George M. Spyrou

Structure-based virtual screening for drug discovery: principles, applications and recent advances.

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Comprehensive Field Synopsis and Systematic Meta-analyses of Genetic Association Studies in Cutaneous Melanoma

BACKGROUND Although genetic studies have reported a number of loci associated with cutaneous melanoma (CM) risk, a comprehensive synopsis of genetic association studies published in the field and systematic meta-analysis for all eligible polymorphisms have not been reported. METHODS We systematically annotated data from all genetic association studies published in the CM field (n = 145), including data from genome-wide association studies (GWAS), and performed random-effects meta-analyses across all eligible polymorphisms on the basis of four or more independent case-control datasets in the main analyses. Supplementary analyses of three available datasets derived from GWAS and GWAS-replication studies were also done. Nominally statistically significant associations between polymorphisms and CM were graded for the strength of epidemiological evidence on the basis of the Human Genome Epidemiology Network Venice criteria. All statistical tests were two-sided. RESULTS Forty-two polymorphisms across 18 independent loci evaluated in four or more datasets including candidate gene studies and available GWAS data were subjected to meta-analysis. Eight loci were identified in the main meta-analyses as being associated with a risk of CM (P < .05) of which four loci showed a genome-wide statistically significant association (P < 1 × 10(-7)), including 16q24.3 (MC1R), 20q11.22 (MYH7B/PIGU/ASIP), 11q14.3 (TYR), and 5p13.2 (SLC45A2). Grading of the cumulative evidence by the Venice criteria suggested strong epidemiological credibility for all four loci with genome-wide statistical significance and one additional gene at 9p23 (TYRP1). In the supplementary meta-analyses, a locus at 9p21.3 (CDKN2A/MTAP) reached genome-wide statistical significance with CM and had strong epidemiological credibility. CONCLUSIONS To the best of our knowledge, this is the first comprehensive field synopsis and systematic meta-analysis to identify genes associated with an increased susceptibility to CM.

MASS SPECTROMETRY-BASED PROTEOMICS IN REPRODUCTIVE MEDICINE

The emergence of powerful mass spectrometry-based proteomic techniques has added a new dimension to the field of biomedical research. Application of these high throughput methodologies in pregnancy-related pathology has contributed to the comprehension of the underlying pathophysiologies and the successful identification of relevant protein biomarkers that can potentially change early diagnosis and treatment of several medical conditions related to human pregnancy. Most of the existing research on human reproduction and gestation has focused on follicular fluid, cervical/vaginal fluid, and amniotic fluid. Although proteome technologies in reproductive medicine research are not as yet widely applied, characterization of the proteome of reproductive fluids can be expected to significantly improve maternal healthcare. This article aims to summarize the applications of mass spectrometry based technology on the most important and specific biological fluids related to reproduction and gestation.

PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficients

An empirical stopping criterion for the 2D-maximum-likelihood expectation-maximization (MLEM) iterative image reconstruction algorithm in positron emission tomography (PET) has been proposed. We have applied the MLEM algorithm on Monte Carlo generated noise-free projection data and studied the properties of the pixel updating coefficients (PUC) in the reconstructed images. Appropriate fitting lead to an analytical expression for the parameterization of the minimum value in the PUC vector for all non-zero pixels for a given number of detected counts, which can be employed as basis for the stopping criterion proposed. These results have been validated with simulated data from real PET images.

A comparative study of image features for classification of breast microcalcifications

Computer-aided diagnosis systems for mammography have been developed in order to assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant mammographic findings. The effectiveness of such systems is based on the image features extracted from mammograms, which are mainly related to the morphology, texture and optical density of the suspicious abnormality. There are many methodologies reported in the literature able to provide a mathematical description of a mammographic lesion. In this paper, we apply various feature extraction methodologies on cases containing clusters of microcalcifications. Our purpose is to compare their performance in large scale in terms of classification accuracy and to investigate their potentiality in discriminating benign from malignant clusters. Experiments were performed on 1715 cases (882 benign and 833 malignant) extracted from the Digital Database of Screening Mammography, which is the largest publicly available database of mammograms. The results of our study indicated that texture features outperformed the rest of the considered categories, while the combination of the best features optimized the classification results, leading to an area under the receiver operating characteristic curve equal to 0.82.

ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery

SUMMARY ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase. AVAILABILITY The ChemBioServer web application is available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/. CONTACT gspyrou@bioacademy.gr

A comparative study of multi-classification methods for protein fold recognition

Fold recognition based on sequence-derived features is a complex multi-class classification problem. In the current study, we comparatively assess five different classification techniques, namely multilayer perceptron and probabilistic neural networks, nearest neighbour classifiers, multi-class support vector machines and classification trees for fold recognition on a reference set of proteins that are organised in 27 folds and are described by 125-dimensional vectors of sequence-derived features. We evaluate all classifiers in terms of total accuracy, mutual information coefficient, sensitivity and specificity measurements using a ten-fold cross-validation method. A polynomial support vector machine and a multilayer perceptron of one hidden layer of 88 nodes performed better and achieved satisfactory multi-class classification accuracies (42.8% and 42.1%, respectively) given the complexity of the problem and the reported similar classification performances of other researchers.

Monte Carlo generated conversion factors for the estimation of average glandular dose in contact and magnification mammography

Magnification mammography is a special technique used in the cases where breast complaints are noted by a woman or when an abnormality is found in a screening mammogram. The carcinogenic risk in mammography is related to the dose deposited in the glandular tissue of the breast rather than the adipose, and average glandular dose (AGD) is the quantity taken into consideration during a mammographic examination. Direct measurement of the AGD is not feasible during clinical practice and thus, the incident air KERMA on the breast surface is used to estimate the glandular dose, with the help of proper conversion factors. Additional conversion factors adapted for magnification and tube voltage are calculated, using Monte Carlo simulation. The effect of magnification degree, tube voltage, various anode/filter material combinations and glandularity on AGD is also studied, considering partial breast irradiation. Results demonstrate that the estimation of AGD utilizing conversion factors depends on these parameters, while the omission of correction factors for magnification and tube voltage can lead to significant underestimation or overestimation of AGD. AGD was found to increase with filter materials k-absorption edge, anode materials k-emission edge, tube voltage and magnification. Decrease of the glandularity of the breast leads to higher AGD due to the increased penetrating ability of the photon beam in thick breasts with low glandularity.

Bioinformatics methods in drug repurposing for Alzheimer’s disease

Alarming epidemiological features of Alzheimers disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substances indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cells gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimers disease.

Updated field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma: the MelGene database.

We updated a field synopsis of genetic associations of cutaneous melanoma (CM) by systematically retrieving and combining data from all studies in the field published as of August 31, 2013. Data were available from 197 studies, which included 83,343 CM cases and 187,809 controls and reported on 1,126 polymorphisms in 289 different genes. Random-effects meta-analyses of 81 eligible polymorphisms evaluated in >4 data sets confirmed 20 single-nucleotide polymorphisms across 10 loci (TYR, AFG3L1P, CDK10, MYH7B, SLC45A2, MTAP, ATM, CLPTM1L, FTO, and CASP8) that have previously been published with genome-wide significant evidence for association (P<5 × 10(-8)) with CM risk, with certain variants possibly functioning as proxies of already tagged genes. Four other loci (MITF, CCND1, MX2, and PLA2G6) were also significantly associated with 5 × 10(-8)<P<1 × 10(-3). In supplementary meta-analyses derived from genome-wide association studies, one additional locus located 11 kb upstream of ARNT (chromosome 1q21) showed genome-wide statistical significance with CM. Our approach serves as a useful model in analyzing and integrating the reported germline alterations involved in CM.