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The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

Based on the finding that a central antihypertensive agent with high affinity for I1-type imidazoline receptors ? rilmenidine, shows cytotoxic effects on cultured cancer cell lines, it has been suggested that imidazoline receptors agonists might have a therapeutic potential in the cancer therapy. Nevertheless, potential rilmenidine side effects caused by activation of α-adrenoceptors, or other associated receptors and enzymes, might hinder its therapeutic benefits. Considering that human α-adrenoceptors belong to the rhodopsin-like class A of G-protein-coupled receptors (GPCRs) it is reasonable to assume that imidazolines might have the affinity for other receptors from the same class. Therefore, to investigate possible off-target effects of imidazoline ligands we have prepared a reverse docking protocol on class A GPCRs, using imidazoline ligands and their decoys. To verify our in silico results, three ligands with high scores and three ligands with low scores were tested for antagonistic activity on α2- adrenoceptors.

Motivation Proteins containing tandem repeats (TRs) are abundant, frequently fold in elongated non-globular structures and perform vital functions. A number of computational tools have been developed to detect TRs in protein sequences. A blurred boundary between imperfect TR motifs and non-repetitive sequences gave rise to necessity to validate the detected TRs. Results Tally-2.0 is a scoring tool based on a machine learning (ML) approach, which allows to validate the results of TR detection. It was upgraded by using improved training datasets and additional ML features. Tally-2.0 performs at a level of 93% sensitivity, 83% specificity and an area under the receiver operating characteristic curve of 95%. Availability and implementation Tally-2.0 is available, as a web tool and as a standalone application published under Apache License 2.0, on the URL https://bioinfo.crbm.cnrs.fr/index.php? route=tools&tool=27. It is supported on Linux. Source code is available upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Recent outbreaks of NDM-1-positive Entero-bacteriaceae in Great Britain and India and the highly pathogenic Escherichia coli strain EHEC O104:H4 in Germany and some other E.U. countries point out an urgent need for the ability to decide on appropriate antibiotics to treat multi-drug-resistant (MDR) bacteria. Here we propose a simple criterion for choosing antibiotics based on characteristics of electron donor and acceptor properties. Using molecular descriptors, such as electron-ion interaction potential (EIIP) and average quasi-valence number (AQVN), which can describe potential long-range interactions between therapeutic molecules and their targets, we have been able to suggest appropriate antibiotics for treatment of MDR bacterial infections. To demonstrate the prospective usefulness of these molecular descriptors we have used this informatics system to propose that pleuromutilins and nitrofurans could be effective against of NDM-1-positive Enterobacteriacea and that aminoglycosides, macrolides and pluromutilins (and possibly nitrofurans) could be suitable for treatment of the highly pathogenic Escherichia coli strain EHEC O104:H4. Similarly, because of their specific electronic properties, we can also suggest antibiotics that could be potentially effective against other MDR bacteria. The proposed antibiotics should be further evaluated for their treatment potentials.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

This study aimed to functionally characterize β2-adrenergic (β2AR) and insulin receptor (IR) heteromers in regard to β-arrestin 2 (βarr2) recruitment and cAMP signaling and to examine the involvement of the cytoplasmic portion of the IR β chain in heteromerization with β2AR. Evidence for β2AR:IR:βarr2 complex formation and the specificity of the IR:βarr2 interaction was first provided by bioinfomatics analysis. Receptor-heteromer investigation technology (HIT) then provided functional evidence of β2AR:IR heterodimerization by showing isoproterenol-induced but not insulin-induced GFP2-βarr2 recruitment to the β2AR:IR complex; the IR:βarr2 interaction was found to only be constitutive. The constitutive IR:βarr2 BRET signal (BRETconst) was significantly smaller in cells coexpressing IR-RLuc8 and a GFP2-βarr2 1-185 mutant lacking the proposed IR binding domain. β2AR:IR heteromerization also influenced the pharmacological phenotype of β2AR, i.e., its efficacy in recruiting βarr2 and activating cAMP signaling. Evidence suggesting involvement of the cytoplasmic portion of the IR β chain in the interaction with β2AR was provided by BRET2 saturation and HIT assays using an IR 1-1271 stop mutant lacking the IR C-terminal tail region. For the complex consisting of IR 1-1271-RLuc8:β2AR-GFP2, saturation was not reached, most likely reflecting random collisions between IR 1-1271 and β2AR. Furthermore, in the HIT assay, no substantial agonist-induced increase in the BRET2 signal was detected that would be indicative of βarr2 recruitment to the IR 1-1271:β2AR heteromer. Complementary 3D visualization of β2AR:IR provided supporting evidence for stability of the heterotetramer complex and identified amino acid residues involved in β2AR:IR heteromerization. This article is part of the Special Issue entitled 'Receptor heteromers and their allosteric receptor-receptor interactions'.

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I-1-type imidazoline receptors (I-1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from alpha(2)-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine- derived compounds with anticancer potential and devoid of alpha(2)-adrenoceptor effects by means of ligand-and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to alpha(2)-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers. (C) 2016 Elsevier Ltd. All rights reserved.

The genetic and environmental factors and their interaction contribute to sports performance. So far, it has been identified a large number of genetic markers associated with sports performance and risk of sports injuries. Sports genomics is a relatively young scientific discipline and the necessary additional complex research on a large number of participants is required before scientific results in this field could be applicable in practice. At present, the application of tests based on genetic information for sport talent identification or recommendations for personalized training, in order to achieve optimal sport performance, is not scientifically justified. It is also necessary to carefully consider all the ethical issues related to such testing in children.

This study aimed to functionally characterize β

This study aimed to design and functionally characterize peptide mimetics of the nanobody (Nb) related to the β2-adrenergic receptor (β2-AR) (nanobody-derived peptide, NDP). We postulated that the computationally derived and optimized complementarity-determining region 3 (CDR3) of Nb is sufficient for its interaction with receptor. Sequence-related Nb-families preferring the agonist-bound active conformation of β2-AR were analysed using the informational spectrum method (ISM) and β2-AR:NDP complexes studied using protein-peptide docking and molecular dynamics (MD) simulations in conjunction with metadynamics calculations of free energy binding. The selected NDP of Nb71, designated P3, was 17 amino acids long and included CDR3. Metadynamics calculations yielded a binding free energy for the β2-AR:P3 complex of ΔG = (−7.23 ± 0.04) kcal/mol, or a Kd of (7.9 ± 0.5) μM, for T = 310 K. In vitro circular dichroism (CD) spectropolarimetry and microscale thermophoresis (MST) data provided additional evidence for P3 interaction with agonist-activated β2-AR, which displayed ~10-fold higher affinity for P3 than the unstimulated receptor (MST-derived EC50 of 3.57 µM vs. 58.22 µM), while its ability to inhibit the agonist-induced interaction of β2-AR with β-arrestin 2 was less evident. In summary, theoretical and experimental evidence indicated that P3 preferentially binds agonist-activated β2-AR.

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.

The Ebola 2014/2015 outbreak has had devastating effects on the people living in West Africa. The spread of the disease in endemic countries and the potential introduction of sporadic cases in other continents points out the global health threat of Ebola virus disease (EVD). Despite the urgent need for treating EVD, there are no approved treatments. Given the lack of treatments available, alternative therapeutic strategies have had to be used.This article summarizes the unregistered therapeutics that were used to treat patients during the Ebola 2014/2015 outbreak, in addition to approaches used for the selection of candidate drugs. The article also proposes potential theoretical criterion for use in virtual screening of molecular libraries for candidate Ebola drugs.In the absence of approved therapeutics for EVD, experimental drugs have had to be used. The repurposing of approved drugs for the treatment of EVD, as an alternative therapeutic strategy, has also been suggested. Screening in vitro- and in silico-approved drugs revealed several promising candidates but further testing is required to test their efficacy. All these therapeutic approaches are, however, only short-term solutions and there is still an urgent need for the development of specific drugs for the current and future outbreaks.

Summary The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions. Availability and Implementation http://www.vin.bg.ac.rs/180/tools/tfpred.php. Supplementary information Supplementary data are available at Bioinformatics online.

The group of imidazoline-1 receptors (I(1)-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I(1)-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I(1)-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I(1)-IR agonist), moxonidine (moderate I(1)-IR agonist), and efaroxan (I(1)-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I(1)-IR ligands (agonists, partial agonists, and antagonists) were carried out on I(1)-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I(1)-IR agonistic and I(1)-IR antagonistic activity and created regression model for prediction of I(1)-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I(1)-IR agonists and I(1)-IR antagonists. The most promising I(1)-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I(1)-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I(1)-IR antagonists or partial I(1)-IR agonists. The performed theoretical study provides reliable system for evaluation of I(1)-IR agonistic and I(1)-IR antagonistic activity of novel I(1)-IR ligands, as drug candidates with anticancer activities.

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins., SCOPUS: ar.j, info:eu-repo/semantics/published

Influenza A virus (IAV) subtypes against which little or no pre-existing immunity exists in humans represent a serious threat to global public health. Monitoring of IAV in animal hosts is essential for early and rapid detection of potential pandemic IAV strains to prevent their spread. Recently, the increased pandemic potential of the avian-like swine H1N1 IAV A/swine/Guangdong/104/2013 has been suggested. The virus is infectious in humans and the general population seems to lack neutralizing antibodies against this virus. Here we present an in silico analysis that shows a strong human propensity of this swine virus further confirming its pandemic potential. We suggest mutations which would further enhance its human propensity. We also propose conserved antigenic determinants which could serve as a component of a prepandemic vaccine. The bioinformatics tool, which can be used to further monitor the evolution of swine influenza viruses towards a pandemic virus, are described here and are made publically available (http://www.vin.bg.ac.rs/180/tools/iav_mon.php; http://www.biomedprotection.com/iav_mon.php).

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent., We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF)., This is the final version of the article. It first appeared from BioMed Central at http://dx.doi.org/10.1186/s13059-016-1037-6.

Recently, the new concept of the long-range intermolecular interactions in biological systems has been proposed. Combined use of molecular modeling techniques and the screening techniques based on the long-range interaction concept could significantly improve and accelerate discovery of new HIV drugs. However, any hit identified in silico needs to be characterized with respect to its biological target by enzymatic studies. Combined use of the in silico screening and the enzymatic studies allows an efficient selection of new anti-HIV drugs.The focus of this article is on the in silico screening of molecular libraries for candidate new HIV drugs, which is based on the molecular descriptors determining the long-range interaction between the drugs and their therapeutic target. This article also reviews the techniques for enzyme kinetic studies which are required for optimization of in silico selected candidate anti-HIV drugs.The novel approach of combining in silico screening techniques with enzymatic studies enables the accurate measurement of the quantitative descriptors of ligand-enzyme interactions. This novel method is a powerful tool for new anti-HIV drug discovery which can also reduce the drug development costs.

Long-range intermolecular interactions (interactions at distances between 100 and 1000 Å) play an important role in the interaction between drugs and therapeutic targets, and design techniques based on this concept could significantly improve and accelerate new drug discovery. Understanding these long-range intermolecular interactions will also help further our understanding of the molecular mechanisms and the underlying basic biological processes.This article looks at the physical bases of long-range intermolecular interactions in biological systems with a brief review of the literature data to support this concept. The article also gives some examples of techniques used in drug discovery that were based on the long-range intermolecular interaction concept.The electron-ion interaction potential (EIIP) and average quasivalence number (AQVN) concepts shed new light on the role of long-range intermolecular interactions in biological systems. Further research of physicochemical mechanisms underlying long-range interactions between biological molecules is necessary for a better understanding of the basic biological processes. The addition of the computer-aided design techniques based on the EIIP/AQVN concept to the research and development will lead not only to a significant reduction in cost but also to an acceleration in the development of new drugs.

Coronary artery disease (CAD) and myocardial infarction (MI) are recognized as leading causes of mortality in developed countries. Although typically associated with behavioral risk factors, such as smoking, sedentary lifestyle, and poor dietary habits, such vascular phenotypes have also long been recognized as being related to genetic background. We review the currently available data concerning genetic markers for CAD in English and non-English articles with English abstracts published between 2003 and 2018. As genetic testing is increasingly available, it may be possible to identify adequate genetic markers representing the risk profile and to use them in a clinical setting. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Here we demonstrated that the 'loss of function' of not-rearranged c-ABL in chronic myeloid leukemia (CML) is promoted by its cytoplasmic compartmentalization bound to 14-3-3 sigma scaffolding protein. In particular, constitutive tyrosine kinase (TK) activity of p210 BCR-ABL blocks c-Jun N-terminal kinase (JNK) phosphorylation leading to 14-3-3 sigma phosphorylation at a critical residue (Ser(186)) for c-ABL binding in response to DNA damage. Moreover, it is associated with 14-3-3 sigma over-expression arising from epigenetic mechanisms (promoter hyper-acetylation). Accordingly, p210 BCR-ABL TK inhibition by the TK inhibitor Imatinib mesylate (IM) evokes multiple events, including JNK phosphorylation at Thr(183), p38 mitogen-activated protein kinase (MAPK) phosphorylation at Thr(180), c-ABL de-phosphorylation at Ser residues involved in 14-3-3 binding and reduction of 14-3-3 sigma expression, that let c-ABL release from 14-3-3 sigma and nuclear import, and address BCR-ABL-expressing cells towards apoptotic death. Informational spectrum method (ISM), a virtual spectroscopy method for analysis of protein interactions based on their structure, and mathematical filtering in cross spectrum (CS) analysis identified 14-3-3 sigma/c-ABL binding sites. Further investigation on CS profiles of c-ABL- and p210 BCR-ABL-containing complexes revealed the mechanism likely involved 14-3-3 precluded phosphorylation in CML cells.

Human immunodeficiency virus (HIV) has infected 50 million people worldwide and killed 16 million so far, and the epidemic is still spreading with 16,000 new cases of HIV infection daily and a projection of 100million infected individuals by the end of the next decade. There is no question that a safe and effective acquired immunodeficiency syndrome (AIDS) vaccine is urgently needed to bring the current AIDS pandemic under control. But, is preventive AIDS vaccine an attainable goat? Unfortunately, the results of many laboratory and clinical studies over the past two decades are not encouraging. We comment on the efficacy, safety and ethics of AIDS vaccine, and the urgent need for a new strategy for AIDS vaccine development. (c) 2007 Elsevier Ltd. All. rights reserved. 1st International Symposium on Genetic and Immune Correlates of HIV Infection and Vaccine-Induced Immunity, Jun 10-13, 2007, Budapest, Hungary

The C-terminus of the second conserved region of HIV-1 gp120 represents a functionally important domain, as it encompasses amino acids directly involved in the binding to the CD4 receptor and in post-receptor binding events. Previous studies have suggested that antibodies with specific affinity to a 23 amino acids-long NTM polypeptide, derived from this HIV-1 gp120 domain, may be involved in the control of HIV disease progression. In the current work, we searched for NTM-recognizing antibodies in specific cohorts of HIV-1 infected individuals, including long-term nonprogressors (LTNP) and progressors. For this purpose, we employed a previously defined bioinformatics criterion for design of an NTM peptide mimetic to select an octapeptide, NTMs (FTDNAKTI), which is more suitable for use in a solid-state enzyme-linked immunosorbent assay (ELISA). Our results show that NTMs-reactive antibodies are significantly more prevalent (p < 0.01) in LTNP as compared to progressors and healthy control subjects, indicating their association with non-progressive infection. The presence of antibodies recognizing the second conserved region of the HIV-1 gp120 derived peptide, NTMs, in LTNP sera suggest that these antibodies could be of considerable interest for development of anti-HIV immune-based therapies and vaccines.

Flavonoid compounds represent an important natural source of antiretrovirals for AIDS therapy due to their significant anti-HIV-1 activity and low toxicity. Here we propose a simple theoretical criterion to discriminate active from inactive flavonoids that is suitable for rapid in silico screening of flavonoid libraries, and selection and optimization of lead compounds with anti-HIV-1 activity. (c) 2007 Elsevier Ltd. All rights reserved.

The development of a new therapeutic drug is a complex, lengthy and expensive process. On average, only one out of 10,000 - 30,000 originally synthesized compounds will clear all the hurdles on the way to becoming a commercially available drug. The process of early and full preclinical discovery and clinical development for a new drug can take twelve to fifteen years to complete, and cost approximately 800 million dollars. The field of bioinformatics has become a major part of the drug discovery pipeline playing a key role in improvement and acceleration of this time and money consuming process. Here we reviewed the application of the EIIP/ISM bioinformatics concept for the development of new drugs. This approach, connecting the electron-ion interaction potential of organic molecules and their biological properties, can significantly reduce development time through (i) identification of promising lead compounds that have some activity against a disease by fast virtual screening of the large molecular libraries, (ii) refinement of selected lead compounds in order to increase their biological activity, and (iii) identification of domains of proteins and nucleotide sequences representing potential targets for therapy. Special attention is paid in this review to the application of the EIIP/ISM bioinformatics platform along with other experimental techniques (screening of a phage displayed peptide libraries, testing selected peptides and small molecules for antiviral activity in vitro) in development of HIV entry inhibitors, representing a new generation of the AIDS drugs.

The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.

The large 2014/2015 Ebola virus outbreak in West Africa points out the urgent need to develop new preventive and therapeutic approaches that are effective against Ebola viruses and can be rapidly utilized. Recently, a simple theoretical criterion for the virtual screening of molecular libraries for candidate inhibitors of Ebola virus infection was proposed. Using this method the ‘drug space’ was screened and 267 approved and 382 experimental drugs as candidates for treatment of the Ebola virus disease (EVD) have been selected. Detailed analysis of these drugs revealed the non-steroidal anti-inflammatory drug ibuprofen as an inexpensive, widely accessible and minimally toxic candidate for prevention and treatment of EVD. Furthermore, the molecular mechanism underlying this possible protective effect of ibuprofen against EVD is suggested in this article.

Maraviroc is a first-in-class antiretroviral (ARV) drug acting on a host cell target (CCR5), which blocks the entry of the HIV virus into the cell. Maraviroc is currently indicated for combination ARV treatment in adults infected only with CCR5-tropic HIV-1.This drug discovery case history focuses on the key studies that led to the discovery and approval of maraviroc, as well as on post-launch clinical reports. The article is based on the data reported in published preclinical and clinical studies, conference posters and on drug package data.The profound understanding of HIV's entry mechanisms has provided a strong biological rationale for targeting the chemokine receptor CCR5. The CCR5-antagonist mariviroc, with its unique mode of action and excellent safety profile, is an important therapeutic option for HIV patients. In general, the authors believe that targeting host factors is a useful approach for combating new and re-emerging transmissible diseases, as well as pathogens that easily become resistant to common antiviral drugs. Maraviroc, offering a potent and safe cellular receptor-mediated pharmacological response to HIV, has paved the way for the development of a new generation of host-targeting antivirals.

The worst Ebola virus (EV) outbreak in history has hit Liberia, Sierra Leone and Guinea hardest and the trend lines in this crisis are grave, and now represents a global public health threat concern. Limited therapeutic and/or prophylactic options are available for people suffering from Ebola virus disease (EVD) and further complicate the situation. Previous studies suggested that the EV glycoprotein (GP) is the main determinant causing structural damage of endothelial cells that triggers the hemorrhagic diathesis, but molecular mechanisms underlying this phenomenon remains elusive. Using the informational spectrum method (ISM), a virtual spectroscopy method for analysis of the protein-protein interactions, the interaction of GP with endothelial extracellular matrix (ECM) was investigated. Presented results of this in silico study suggest that Elastin Microfibril Interface Located Proteins (EMILINs) are involved in interaction between GP and ECM. This finding could contribute to a better understanding of EV/endothelium interaction and its role in pathogenesis, prevention and therapy of EVD.

Aim: Chemokine CXC ligand 16 (CXCL16) has chemoattractive, adhesive and scavenging properties and may play a role in the formation of atherosclerotic lesions. However, studies of CXCL16 polymorphisms in patients with atherosclerosis are scarce. The missense polymorphisms I123T and A181V are potentially important factors in the regulation of presentation and shedding of the CXCL16 chemokine domain. The aim of this study was to analyze the association between I123T and A181V polymorphism haplotypes and the accumulation of carotid plaque as well as the effect of the haplotype on the CXCL16 mRNA expression in carotid plaques in patients with advanced atherosclerosis. Additionally, we performed a bioinformatic prediction analysis of the impact of CXCL16 protein sequence variation on CXCL16-CXCR6 interactions and analyzed the soluble CXCL16 plasma levels according to the CXCL16 haplotype. Methods: This study evaluated a total of 733 participants, including 283 controls and 450 patients with carotid atherosclerosis (CA) undergoing endarterectomy. Analyses of the polymorphisms and the gene expression were performed using real-time PCR. The soluble CXCL16 levels were measured with ELISA. Results: The missense allele haplotype, T123V181, was found to be significantly and independently associated with the occurrence of CA plaque (OR=1.27; 1.02-1.57, p=0.03). This haplotype was predicted to significantly change the CXCL16-CXCR6 interaction, compared to I123A181. Neither the CXCL16 mRNA expression in the human plaques nor the soluble CXCL16 plasma levels differed according to the haplotype. Conclusions: These results indicate that the CXCL16 T123V181 haplotype is a moderate genetic risk factor for the development of carotid plaque. Further functional and replication studies are needed to clarify the mechanisms by which this combination of alleles promotes advanced CA and validate its impact on disease progression.

The Informational Spectrum Method (ISM) is the tool for the in silico analysis of proteins which interprets protein sequence linear information using signal analyses methods. In this paper the ISM was employed to characterize the products of genetic variants of tumor suppressor gene p53 and its natural binding regulator protein Mdm2. Based on this we propose the criterion for identification of missense mutations that have impact on the p53-Mdm2 feedback loop. The efficiency of the proposed criterion was confirmed by the ISM analyses of p53 mutants reported in: (i) healthy individuals, (ii) germline mutations database and (iii) somatic mutations database.

A broad range of similarities between HIV-1 gp120 and human proteins-especially those participating in immune responses-highlight gp120 as a pleiotropic protein which can influence many important functions of the human immune system. The molecular mimicry that serves to the human immunodeficiency virus as potent destructive arms against immune system could be the weak point we are in search of over decades. Examples involving sequence and informational similarities of HIV-1 gp120 and immunerelated host cell proteins important for prevention and treatment of HIV infection are presented. .