Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction

Cinnamon extract induces tumor cell death through inhibition of NFκB and AP1

<p>Abstract</p> <p>Background</p> <p><it>Cinnamomum cassia </it>bark is the outer skin of an evergreen tall tree belonging to the family Lauraceae containing several active components such as essential oils (cinnamic aldehyde and cinnamyl aldehyde), tannin, mucus and carbohydrate. They have various biological functions including anti-oxidant, anti-microbial, anti-inflammation, anti-diabetic and anti-tumor activity. Previously, we have reported that anti-cancer effect of cinnamon extracts is associated with modulation of angiogenesis and effector function of CD8⁺T cells. In this study, we further identified that anti-tumor effect of cinnamon extracts is also link with enhanced pro-apoptotic activity by inhibiting the activities NFκB and AP1 in mouse melanoma model.</p> <p>Methods</p> <p>Water soluble cinnamon extract was obtained and quality of cinnamon extract was evaluated by HPLC (High Performance Liquid Chromatography) analysis. In this study, we tested anti-tumor activity and elucidated action mechanism of cinnamon extract using various types of tumor cell lines including lymphoma, melanoma, cervix cancer and colorectal cancer <it>in vitro </it>and <it>in vivo </it>mouse melanoma model.</p> <p>Results</p> <p>Cinnamon extract strongly inhibited tumor cell proliferation <it>in vitro </it>and induced active cell death of tumor cells by up-regulating pro-apoptotic molecules while inhibiting NFκB and AP1 activity and their target genes such as <it>Bcl-2</it>, <it>BcL-xL </it>and <it>survivin</it>. Oral administration of cinnamon extract in melanoma transplantation model significantly inhibited tumor growth with the same mechanism of action observed <it>in vitro</it>.</p> <p>Conclusion</p> <p>Our study suggests that anti-tumor effect of cinnamon extracts is directly linked with enhanced pro-apoptotic activity and inhibition of NFκB and AP1 activities and their target genes <it>in vitro </it>and <it>in vivo </it>mouse melanoma model. Hence, further elucidation of active components of cinnamon extract could lead to development of potent anti-tumor agent or complementary and alternative medicine for the treatment of diverse cancers.</p

Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network

<p>Abstract</p> <p>Background</p> <p>The identification of potentially relevant biomarkers and a deeper understanding of molecular mechanisms related to heart failure (HF) development can be enhanced by the implementation of biological network-based analyses. To support these efforts, here we report a global network of protein-protein interactions (PPIs) relevant to HF, which was characterized through integrative bioinformatic analyses of multiple sources of "omic" information.</p> <p>Results</p> <p>We found that the structural and functional architecture of this PPI network is highly modular. These network modules can be assigned to specialized processes, specific cellular regions and their functional roles tend to partially overlap. Our results suggest that HF biomarkers may be defined as key coordinators of intra- and inter-module communication. Putative biomarkers can, in general, be distinguished as "information traffic" mediators within this network. The top high traffic proteins are encoded by genes that are not highly differentially expressed across HF and non-HF patients. Nevertheless, we present evidence that the integration of expression patterns from high traffic genes may support accurate prediction of HF. We quantitatively demonstrate that intra- and inter-module functional activity may be controlled by a family of transcription factors known to be associated with the prevention of hypertrophy.</p> <p>Conclusion</p> <p>The systems-driven analysis reported here provides the basis for the identification of potentially novel biomarkers and understanding HF-related mechanisms in a more comprehensive and integrated way.</p

EcoTILLING in Capsicum species: searching for new virus resistances

<p>Abstract</p> <p>Background</p> <p>The EcoTILLING technique allows polymorphisms in target genes of natural populations to be quickly analysed or identified and facilitates the screening of genebank collections for desired traits. We have developed an EcoTILLING platform to exploit <it>Capsicum </it>genetic resources. A perfect example of the utility of this EcoTILLING platform is its application in searching for new virus-resistant alleles in <it>Capsicum </it>genus. Mutations in translation initiation factors (eIF4E, eIF(iso)4E, eIF4G and eIF(iso)4G) break the cycle of several RNA viruses without affecting the plant life cycle, which makes these genes potential targets to screen for resistant germplasm.</p> <p>Results</p> <p>We developed and assayed a cDNA-based EcoTILLING platform with 233 cultivated accessions of the genus <it>Capsicum</it>. High variability in the coding sequences of the <it>eIF4E </it>and <it>eIF(iso)4E </it>genes was detected using the cDNA platform. After sequencing, 36 nucleotide changes were detected in the CDS of <it>eIF4E </it>and 26 in <it>eIF(iso)4E</it>. A total of 21 <it>eIF4E </it>haplotypes and 15 <it>eIF(iso)4E </it>haplotypes were identified. To evaluate the functional relevance of this variability, 31 possible eIF4E/eIF(iso)4E combinations were tested against <it>Potato virus Y</it>. The results showed that five new <it>eIF4E </it>variants (<it>pvr2¹⁰</it>, <it>pvr2¹¹</it>, <it>pvr2¹²</it>, <it>pvr2¹³</it>and <it>pvr2¹⁴</it>) were related to PVY-resistance responses.</p> <p>Conclusions</p> <p>EcoTILLING was optimised in different <it>Capsicum </it>species to detect allelic variants of target genes. This work is the first to use cDNA instead of genomic DNA in EcoTILLING. This approach avoids intronic sequence problems and reduces the number of reactions. A high level of polymorphism has been identified for initiation factors, showing the high genetic variability present in our collection and its potential use for other traits, such as genes related to biotic or abiotic stresses, quality or production. Moreover, the new <it>eIF4E </it>and <it>eIF(iso)4E </it>alleles are an excellent collection for searching for new resistance against other RNA viruses.</p

Protein Networks as Logic Functions in Development and Cancer

Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference