Using Protein Homology Models for Structure-Based Studies: Approaches to Model Refinement

Homology modeling is a computational methodology to assign a 3-D structure to a target protein when experimental data are not available. The methodology uses another protein with a known structure that shares some sequence identity with the target as a template. The crudest approach is to thread the target protein backbone atoms over the backbone atoms of the template protein, but necessary refinement methods are needed to produce realistic models. In this mini-review anchored within the scope of drug design, we show the validity of using homology models of proteins in the discovery of binders for potential therapeutic targets. We also report several different approaches to homology model refinement, going from very simple to the most elaborate. Results show that refinement approaches are system dependent and that more elaborate methodologies do not always correlate with better performances from built homology models

Lietuvos gyventojų reprezentatyvios imties psichologinės gerovės struktūra

Some studies focusing on well-being were unable to distinguish two – hedonic and eudaimonic – components of well-being (Vitterso et al., 2010; Kashdan et al., 2008; Šarakauskienė, 2012). Previous comparatively smaller sample researches, conducted by the authors of this article, also yielded a single psychological well-being phenomenon (Bagdonas et al., 2012; 2013). Although a slightly different structure was established, studies could not isolate two traditional – hedonic and eudaimonic – components. The aim of this study was to verify the structure of psychological well-being in a representative Lithuanian sample using the original Lithuanian Psychological Well-being Scale.Methods. 1202 Lithuanian citizens aged 16–89 were enrolled in the research (M = 45.5 years, SD = 18.7 years). The study sample was representative of the Lithuanian population aged from 16 to 89 years according to gender, age, educational level, nationality, type of settlement, and region. The Lithuanian Psychological Well-being Scale, which consists of seven subscales (optimism / control, satisfaction with living standards, negative affectivity, satisfaction with relatives, satisfaction with interpersonal relations, satisfaction with physical health, satisfaction with work) was used in this study. Two items that depict satisfaction with living in Lithuania were not included in the factor analysis. The analysis of the data was conducted using the SPSS 20 and structural equation modelling AMOS software.Results. The data obtained from the Lithuanian representative sample supports the same factor structure of the Lithuanian Psychological Well-being Scale (χ2 = 4851.0; df = 1487; p < 0.0001; RMSEA = 0.043; CFI = 0.9; TLI = 0.889) that was established by previous studies. The scale consists of optimism / control, satisfaction with living standards, negative affectivity, satisfaction with relatives, satisfaction with interpersonal relations, satisfaction with physical health, and satisfaction with work subscales. All the mentioned subscales have a high internal consistency (Cronbach α is no less than 0.8).The testing of the second-order factor models has shown that psychological well-being is a coherent and unanimous phenomenon; even when two second-order factors representing the hedonic and eudaimonic aspects of well-being are omitted in the model, the correlation between those two is very high (>0.8). The obtained results do not support the idea that the construct of well-being consists of two different foci; it seems like there is no hedonic or eudaimonic well-being, but just a person’s psychological well-being.Straipsnyje pristatomos 16–89 metų Lietuvos gyventojų reprezentatyvios imties asmens psichologinės gerovės struktūros paieškos, naudojant Lietuviškąją psichologinės gerovės skalę (LPGS). Atlikus patvirtinamąją faktorių analizę išryškėjo vieno faktoriaus psichologinės gerovės modelis, apimantis abu gerovės aspektus – hedoninį ir eudaimoninį. Išskirti septyni gerovės komponentai: optimizmas / kontrolė; pasitenkinimas pragyvenimo lygiu; negatyvus emocingumas; pasitenkinimas šeima ir artimaisiais; pasitenkinimas tarpasmeniniais santykiais; pasitenkinimas fizine sveikata; pasitenkinimas darbu. Du teiginiai, matuojantys pasitenkinimą gyvenimu Lietuvoje, į faktorių analizę nebuvo įtraukti. Skalei ir subskalėms būdingas labai geras vidinis suderintumas

The Effective Fragment Molecular Orbital Method for Fragments Connected by Covalent Bonds

We extend the effective fragment molecular orbital method (EFMO) into treating fragments connected by covalent bonds. The accuracy of EFMO is compared to FMO and conventional ab initio electronic structure methods for polypeptides including proteins. Errors in energy for RHF and MP2 are within 2 kcal/mol for neutral polypeptides and 6 kcal/mol for charged polypeptides similar to FMO but obtained two to five times faster. For proteins, the errors are also within a few kcal/mol of the FMO results. We developed both the RHF and MP2 gradient for EFMO. Compared to ab initio, the EFMO optimized structures had an RMSD of 0.40 and 0.44 {\AA} for RHF and MP2, respectively.Comment: Revised manuscrip

Structure-Based Discovery of A2A Adenosine Receptor Ligands

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A(2A) adenosine receptor, based on complementarity to its recently determined structure. The A(2A) adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 microM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A(2A) versus the related A(1) and A(3) subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target

Predicting olfactory receptor neuron responses from odorant structure

Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusions The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their 'receptive fields'. Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

Background: The human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention. Methodology/Principal Findings: We present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium. Conclusions/Significance: This study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection

Predictive Power of Molecular Dynamics Receptor Structures in Virtual Screening

Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range