysis has been demonstrated, allowing the detection of platinum-containing peptides, with significantly lower abundance and ionization efficiency than unmodified peptides. nLC-MS/MS analysis of selected OFFGEL-IEF fractions from tryptic digests with different complexity degrees: standard human serum albumin (HSA), a mixture of five proteins (albumin, transferrin, carbonic anhydrase, myoglobin, and cytochrome-c) and human blood serum allowed the identification of several platinum–peptides from cisplatin–HSA. Cisplatin-binding sites in HSA were elucidated from the MS/MS spectra and assessed considering the protein three-dimensional structure. Most of the potential superficial binding sites available on HSA were identified for all the samples, including a biologically relevant cisplatin-cross-link of two protein domains, demonstrating the capabilities of the methodology.

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Graphical Abstract Graphical abstract shows the several steps involved in the identification of platinum-protein complexes: FASP digestion of proteins, peptide fractionation by OFFGEL-IEF and identification of Pt-complexes by nLC-ESIMS/MS

     

In-vitro drug–drug interaction studies of diltiazem with floroquinolones

Diltiazem is an established cardiovascular drug mainly used for the management of hypertension specifically for the angina pectoris. Fluoroquinolones are widely prescribed against the treatment of severe infections. In vitro relations of diltiazem with fluoroquinolones (ciprofloxacin, levofloxacin, norfloxacin, and ofloxacin) were examined using spectrophotometric and separation techniques, i.e., RP-HPLC. Diltiazem’s availabilities were observed to be predisposed highly in the presence of fluoroquinolones. To investigate the mechanism of interaction in a variety of dissolution environments, i.e., simulating body environments with regard to pH on these interactions has been studied. Moreover, complex of diltiazem–fluoroquinolones were prepared and elucidated through IR spectroscopy and confirmed by computational molecular modeling.     

Study of tungstate–protein interaction in human serum by LC–ICP-MS and MALDI-TOF

Oral administration of sodium tungstate is an effective treatment for type 1 and 2 diabetes in animal models; it does not incur significant side effects, and it may constitute an alternative to insulin. However, the mechanism by which tungstate exerts its observed metabolic effects in vivo is still not completely understood. In this work, serum-containing proteins which bind tungstate have been characterized. Size exclusion chromatography (SEC) coupled to inductively coupled plasma mass spectrometry (ICP-MS) with a Phenomenex Bio-Sep-S 2000 column and 20 mM HEPES and 150 mM NaCl at pH 7.4 as the mobile phase was chosen as the most appropriate methodology to screen for tungsten–protein complexes. When human serum was incubated with tungstate, three analytical peaks were observed, one related to tungstate–albumin binding, one to free tungstate, and one to an unknown protein binding (MW higher than 300 kDa). Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometric analysis of the tungsten-containing fractions collected from SEC–ICP-MS chromatograms, after desalting and preconcentration processes, confirmed the association of tungstate with albumin and the other unknown protein. Figure SEC-ICP-MS // MALDI-TOF     

A flow-injection ultrafiltration sampling chemiluminescence system for on-line determination of drug–protein interaction

A flow-injection ultrafiltration sampling chemiluminescence system for on-line determination of cimetidine–bovine serum albumin (BSA) interaction is proposed in this paper. Cimetidine can be oxidized by N-bromosuccinimide (NBS) and sensitized by fluorescein to produce high chemiluminescence emission in basic media. The concentration of cimetidine is linear with the CL intensity in the range 3×10^–7–1×10^–4 mol L^–1 with a detection limit of 1×10^–7 mol L^–1 (3). The drug and protein were mixed in different molar ratios in 0.067 mol L^–1 phosphate buffer, pH 7.4, and incubated at 37 °C in a water bath. The ultrafiltration probe was utilized to sample the mixed solution at a flow rate of 5 µL min^–1. The data obtained by the proposed ultrafiltration flow-injection chemiluminescence method was analyzed with Scrathard analysis and a Klotz plot. The estimated association constant (K) and the number of the binding site (n) on one molecule of BSA by Scrathard analysis and Klotz plot were 3.15×10⁴ L mol^–1 and 0.95, 3.25×10⁴ L mol^–1 and 0.92, respectively. The proposed system proved that flow-injection chemiluminescence analysis coupled with on-line ultrafiltration sampling is a simple and reliable technique for the study of drug–protein interaction.     

Synthesis of bifunctional peptide derivatives based on a β-cyclodextrin core with drug delivery potential

A selective, versatile, robust methodology for bifunctionalization of β-cyclodextrin is achieved allowing the attachment of peptides in varying C- and/or N-terminal combinations on resin using Fmoc SPPS. Two linkers are attached to cyclodextrin enabling selective binding to the resin (or a peptide attached to the resin). Continuation of peptide growth and/or cleavage from the resin follows, thus various combinations of peptide-cyclodextrin species are achieved. A model peptide (Gly-Ala) is used in this study to illustrate the potential of this system for attaching one or more bioactive peptides for drug transport and release purposes.     

P–V–T properties of polymer melts based on equation of state and neural network

An accurate and efficient analytical equation of state (EOS) and artificial neural network (ANN) methods are developed for the prediction of volumetric properties of polymer melts. To apply EOS, the second virial coefficients B₂(T), effective van der Waals co-volume, b(T) and correction factor, α(T) were determined. The second virial coefficient was calculated from a two-parameter corresponding states correlation, which is constructed with two constants as scaling parameters, i.e., temperature (T_f) and density at melting (ρ_f) point. The new correlations were used to predict the specific volumes of polypropylene glycol (PPG), polyethylene glycol (PEG), polypropylene (PP), polyvinylchloride (PVC), poly(1-butene)(PB1), poly (?-caprolactone) (PCL), polyethylene (PE) and polyvinylmethylether (PVME) at compressed state in the temperature range of 298.15–634.6 K. The obtained results show that the two models have good agreement with the experimental data with absolute average deviation of 0.28% and 0.39% for ANN and EOS, respectively. The Comparison of the results with ISM model shows that the proposed models represent an efficient method and are more accurate.     

Analysis of drug–protein binding by ultrafast affinity chromatography using immobilized human serum albumin

Human serum albumin (HSA) was explored for use as a stationary phase and ligand in affinity microcolumns for the ultrafast extraction of free drug fractions and the use of this information for the analysis of drug–protein binding. Warfarin, imipramine, and ibuprofen were used as model analytes in this study. It was found that greater than 95% extraction of all these drugs could be achieved in as little as 250 ms on HSA microcolumns. The retained drug fraction was then eluted from the same column under isocratic conditions, giving elution in less than 40 s when working at 4.5 mL/min. The chromatographic behavior of this system gave a good fit with that predicted by computer simulations based on a reversible, saturable model for the binding of an injected drug with immobilized HSA. The free fractions measured by this method were found to be comparable to those determined by ultrafiltration, and equilibrium constants estimated by this approach gave good agreement with literature values. Advantages of this method include its speed and the relatively low cost of microcolumns that contain HSA. The ability of HSA to bind many types of drugs also creates the possibility of using the same affinity microcolumn to study and measure the free fractions for a variety of pharmaceutical agents. These properties make this technique appealing for use in drug-binding studies and in the high-throughput screening of new drug candidates.     

Theoretical prediction of the protein–protein interaction between Arabidopsis thaliana COP1 and UVR8

In plants, ultraviolet-B radiation (280–315 nm) regulates gene expression and plant morphology through the UV RESPONSE LOCUS 8 (UVR8) photoreceptor. The first signaling event after quantal absorbance is the interaction of the UVR8 C-terminus with the E3 ubiquitin ligase CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1). The nature of the interaction between these two proteins is hitherto unknown. A protein homology model of the Arabidopsis thaliana COP1 seven-bladed propeller WD40 repeat domain and de novo folds of the C-terminal 27 amino acid (amino acids 397–423) peptide of Arabidopsis UVR8 (UVR8^397?423) is herein reported. Using a theoretical computational docking protocol, the interaction between COP1 and UVR8 was predicted. A core motif was identified in UVR8^397?423 comprising adjacent hydrophobic residues V410 and P411 together with a charged residue D412, homologous to corresponding motifs in other COP1-binding proteins, such as ELONGATED HYPOCOTYL 5 (HY5), cryptochrome 1 (CRY1), and salt tolerance proteins STO/STH. The protein–protein interaction between the COP1 WD40 repeat domain and UVR8^397?423 reveals binding within a region of COP1 overlapping with the binding site for HY5 and the other COP1-interacting proteins. This study provides a framework for understanding docking between UVR8 and COP1, which in turn gives clues for experimental testing of UVR8/COP1 interaction.     

Specific features of coupling reactions of polystyrene in the presence α-dinitrones based on glyoxal

Russian Chemical Bulletin - Bromine-containing polystyrene synthesized by atom transfer radical polymerization was used as a model to study coupling reactions in the presence of such free radical...     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Protein–protein interface prediction based on hexagon structure similarity

Studies on protein–protein interaction are important in proteome research. How to build more effective models based on sequence information, structure information and physicochemical characteristics, is the key technology in protein–protein interface prediction. In this paper, we study the protein–protein interface prediction problem. We propose a novel method for identifying residues on interfaces from an input protein with both sequence and 3D structure information, based on hexagon structure similarity. Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein–protein interface. Comparing to existing methods, our approach improves F-measure value by at least 0.03. On a common dataset consisting of 41 complexes, our method has overall precision and recall values of 63% and 57%. On Benchmark v4.0, our method has overall precision and recall values of 55% and 56%. On CAPRI targets, our method has overall precision and recall values of 52% and 55%.     

Do cancer proteins really interact strongly in the human protein-protein interaction network?

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer.