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Monday, February 11, 2013

Polymerase chain displacement reaction

Polymerase chain displacement reaction

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Thursday, January 31, 2013

Proteins Never Act Alone

Biology has no wallflowers. Inside cells, it’s a party: DNA dances with RNA, enzymes hug small molecules, and proteins interact with proteins. Biologists, however, tend to study proteins one at a time. For example, Barry Honig’s group at Columbia University estimates that, as of 2010, individual structures were solved for about 10 percent of all yeast proteins. Meanwhile, structures were available for only 0.5 percent of protein complexes.

Target T32 of CAPRI was predicting the structure of the complex between the protease savinase (1SVN) and the bifunctional inhibitor BASI (1AVA_C). The figure shows the highest probability model submitted by the Vajda group. Source: Sandor Vajda


Barry Honig and collaborators introduced an online tool—PrePPI—that predicts PPIs with less detail but a broader scope. Source: Barry Honig

Sandor Vajda, who self-identifies as a “classic protein docking guy” and is director of Boston University’s Biomolecular Engineering Research Center, says PrePPI adds to earlier bioinformatics efforts on PPIs. Source: Sandor Vajda

David Koes is a computational biologist at the University of Pittsburgh who works on drugs that target PPIs. Source: David Koes
Protein-protein interactions, or PPIs, literally keep us together. PPIs link cells in multicellular organisms, relay signal transduction information, and connect enzymes with inhibitors or activators. Experimental research on PPIs pulls complexes from cells and identifies the interacting proteins; computational research builds models of protein associations. Much computer modeling of PPIs uses known protein structures for atomic-level studies. In a paper published recently in Nature (1), Honig and collaborators from Columbia University, the Salk Institute for Biological Studies, and Tongji University in China, introduced an online tool—PrePPI—that predicts PPIs with less detail but a broader scope. “Structural BLAST” is how Honig describes the PrePPI approach. The BLAST (basic local alignment search tool) analogy resonates with anyone who uses the public, online search tool to get nucleotide or amino acid sequence alignments. PrePPI users plug in protein queries and get search results for predicted PPIs. PrePPI (pronounced preppy) searches for local geometric relationships between proteins based on both experimentally determined and modeled protein structures. “We look broadly for any relationship between two structures because we can get information from these relationships and the models on which they are based, if even they are imperfect,” says Honig. PrePPI is meant to be used like BLAST: to kickstart hypotheses, generate research ideas, and plan experiments.
How PrePPI Works
PrePPI predicts interactions between a pair of query proteins using known structures for the proteins or—and this is the key—for their homologs. Although other PPI researchers have used homology modeling, Honig’s group is the first to apply the strategy on such a large scale. Any complex with a structure that involves the proteins, their homologs, or structural neighbors becomes the template for how the query proteins might interact.
Then PrePPI rates the template complex. It scores the structural fit between the query proteins and their stand-ins and asks whether the query proteins have the right amino acids in the right place to make the bonds of the interaction. PrePPI also checks if the query proteins have properties that make the interaction likely: similar expression profiles, functions, cellular location, and evolutionary patterns. Although each datapoint might be weak on its own, enough evidence adds up to a high-confidence interaction prediction.
Overall, PrePPI is too blunt of an instrument for Sandor Vajda’s research on the structure of protein complexes. But Vajda, who self-identifies as a “classic protein docking guy” and is director of Boston University’s Biomolecular Engineering Research Center, says PrePPI adds to earlier bioinformatics efforts on PPIs. Although the protein interaction field has heaps of experimental data from mass spectrometry experiments and yeast two-hybrid screens, the reliability of these data can be low. So researchers need additional information to weed out false leads. “What Honig brings to the table,” says Vajda, “is adding structure to computational PPI predictions and a good scoring scheme that filters out many false positives.”
In fact, PrePPI performed at least as well as high-throughput experimental methods in validation tests published in the Nature paper (1). PrePPI also showed that data on a distantly related protein can predict a protein’s structure. For example, PrePPI used a template of interacting proteins in the ubiquitin pathway to correctly predict an association between two kinases that were unrelated to the ubiquitin-pathway models.
You can see if your favorite proteins might interact at http://bhapp.c2b2.columbia.edu/PrePPI/. So far, PrePPI predicts yeast and human PPIs, with proteins from other organisms to come. Honig says, “Put in a protein name, and we’ll give you our best shot.”
From PPIs to New Drugs
Future versions of PrePPI might predict associations between proteins and membranes, nucleotides, or—crucial to drug development—small molecules. This is the realm of David Koes, a computational biologist at the University of Pittsburgh who works on drugs that target PPIs. An example is the PPI between the tumor suppressor protein p53 and its negative regulator Mdm2. Interrupting this interaction could combat cancer by freeing p53, and candidate drugs are currently in early trials. The challenge of PPI drug discovery, says Koes, is that “proteins in PPIs evolved to bind large molecules—other proteins—not small molecules like drugs.”
That’s why Koes and colleagues study PPI interfaces: the size, charges, and arrangements of interacting amino acids. The goal is to find small molecules that mimic the PPI interface and might wedge between proteins, blocking interactions “like a foot in a door,” says Koes. To find molecules that target a PPI, he has a package of tools. To begin with, Koes offers PocketQuery, a free online tool that, unlike PrePPI, requires known protein structures. Then based on information from PocketQuery about the PPI interface, additional search tools such as AnchorQuery and ZINCPharmer help him find small molecules that might modulate the PPI (2-3).
An eternal challenge for computer modelers is the complexity of biology, which stubbornly refuses to be reduced to a simple algorithm. However, Koes says, the PrePPI method of combining as much information as possible to create a meaningful model is “the heart of systems biology.” This is the future of personalized medicine—using large amounts of data about a single patient to make individualized predictions about disease, diagnosis, and treatment.
In the end, Koes, Honig, and other modelers also make up for the inevitable simplifications of computer modeling by adding the human element. “We generate interactive programs that incorporate the expert knowledge of the person doing the search,” Koes says. Fast, free, flexible modeling tools like PrePPI, PocketQuery, and others respect the user’s intelligence. The tools do not provide the ultimate answers. Instead, users get information that is a starting point for hypotheses, experiments, and further queries.
And Just For Fun…
More free tools and even contests for exploring PPIs abound. Sandor Vajda was an initiator of the Critical Assessment of Prediction of Interactions (CAPRI), which he describes as “an international experiment, but also a competition.” CAPRI is an ongoing contest, just for fun and glory, with the same give-it-a-try spirit of PrePPI.
Hosted by the European Bioinformatics Institute, CAPRI gets solved structures of protein-protein complexes that have not yet been published from researchers who are willing to wait a few weeks before releasing the structure. CAPRI contestants get information about the proteins in the complex and have a month to submit interaction models. A CAPRI committee determines a “winner” by comparing submitted models to the known, but previously secret, structure. Boston University’s free, online PPI modeling program ClusPro (cluspro.bu.edu/) consistently performs well in CAPRI (4).
All things considered, ClusPro, PrePPI, PocketQuery, and other PPI tools are giving researchers lots to do, on their desktops and at the bench. Vajda says that predicted interactions from PrePPI are a good starting point for exploring PPIs experimentally, validating predicted interactions and determining their physiological importance.
Barry Honig says that PrePPI will contribute to and benefit from experimental research: “Computational modeling starts with experimental data but drives wet lab work, too. It goes both ways.”
References
1. Zhang, Q. C. C., D. Petrey, L. Deng, L. Qiang, Y. Shi, C. A. A. Thu, B. Bisikirska, C. Lefebvre, D. Accili, T. Hunter, T. Maniatis, A. Califano, and B. Honig. 2012. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 490(7421):556-560.
2. Koes D, Khoury K, Huang Y, Wang W, Bista M, Popowicz GM, Wolf S, Holak TA, Dömling A, Camacho CJ. 2012. Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists. PLoS ONE 7(3):e32839.
3. Koes, DR, Camacho CJ. 2012. ZINCPharmer: pharmacophore search of the ZINC database. Nucl Acid Res. 40:W409-W414.
4. Kozakov, D., D. R. Hall, D. Beglov, R. Brenke, S. R. Comeau, Y. Shen, K. Li, J. Zheng, P. Vakili, I. C. C. h. Paschalidis, and S. Vajda. 2010. Achieving reliability and high accuracy in automated protein docking: ClusPro, PIPER, SDU, and stability analysis in CAPRI rounds 13-19. Proteins 78(15):3124-3130.

Thursday, November 15, 2012

Repair of Damaged DNA for Forensic Analysis Ebook

Tuesday, August 28, 2012

PhD in Molecular Medicine / Computational Biology In Italy

PhD in Molecular Medicine / Computational Biology

  • DURATION: 4 years
  • AWARDING BODIES: University of Milan
  • LOCATION: Campus IFOM-IEO Milan
  • COORDINATOR OF THE PROGRAM: Pier Giuseppe Pelicci
  • SCIENTIFIC COORDINATORS: F. CiccarelliA. Ciliberto
  • DESCRIPTION:
    This course is intended to students having a specific interest for quantitative aspects of biology, such as genomics, systems biology, and modeling of molecular networks.
  • RESEARCH TOPICS:
    Systems Biology, Analysis of Next-Gen Sequencing Data, Network Biology, Evolutionary Genomics, Bioinformatics, Cancer Genomics
  • SUPERVISION: 
    Each PhD student will be tutored by three advisors:
    The Supervisor is the scientific head of the host laboratory, who will guide the work of the student during his/her PhD period.
    The Co-supervisor will contribute in tutoring the student with particular emphasis to the technical and quantitative aspects of the project.
    The External Co-supervisor is a foreign expert, who will provide advice at critical stages of the project and meet the student at least once during his/her PhD period.
  • TRAINING OFFERED:
    Courses: Courses cover the first three years and address basic and advanced topics in cancer biology from a computational perspective.
    Seminars: Students are exposed to a wide selection of seminars from international speakers.
  • FELLOWSHIP:
    Tuition and salaries for students are fully covered for the entire PhD period. Refer to "financial matters" for more info.
  • WORKING LANGUAGE: English
  • HOW TO APPLY:
    Applications are accepted exclusively online once a year in the period July-September. Go to application process.
  • STARTING DATE:: November 1st of each year

Thursday, August 2, 2012

Biochemistry - Donald Voet, Judith G. Voet - 4th Edition



Biochemistry is a field of enormous fascination and utility, arising, no doubt, from our own self-interest. Human welfare, particularly its medical and nutritional aspects, has been vastly improved by our rapidly growing understanding of biochemistry. Indeed, scarcely a day passes without the report of a biomedical discovery that benefits a significant portion of humanity. Further advances in this rapidly expanding field of knowledge will no doubt lead to even more spectacular gains in our ability to understand nature and to control our destinies. It is therefore essential that individuals embarking on a career in biomedical sciences be well versed in biochemistry.

Friday, July 13, 2012

7 Tips for Optimal DNA Transfection

    DNA transfection is a main tool for current genetics as well as cell and molecular biology studies. Understanding the underlying mechanisms and the different parameters affecting transfection is crucial for optimal, reproducible, and trustable results. Following these seven tips will ensure reliable results.
    1. Adapt the transfection conditions to your experiment. Transfection conditions should be optimized according to the cells, the size of the plasmid, the number of plasmids, the expression pattern of the gene of interest, the culture vessel, and the transfection reagent. Reoptimize the conditions for each new cell line.
    2. Transfect healthy cells. Passage cells at least twice after thawing to allow recovery before transfection, and use cells at low passage number (< 20 passages). Discard overconfluent cells. Regularly check for mycoplasma contaminations. Seed cells the day before transfection accordingly to the confluency recommended by the transfection reagent provider.
    3. Follow the transfection reagent protocol. Some reagents are inhibited by serum or antibiotics, while others may be used in serum- and antibiotic-containing medium, hence reducing the risks of toxicity and the number of steps in the protocol. Check the recommended cell confluency, as well as the recommended DNA amount and reagent volume.
    4. Work with high-quality DNA preparation. Check for RNA contamination by agarose gel electrophoresis and ethidium bromide staining. Measure UV absorbance at 280 nm. OD260/280 ratio should reach at least 1.8. Resuspend the plasmid in deionized water or TE buffer at a concentration of ca. 1 µg/µl. Aliquot the plasmid preparation and store at -20°C to avoid freeze/thaw cycles.
    5. Check serum quality. Some serum lots may inhibit drastically transfection efficiency, hence resulting in lower silencing efficiency. Check transfection efficiency of different serum lots before purchasing a new batch of serum. Also ensure that the medium used allows for efficient transfection, as some media coumpound may decrease transfection efficiency.
    6. Minimize cytotoxicity by using low DNA amount and low transfection reagent volume. Check that the target gene does not affect cell viability. Analyze transfection at an earlier time point (24 h after transfection instead of 48 h for instance).
    7. Use appropriate controls. Use a reporter gene to set up and optimize transfection conditions, as those may vary depending on the cells to transfect. Various reporter systems are commercially available: Renilla Luciferase and GFP (Green Fluorescent Protein) are the most commonly used.