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.     

Binding affinity prediction for binary drug–target interactions using semi-supervised transfer learning

Journal of Computer-Aided Molecular Design - In the field of drug–target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task....     

Droplet microfluidics with magnetic beads: a new tool to investigate drug–protein interactions

In this study, we give the proof of concept for a method to determine binding constants of compounds in solution. By implementing a technique based on magnetic beads with a microfluidic device for segmented flow generation, we demonstrate, for individual droplets, fast, robust and complete separation of the magnetic beads. The beads are used as a carrier for one binding partner and hence, any bound molecule is separated likewise, while the segmentation into small microdroplets ensures fast mixing, and opens future prospects for droplet-wise analysis of drug candidate libraries. We employ the method for characterization of drug–protein binding, here warfarin to human serum albumin. The approach lays the basis for a microfluidic droplet-based screening device aimed at investigating the interactions of drugs with specific targets including enzymes and cells. Furthermore, the continuous method could be employed for various applications, such as binding assays, kinetic studies, and single cell analysis, in which rapid removal of a reactive component is required.     

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.     

Determination of drug–serum protein interactions via fluorescence polarization measurements

New fast methods for the determination of pharmacokinetic behaviour of potential drug candidates are receiving increasing interest. We present a new homogeneous method for the determination of drug binding and drug competition for human serum albumin and α₁-acid glycoprotein that is amenable to high-throughput-screening. It is based on selective fluorescent probes and the measurement of fluorescence polarization. This leads to decreased interference with fluorescent drugs as compared with previously published methods based on similar probes and the measurement of fluorescence intensity. The binding of highly fluorescent drugs that still interfere with the probes can be measured by simply titrating the drugs in a two-component system with the serum protein. The assay may also be used to discover strongly binding protein ligands that are interesting for drug-targeting strategies. Additionally, binding data could be obtained from larger libraries of compounds for in silico predictive pharmacokinetics. Figure Fluorescence polarization displacement titration of dansylsarcosine (3D-structure as insert) bound to human serum albumin (HSA) by naproxene Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Investigation of metal–buffer interactions using isothermal titration calorimetry

Isothermal titration calorimetry (ITC) and potentiometric titration (PT) methods were used to study the interactions of cobalt(II) and nickel(II) ions with buffer substances 2-(N-morpholino)ethanesulfonic acid (Mes), dimethylarsenic acid (Caco), and piperazine-N,N′-bis(2-ethanesulfonic acid) (Pipes). Based on the results of PT data, the stability constants were calculated for the metal–buffer complexes (T = 298.15 K, ionic strength I = 100 mM NaClO₄). Furthermore, calorimetric measurements (ITC) were run in 100 mM Mes, Caco, and Pipes solutions with pH 6, at 298.15 K. The enthalpies (ΔH) of the metal–buffer complexation reactions were calculated indirectly by displacement titration using nitrilotriacetic acid (H₃NTA) as a strong-binding, competitive ligand. Finally, to verify obtained results, the number of protons released by H₃NTA due to complexation of the cobalt(II) and nickel(II) ions was determined from calorimetric data and compared with results of calculations.     

Retention time prediction of polycyclic aromatic hydrocarbons in gas chromatography–mass spectrometry using QSPR based on random forests and artificial neural network

Structural Chemistry - In this study, a quantitative structure–property relationship (QSPR) was proposed using the random forests (RF) and artificial neural network (ANN) for determining the...     

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.     

Identifying conformational changes of the β2 adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators

The new β₂ Adrenoceptor (β₂AR) crystal structures provide a high-resolution snapshot of receptor interactions with two particular partial inverse agonists, (−)-carazolol and timolol. However, both experimental and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacological activities. In this work, we (1) established that the existing β₂AR crystallographic conformers can be extended to describe ligand/receptor interactions for additional antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, we derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial β₂AR agonists in VLS trials. Additionally, the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based β₂AR homology models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mathematical modeling of gas–liquid membrane contactors using random distribution of fibers

This work presents a mathematical model for gas absorption in microporous hollow fiber membrane contactors by using a random distribution of fibers. The chemical absorption of carbon dioxide into aqueous amine solutions and sulfur dioxide into water were simulated by this model. The nonlinear mathematical expressions of the component material balance for the liquid, membrane, and gas were solved simultaneously by using a numerical method. The results from the model were compared with four sets of different experimental data in the literature. In addition, the contactors were modeled based on the assumption of regular arrangement of fibers in the shell side by using Happel's free surface as well as plug flow models. The plug flow model was employed to compare the various available equations in the literature for the shell side mass transfer coefficient. The results indicate that the channeling of gas in the shell side decreases the efficiency of contactor significantly. It was found that the random distribution of fibers is a suitable method to simulate the commercial modules. The results also indicate that, the regular Happel's free surface model and the plug flow model are more suitable for handmade modules. The influence of shell side channeling on the contactor performance were investigated in different fiber packing densities, and in various gas and liquid flow rates.     

Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain–peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with Rosetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several Rosetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain–peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain–peptide interactions.     

Titration calorimetry of surfactant–drug interactions: Micelle formation and saturation studies

Isothermal titration calorimetry (ITC) was employed to monitor the addition of five model drugs to anionic surfactant based micelles, composed of sodium dodecyl sulfate (SDS), through to the point at which they were saturated with drug. Analysis of the resultant data using this newly developed method has confirmed the suitability of the technique to acquire such data with saturation limits established in all cases. Values for the point at which saturation occurred ranged from 17 molecules of theophylline per micelle at T = 298 K up to 63 molecules of caffeine per micelle at 310 K. Micellar systems can be disrupted by the presence of additional chemicals, such as the drugs used in this study, therefore a separate investigation was undertaken to determine the critical micellar concentration (CMC) for SDS in the presence of each drug at T = 298 K and 310 K using ITC. In the majority of cases, there was no appreciable alteration to the CMC of SDS with drug present.     

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.     

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.