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.     

Studying protein–protein affinity and immobilized ligand–protein affinity interactions using MS-based methods

This review discusses the most important current methods employing mass spectrometry (MS) analysis for the study of protein affinity interactions. The methods are discussed in depth with particular reference to MS-based approaches for analyzing protein–protein and protein–immobilized ligand interactions, analyzed either directly or indirectly. First, we introduce MS methods for the study of intact protein complexes in the gas phase. Next, pull-down methods for affinity-based analysis of protein–protein and protein–immobilized ligand interactions are discussed. Presently, this field of research is often called interactomics or interaction proteomics. A slightly different approach that will be discussed, chemical proteomics, allows one to analyze selectivity profiles of ligands for multiple drug targets and off-targets. Additionally, of particular interest is the use of surface plasmon resonance technologies coupled with MS for the study of protein interactions. The review addresses the principle of each of the methods with a focus on recent developments and the applicability to lead compound generation in drug discovery as well as the elucidation of protein interactions involved in cellular processes. The review focuses on the analysis of bioaffinity interactions of proteins with other proteins and with ligands, where the proteins are considered as the bioactives analyzed by MS.     

Characterization of peptide–protein interactions using photoaffinity labeling and LC/MS

The combination of photoaffinity labeling (PAL) with modern mass spectrometric techniques is a powerful approach for the characterization of peptide–protein interactions. Depending on the analytical strategy applied, a PAL experiment can provide different levels of information ranging from the identification of interaction partners to the structural characterization of ligand-binding sites. On the basis of LC/MS data generated in the framework of the identification of the binding site of the neuropeptide corticotropin-releasing factor (CRF) on its binding protein (CRFBP), the key role of LC/MS in the characterization of photoadducts on different structural levels was demonstrated. Covalent photoadducts of rat CRFBP (rCRFBP) were obtained by PAL with different mono- and bifunctional benzophenone photoprobes designed on the basis of the sequence of the synthetic CRF fragment human/rat CRF^6–33 which binds to CRFBP with high affinity. In view of the stoichiometry, LC/MS analysis revealed that the photoadducts consisted of one molecule of photoprobe and one molecule of rCRFBP. For a further characterization of the photoadducts on the oligopeptide level, enzymatic digests of unlabeled rCRFBP and of the respective photoadduct were compared by peptide mapping monitored with LC/MS. Thereby, it was found that the photoprobe that contained the photophore at its N-terminus labeled the amino acid sequence rCRFBP(34–38), whereas the photoprobe that contained the photophore at its C-terminus labeled rCRFBP(12–26). On the basis of the characterization of the photoadduct formed by rCRFBP and the bifunctional photoprobe that contained photophores on both termini, semiquantitative comparison of different enzymatic digests was accomplished by application of the mass-selective multiple ion chromatogram strategy.     

Probing noncovalent protein—Ligand interactions of the cGMP-dependent protein kinase using electrospray ionization time of flight mass spectrometry

Nanoflow electrospray ionization time of flight mass spectrometry (ESI-TOF-MS) was used to study activation properties of the cGMP-dependent protein kinase (PKG). Our nanoflow ESI-TOF-MS analysis confirms that PKG mainly occurs as a 153 kDa homodimer and is able to bind four cGMP molecules, which is in agreement with the known stoichiometry. Binding order and stoichiometry of cGMP, the non-hydrolysable ATP analog beta,gamma-imidoadenosine 5'-triphosphate (AMPPNP) and Mn2+ for PKG were characterized as model for the active PKG-cGMP-ATP/Mg2+ complex. Already in the absence of cGMP, a noncovalent complex between PKG and two molecules of AMPPNP could be observed by ESI-TOF-MS. Binding of AMPPNP to PKG was strongly enhanced by the addition of MnCl2 to the spray solution. This is in agreement with binding of AMPPNP/Mn2+ in the ATP binding pocket of PKG since all protein kinases require a metal ion to accompany ATP in the ATP-binding pocket for proper positioning of the beta and gamma phosphates. Additionally, this finding could imply that within the inactive conformation of PKG, the autoinhibition-domain, when in contact with the substrate-docking domain, does not block the entrance to the ATP-binding site. In the presence of cGMP, less of the fully saturated PKG-(cGMP)4(AMPPNP/Mn2+)2 complex was observed, suggesting that the PKG-ATP interaction is weakened in the active conformation of PKG. Additionally, limited proteolysis in combination with native-ESI MS showed to be a useful tool to study the contact regions on the PKG-dimer and also allowed the rapid determination of the overall autophosphorylation status of the protein. These measurements indicated that autophosphorylation mainly occurs within the first 80 aminoterminal residues and involves in total 3-4 phosphates per subunit.     

Solid–solvent molecular interactions observed in crystal structures of β-chitin complexes

Three β-chitin structures [anhydrous, di-hydrate, mono-ethylenediamine (EDA)] recently determined by synchrotron X-ray and neutron fiber diffraction were reviewed from the viewpoint of molecular interactions. Both water and EDA molecules interact with the chitin chains through multiple hydrogen bonds. When water complexes with chitin, the hydrogen bonding pattern rearranges with the replacement of an intrachain chitin hydrogen bond by a stronger hydrogen bond between chitin and water, with an associated reduction in the degrees of freedom; the water oxygen is a much stronger acceptor than the O5 ring atom. The behavior of hydrogen exchange by deuterium supports this interpretation. EDA-molecules change the conformation of hydroxymethyl group from gg to gt, accompanied by changes in hydrogen bonds due to the strong accepting ability of the EDA nitrogen atoms. Some important interactions are in common with experimental crystallographic results of cellulosic crystals and of molecular dynamics studies. These new insights into solid–solvent interactions are valuable in understanding molecular interactions in other polysaccharides-solvents system in solution or on surface.     

A new sequence based encoding for prediction of host–pathogen protein interactions

Pathogen–host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen–host interactions. Developing a computational method to predict pathogen–host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen–host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen–host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.     

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.     

Analysis of Fas–ligand interactions using a molecular model of the receptor–ligand interface

A molecular model of the complex between Fas and its ligand was generated to better understand the location and putative effects of site-specific mutations, analyze interactions at the Fas–FasL interface, and identify contact residues. The modeling study was conservative in the sense that regions in Fas and its ligand which could not be predicted with confidence were omitted from the model to ensure accuracy of the analysis. Using the model, it was possible to map four of five N-linked glycosylation sites in Fas and FasL and to study 10 of 11 residues previously identified by mutagenesis as important for binding. Interactions involving six of these residues could be analyzed in detail and their importance for binding was rationalized based on the model. The predicted structure of the Fas–FasL interface was consistent with the experimentally established importance of these residues for binding. In addition, five previously not targeted residues were identified and predicted to contribute to binding via electrostatic interactions. Despite its limitations, the study provided a much improved basis to understand the role of Fas and FasL residues for binding compared to previous residue mapping studies using only a molecular model of Fas.     

Biophysical and electrochemical studies of protein–nucleic acid interactions

Monatshefte für Chemie - Chemical Monthly - This review is devoted to biophysical and electrochemical methods used for studying protein–nucleic acid (NA) interactions. The importance of...     

NMR-based analysis of protein–ligand interactions

Physiological processes are mainly controlled by intermolecular recognition mechanisms involving protein–protein and protein–ligand (low molecular weight molecules) interactions. One of the most important tools for probing these interactions is high-field solution nuclear magnetic resonance (NMR) through protein-observed and ligand-observed experiments, where the protein receptor or the organic compounds are selectively detected. NMR binding experiments rely on comparison of NMR parameters of the free and bound states of the molecules. Ligand-observed methods are not limited by the protein molecular size and therefore have great applicability for analysing protein–ligand interactions. The use of these NMR techniques has considerably expanded in recent years, both in chemical biology and in drug discovery. We review here three major ligand-observed NMR methods that depend on the nuclear Overhauser effect—transferred nuclear Overhauser effect spectroscopy, saturation transfer difference spectroscopy and water–ligand interactions observed via gradient spectroscopy experiments—with the aim of reporting recent developments and applications for the characterization of protein–ligand complexes, including affinity measurements and structural determination.     

Inhibition of TRAF6-Ubc13 interaction in NFkB inflammatory pathway by analyzing the hotspot amino acid residues and protein–protein interactions using molecular docking simulations

Protein–protein interactions (PPIs) are important in most of the biochemical processes. Hotspot amino acid residues in proteins are the most important contributors for proper protein–protein interactions. Hotspot amino acid residues have been looked down upon as important therapeutic targets in inhibiting PPIs. Interaction between TRAF6 and Ubc13 is a crucial point in the NFkB inflammatory pathway. Dysfunction of the NFkB pathway is associated with numerous human diseases including cancer and neurodenegeration disorders. Ubc13 also interacts specifically to TRAF6 and not with other proteins of the TRAF family and this makes the TRAF6-Ubc13 complex an important target for specific inhibition. Hence, interfering with the TRAF6-Ubc13 association may prove effective in suppressing the NFkB disease pathway. In the present study, we searched the TRAF6-Ubc13 interaction interface to analyze their binding hotspot amino acid residues using various computational techniques. Heterocyclic compounds are known for their medicinal properties. We screened for heterocyclic analogues to the known TRAF6 inhibitor PDTC, to predict a better inhibitor using in silico protein–ligand and protein–protein interaction studies. Our in silico prediction results suggest that tetrahydro-2-thiophenecarbothioamide (Chemspider ID 36027528) binds one of the major hot-spot residues of TRAF6-Ubc13 interface and can be a better alternative in suppressing TRA6-Ubc13 complex formation in chronic inflammation than PDTC.     

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.     

Carbohydrate–protein interactions and their biosensing applications

Carbohydrate recognition is clearly present throughout nature, playing a major role in the initial attachment of one biological entity to another. The important question is whether these prevalent interactions could provide a real suitable alternative to the use of antibodies or nucleic acid for detection and identification. Currently, examples of carbohydrates being employed in biological detection systems are limited. The challenges of using carbohydrate recognition for detection mainly come from the weak affinity of carbohydrate–protein interactions, the lack of versatile carbohydrate scaffolds with well-defined structures, and the less developed high-information-content, real-time, and label-free assay technology. In this review, we focus on discussing the characteristics of carbohydrate–protein interactions in nature and the methods for carbohydrate immobilization based on surface coupling chemistry in terms of their general applicability for developing carbohydrate- and lectin-based label-free sensors. Furthermore, examples of innovative design of multivalent carbohydrate–protein interactions for sensor applications are given. We limit our review to show the feasibility of carbohydrate and lectin as recognition elements for label-free sensor development in several representative cases to formulate a flexible platform for their use as recognition elements for real-world biosensor applications.     

Label-free bioelectronic detection of aptamer–protein interactions

We demonstrate for the first time the utility of nucleic acid aptamers for electrochemical detection of proteins. Highly specific and sensitive label-free detection of the target protein is achieved by combining aptamer-coated magnetic beads and chronopotentiometric stripping measurements of the captured protein (in connection to the intrinsic electroactivity of the protein). Lysozyme has thus been detected selectively in a mixture containing a large excess of six proteins and amino acids (both electroactive and non-electroactive), with a detection limit of 350 fmol (7 nM). While aptamer-based electronic sensors are in their infancy, such devices offer attractive opportunities for electrochemical detection of proteins and for developing proteomic chips.     

GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR–drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.     

Electron–phonon interactions in atomic and molecular devices

Electron–phonon interactions are extremely important for understanding charge transport, inelastic processes, heating, and heat dissipation in nanoscale molecular and atomic devices. In molecular electronics Inelastic Electron Tunneling Spectroscopy (IETS) has recently emerged as one of the premier methods for characterizing molecular-scale junctions and devices. This method provides a distinct chemical fingerprint for identifying molecules within a junction, and has allowed for clear demonstrations of single molecule devices, the effects of electric field on molecular orbitals, the importance of molecular configuration on conductance, as well as information about the charge transport mechanism. In this review we will discuss the use of Point Contact (PC) and IET spectroscopies on molecular and atomic systems, discuss the basic principles involved in inelastic transport for these spectroscopic methods to function, and focus on the experimental techniques involved and the important conclusions drawn from the experiments performed to date.