Computational prediction of actin–actin interaction

Actin is one of the most abundant proteins in eukaryotic cells, where it plays key roles in cell shape, motility, and regulation. Actin is found in globular (G) and filamentous (F) structure in the cell. The helix of actin occurs as a result of polymerization of monomeric G-actin molecules through sequential rowing, is called F-actin. Recently, the crystal structure of an actin dimer has been reported, which details molecular interface in F-actin. In this study, the computational prediction model of actin and actin complex has been constructed base on the atomic model structure of G-actin. To this end, a docking simulation was carried out using predictive docking tools to obtain modeled structures of the actin–actin complex. Following molecular dynamics refinement, hot spots interactions at the protein interface were identified, that were predicted to contribute substantially to the free energy of binding. These provided a detailed prediction of key amino acid interactions at the protein–protein interface. The obtained model can be used for future experimental and computational studies to draw biological and functional conclusions. Also, the identified interactions will be used for designing next studies to understand the occurrence of F-actin structure.     

Albumin–drug interaction and its clinical implication

Background

Human serum albumin acts as a reservoir and transport protein for endogenous (e.g. fatty acids or bilirubin) and exogenous compounds (e.g. drugs or nutrients) in the blood. The binding of a drug to albumin is a major determinant of its pharmacokinetic and pharmacodynamic profile.

Scope of review

The present review discusses recent findings regarding the nature of drug binding sites, drug-albumin binding in certain diseased states or in the presence of coadministered drugs, and the potential of utilizing albumin–drug interactions in clinical applications.

Major conclusions

Drug–albumin interactions appear to predominantly occur at one or two specific binding sites. The nature of these drug binding sites has been fundamentally investigated as to location, size, charge, hydrophobicity or changes that can occur under conditions such as the content of the endogenous substances in question. Such findings can be useful tools for the analysis of drug–drug interactions or protein binding in diseased states. A change in protein binding is not always a problem in terms of drug therapy, but it can be used to enhance the efficacy of therapeutic agents or to enhance the accumulation of radiopharmaceuticals to targets for diagnostic purposes. Furthermore, several extracorporeal dialysis procedures using albumin-containing dialysates have proven to be an effective tool for removing endogenous toxins or overdosed drugs from patients.

General significance

Recent findings related to albumin–drug interactions as described in this review are useful for providing safer and efficient therapies and diagnoses in clinical settings. This article is part of a Special Issue entitled Serum Albumin.     

Molecular mechanism of an adverse drug–drug interaction of allopurinol and furosemide in gout treatment

Gout patients receiving a combination of allopurinol and furosemide require higher allopurinol doses to achieve the target serum urate (SU) of <6 mg/dl (Stamp et al., 2012) [1]. Our study aimed to identify the molecular basis for this observation. We used a fluorimetric assay to determine the impact of furosemide and oxypurinol (the active metabolite of allopurinol) on xanthine oxidase (XO) activity. Immunoblot analysis quantified expression of XO and AMP-kinase (AMPK) in drug-treated human liver (HepG2) and primary kidney (HRCE) cells. In silico analysis identified miR-448 as a potential XO-regulator, whose expression level in HepG2 cells was examined by qPCR.     

Automated feature engineering improves prediction of protein–protein interactions

Amino Acids - Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating...     

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.     

NRLMFβ: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug–target interaction prediction

Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug–target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug–target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the number of target proteins for a drug). This study aimed to address this issue by proposing NRLMF with beta distribution rescoring (NRLMFβ), which is an algorithm to improve the score of NRLMF. The score of NRLMFβ is equivalent to the value of the original NRLMF score when the concentration of the beta distribution becomes infinity. The beta distribution is known as a conjugative prior distribution of the Bernoulli distribution and can reflect the amount of interaction information to its shape based on Bayesian inference. Therefore, in NRLMFβ, the beta distribution was used for rescoring the NRLMF score. In the evaluation experiment, we measured the average values of area under the receiver operating characteristics and area under precision versus recall and the 95% confidence intervals. The performance of NRLMFβ was found to be better than that of NRLMF in the four types of benchmark datasets. Thus, we concluded that NRLMFβ improved the prediction accuracy of NRLMF. The source code is available at https://github.com/akiyamalab/NRLMFb.     

Characterisation of aptamer–target interactions by branched selection and high-throughput sequencing of SELEX pools

Nucleic acid aptamer selection by systematic evolution of ligands by exponential enrichment (SELEX) has shown great promise for use in the development of research tools, therapeutics and diagnostics. Typically, aptamers are identified from libraries containing up to 10¹⁶ different RNA or DNA sequences by 5–10 rounds of affinity selection towards a target of interest. Such library screenings can result in complex pools of many target-binding aptamers. New high-throughput sequencing techniques may potentially revolutionise aptamer selection by allowing quantitative assessment of the dynamic changes in the pool composition during the SELEX process and by facilitating large-scale post-SELEX characterisation. In the present study, we demonstrate how high-throughput sequencing of SELEX pools, before and after a single round of branched selection for binding to different target variants, can provide detailed information about aptamer binding sites, preferences for specific target conformations, and functional effects of the aptamers. The procedure was applied on a diverse pool of 2′-fluoropyrimidine-modified RNA enriched for aptamers specific for the serpin plasminogen activator inhibitor-1 (PAI-1) through five rounds of standard selection. The results demonstrate that it is possible to perform large-scale detailed characterisation of aptamer sequences directly in the complex pools obtained from library selection methods, thus without the need to produce individual aptamers.     

Modulation of the albumin–paraoxon interaction sites by fatty acids: Analysis by the molecular modeling methods

It is known that albumin can break the ester bonds in organophosphorus compounds (OPs). Amino acids responsible for esterase and pseudoesterase activity of albumin towards OPs are still not determined. It is assumed that Sudlow’s site I with residue Tyr150 exhibits the “true” esterase activity; and Sudlow’s site II containing residue Tyr411, a pseudoesterase one. Binding of fatty acids to albumin affects the efficiency of its interaction with xenobiotics; however, the effect of fatty acids on the interaction of albumin with OPs was not studied. The purpose of this work was to study the interaction of OPs with potential sites of albumin enzymatic activity and to examine the effect of fatty acids on the efficiency of such interaction using the molecular modeling methods by the example of paraoxon, a known inhibitor of acetylcholinesterase, and oleic acid. The structures of the protein complexes with paraoxon and oleic acid were determined by the molecular docking procedure; the conformational changes were calculated by the molecular dynamics method. It has been shown that sorption of oleic acid in one of the fatty acid-binding sites leads to the conformational changes in Sudlow’s sites I and II due to a “reversal” of the side chains of Arg410 and Arg257 residues by 90°. It has been found that this change in geometry reduces the affinity of Sudlow’s site I and increases the Sudlow’s site II affinity to paraoxon. The amino acid residue Ser193, which was previously identified as a site of possible albumin esterase activity, is not able to bind paraoxon efficiently. It is assumed that its activity can be affected by the interaction of the oleic acid molecules with other fatty acid-binding sites. It is hypothesized that the lesser toxicity of paraoxon compared to soman may be associated not only with its lower inhibitory activity against cholinesterases, but also with the increased affinity of paraoxon to albumin. It was concluded that albumin may serve as an alternative means of OP detoxification.     

Herb–drug interaction of Andrographis paniculata extract and andrographolide on the pharmacokinetics of theophylline in rats

Herb–drug interaction has become a serious problem since herbal medicine is extensively used in the modern world. This study investigates effects of Andrographis paniculata extract (APE) and its major component, andrographolide (AG), on the pharmacokinetics of theophylline, a typical substrate of cytochrome P450 1A2 enzyme, in rats. After APE or AG pretreatment for 3 days, on the fourth day rats were administered theophylline via femoral vein cannula. The blood theophylline levels were monitored by microdialysis sampling combined with HPLC-UV. The results indicated that the clearance of theophylline was significantly increased and the area under concentration–time curve (AUC) was reduced in both AG and APE pretreated groups at low-dose theophylline administration (1 mg/kg). The elimination half-life (t_1/2β) and mean residence time (MRT) of theophylline were shortened by 14% and 17%, respectively, in the AG pretreated group when high-dose theophylline (5 mg/kg) was given. However, theophylline accumulated in rat of the group with APE pretreatment. This phenomenon suggests that some other herbal components contained in APE may interact with theophylline and retard its elimination when theophylline was administered at a high dose. Our results suggest that patients who want to use CYP1A2-metabolized drugs such as caffeine and theophylline should be advised of the potential herb–drug interaction, to reduce therapeutic failure or increased toxicity of conventional drug therapy.     

Spin–spin interaction between molybdenum and one of the iron–sulphur systems of xanthine oxidase and its relevance to the enzymic mechanism

1. Electron-paramagnetic-resonance (e.p.r.) studies at 9 and 35GHz at helium temperatures have given new information relating to the structure and mechanism of action of xanthine oxidase. 2. As reported by others, the enzyme gives two types of e.p.r. signal attributed to iron-sulphur systems. The first has g(av.)=1.95. Parameters of the second are determined as g(1) 2.12, g(2) 2.007 and g(3) 1.91, with g(av.)=2.01. This species seems to have a slightly higher redox potential than the former one. 3. Temperature-dependent changes in the form of Mo(v) e.p.r. signals from the enzyme, observed under certain conditions, are shown to be due to weak spin-spin interaction between Mo(v) and g(av.)=1.95 Fe/S. The phenomenon has been studied most fully for the Slow Mo(v) signal. Here, the spectral change takes the form of an additional approximately isotropic 11G splitting, detected below about 45 degrees K only. Samples without Fe/S reduced showed no such changes of spectrum. 4. Similar spectral changes were observed in the Rapid Mo(v) signals, obtained in rapid-freezing experiments, but only in samples corresponding to relatively long reaction times with the substrate. It is suggested therefore that the phenomenon may provide a means of distinguishing enzyme centres with Mo only reduced from those in which both Mo and Fe/S are reduced. 5. Additional rapid-freezing data tending to support a two- rather than a one-electron transfer of reducing equivalents from substrates to xanthine oxidase are reported.     

Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation

We present a new method for predicting protein–ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.     

A rapid approach for characterization of thiol-conjugated antibody–drug conjugates and calculation of drug–antibody ratio by liquid chromatography mass spectrometry

We present the demonstration of a rapid “middle-up” liquid chromatography mass spectrometry (LC–MS)-based workflow for use in the characterization of thiol-conjugated maleimidocaproyl-monomethyl auristatin F (mcMMAF) and valine-citrulline-monomethyl auristatin E (vcMMAE) antibody–drug conjugates. Deconvoluted spectra were generated following a combination of deglycosylation, IdeS (immunoglobulin-degrading enzyme from Streptococcus pyogenes) digestion, and reduction steps that provide a visual representation of the product for rapid lot-to-lot comparison—a means to quickly assess the integrity of the antibody structure and the applied conjugation chemistry by mass. The relative abundance of the detected ions also offer information regarding differences in drug conjugation levels between samples, and the average drug–antibody ratio can be calculated. The approach requires little material (<100 μg) and, thus, is amenable to small-scale process development testing or as an early component of a complete characterization project facilitating informed decision making regarding which aspects of a molecule might need to be examined in more detail by orthogonal methodologies.     

Bacterial and Fungal Communities of Gioddu as Revealed by PCR–DGGE Analysis

Indian Journal of Microbiology - Gioddu is the sole variety of fermented milk originating in Italy. Despite the long history of consumption, Gioddu still represents an undisclosed source of...     

Escalating burden of breast cancer in southern Thailand: Analysis of 1990–2010 incidence and prediction of future trends

BackgroundThailand is undergoing an epidemiologic transition, with decreasing incidence of infectious diseases and increasing rates of chronic conditions, including cancer. Breast cancer has the highest incidence rates among females both in the southern region Thailand and throughout Thailand. However, there is a lack of research on the epidemiology of this and other cancers.MethodsHere we use cancer incidence data from the Songkhla Cancer Registry to characterize and analyze the incidence of breast cancer in Southern Thailand. We use joinpoint analysis, age-period-cohort models and nordpred analysis to investigate the incidence of breast cancer in Southern Thailand from 1990 to 2010 and project future trends from 2010 to 2029.ResultsWe found that age-adjusted breast cancer incidence rates in Southern Thailand increased by almost 300% from 1990 to 2010 going from 10.0 to 27.8 cases per 100,000 person-years. Both period and cohort effects played a role in shaping the increase in incidence. Three distinct incidence projection methods consistently suggested that incidence rates will continue to increase in the future with incidence for women age 50 and above increasing at a higher rate than for women below 50.ConclusionsTo date, this is the first study to examine Thai breast cancer incidence from a regional registry. This study provides a basis for future planning strategies in breast cancer prevention and to guide hypotheses for population-based epidemiologic research in Thailand.     

Control of glioma cell death and differentiation by PKM2–Oct4 interaction

Glioma stem cells are highly resistant to cell death and as such are supposed to contribute to tumor recurrence by eluding anticancer treatments. Here, we show that spheroids that contain rat neural stem cells (NSCs) or rat glioma stem cells (cancer stem cells, CSCs) express isoforms 1 and 2 of pyruvate kinase (PKM1 and PKM2); however, the expression of PKM2 is considerably higher in glioma spheroids. Silencing of PKM2 enhances both apoptosis and differentiation of rat and human glioma spheroids. We establish that PKM2 was implicated in glioma spheroid differentiation through its interaction with Oct4, a major regulator of self-renewal and differentiation in stem cells. The small molecule Dichloroacetate (DCA), a pyruvate dehydrogenase kinase inhibitor, increases the amount of PKM2/Oct4 complexes and thus inhibited Oct4-dependent gene expression. Taken together, our results highlight a new molecular pathway through which PKM2 can manage gliomagenesis via the control of glioma stemness by Oct4.     

Structural basis of protein–protein interaction studied by NMR

This paper describes efforts of the structural genomics project in the nuclear magnetic resonance (NMR) laboratory at the University of Science and Technology of China. This structural genomics project is biological-functional driven. Targets are mainly selected from two systems: proteins related with regulation of gene expression in humans and other eukaryotes, and proteins existing in the cell junction in humans. The majority of proteins selected from these two systems are related with human health and diseases, and some are potential drug targets. Twenty-five protein structures from Homo sapiens and other eukaryotes have been determined during last 5 years in this laboratory. Nuclear magnetic resonance (NMR) spectroscopy is highly suited to investigate molecular interactions at a close physiological condition and is particularly suited for the study of low-affinity, transient complexes. It can provide information on protein surface interaction, their complex structure, and their dynamic properties during protein recognition. Several examples are given in this paper.     

Fluid–structure interaction simulation of the brain–skull interface for acute subdural haematoma prediction

Traumatic brain injury is a leading cause of disability and mortality. Finite element-based head models are promising tools for enhanced head injury prediction, mitigation and prevention. The reliability of such models depends heavily on adequate representation of the brain–skull interaction. Nevertheless, the brain–skull interface has been largely simplified in previous three-dimensional head models without accounting for the fluid behaviour of the cerebrospinal fluid (CSF) and its mechanical interaction with the brain and skull. In this study, the brain–skull interface in a previously developed head model is modified as a fluid–structure interaction (FSI) approach, in which the CSF is treated on a moving mesh using an arbitrary Lagrangian–Eulerian multi-material formulation and the brain on a deformable mesh using a Lagrangian formulation. The modified model is validated against brain–skull relative displacement and intracranial pressure responses and subsequently imposed to an experimentally determined loading known to cause acute subdural haematoma (ASDH). Compared to the original model, the modified model achieves an improved validation performance in terms of brain–skull relative motion and is able to predict the occurrence of ASDH more accurately, indicating the superiority of the FSI approach for brain–skull interface modelling. The introduction of the FSI approach to represent the fluid behaviour of the CSF and its interaction with the brain and skull is crucial for more accurate head injury predictions.

     

Analysis of trait–performance–fitness relationships reveals pollinator-mediated selection on orchid pollination traits

Premise

The role of pollinators in evolutionary floral divergence has spurred substantial effort into measuring pollinator-mediated phenotypic selection and its variation in space and time. For such estimates, the fitness consequences of pollination processes must be separated from other factors affecting fitness.

Methods

We built a fitness function linking phenotypic traits of food-deceptive orchids to female reproductive success by including pollinator visitation and pollen deposition as intermediate performance components and used the fitness function to estimate the strength of pollinator-mediated selection through female reproductive success. We also quantified male performance as pollinarium removal and assessed similarity in trait effects on male and female performance.

Results

The proportion of plants visited at least once by an effective pollinator was moderate to high, ranging from 53.7% to 85.1%. Tall, many-flowered plants were often more likely to be visited and pollinated. Given effective pollination, pollen deposition onto stigmas tended to be more likely for taller plants. Pollen deposition further depended on traits affecting the physical fit of pollinators to flowers (flower size, spur length), though the exact relationships varied in time and space. Using the fitness function to assess pollinator-mediated selection through female reproductive success acting on multiple traits, we found that selection varied detectably among taxa after accounting for sampling uncertainty. Across taxa, selection on most traits was stronger on average and more variable when pollination was less reliable.

Conclusions

These results support pollination-related trait–performance–fitness relationships and thus pollinator-mediated selection on traits functionally involved in the pollination process.     

Structural and energetic insights into sequence-specific interaction in DNA–drug recognition: development of affinity predictor and analysis of binding selectivity

Although the molecular mechanism and thermodynamic profile of a wide variety of chemical agents have been examined intensively in the past decades in terms of specific recognition of their protein receptors, to date the physicochemical nature of DNA–drug recognition and association still remains largely unexplored. The present study focused on understanding the structural basis, energetic landscape, and biological implications underlying the binding of small-molecule ligands to their cognate or non-cognate DNA receptors. First, a new method to capture the structural features of DNA–drug complex architecture was proposed and then used to correlate the extracted features with binding affinity of the complexes. By employing this method, a statistical regression-based predictor was developed to quantitatively evaluate the interaction potency of drug compounds with DNA in a fast and reliable manner. Subsequently, we use the predictor to examine the binding behavior of a number of structure-available, affinity-known DNA–drug complexes as well as a large pool of randomly generated DNA decoys in complex with the same drugs. It was found that (1) as compared with protein–DNA recognition, small-molecule agents have relatively low specificity in selecting their cognate DNA targets from the background of numerous random decoys; (2) the abundance of A–T base pairs in the DNA core motif exhibits a significant positive correlation with the affinity of drug ligand binding to the DNA receptor; and (3) high affinity seems not to be closely related to high selectivity for a DNA-targeting drug, although high-affinity drug entities have a greater possibility of being ranked computationally as top binders. We hope that this work will provide a preliminary insight into the molecular origin of sequence-specific interactions in DNA–drug recognition.