Prediction of Phosphorylation Sites from Protein Sequences

Phosphorylation is amongst the most crucial and well-studied post-translational modifications. It is involved in multiple cellular processes which makes phosphorylation prediction vital for understanding protein functions. However, wet-lab techniques are labour and time intensive. Thus, computational tools are required for efficiency. This project aims to provide a novel way to predict phosphorylation sites from protein sequences by adding flexibility and Sezerman Grouping amino acid similarity measure to previous methods, as discovering new protein sequences happens at a greater rate than determining protein structures. The predictor – NOPAY - relies on Support Vector Machines (SVMs) for classification. The features include amino acid encoding, amino acid grouping, predicted secondary structure, predicted protein disorder, predicted protein flexibility, solvent accessibility, hydrophobicity and volume. As a result, we have managed to improve phosphorylation prediction accuracy for Homo sapiens by 3% and 6.1% for Mus musculus. Sensitivity at 99% specificity was also increased by 6% for Homo sapiens and for Mus musculus by 5% on independent test sets. In this study, we have managed to increase phosphorylation prediction accuracy for Homo sapiens and Mus musculus. When there is enough data, future versions of the software may also be able to predict other organisms.

Microfluidic platforms for the characterization of in meso membrane protein crystallization

Membrane proteins, which reside in the membranes of cells, play a critical role in many important biological processes including cellular signaling, immune response, and material and energy transduction. Because of their key role in maintaining the environment within cells and facilitating intercellular interactions, understanding the function of these proteins is of tremendous medical and biochemical significance. Indeed, the malfunction of membrane proteins has been linked to numerous diseases including diabetes, cirrhosis of the liver, cystic fibrosis, cancer, Alzheimer's disease, hypertension, epilepsy, cataracts, tubulopathy, leukodystrophy, Leigh syndrome, anemia, sensorineural deafness, and hypertrophic cardiomyopathy.1-3 However, the structure of many of these proteins and the changes in their structure that lead to disease-related malfunctions are not well understood. Additionally, at least 60% of the pharmaceuticals currently available are thought to target membrane proteins, despite the fact that their exact mode of operation is not known.4-6 Developing a detailed understanding of the function of a protein is achieved by coupling biochemical experiments with knowledge of the structure of the protein. Currently the most common method for obtaining three-dimensional structure information is X-ray crystallography. However, no a priori methods are currently available to predict crystallization conditions for a given protein.7-14 This limitation is currently overcome by screening a large number of possible combinations of precipitants, buffer, salt, and pH conditions to identify conditions that are conducive to crystal nucleation and growth.7,9,11,15-24 Unfortunately, these screening efforts are often limited by difficulties associated with quantity and purity of available protein samples. While the two most significant bottlenecks for protein structure determination in general are the (i) obtaining sufficient quantities of high quality protein samples and (ii) growing high quality protein crystals that are suitable for X-ray structure determination,7,20,21,23,25-47 membrane proteins present additional challenges. For crystallization it is necessary to extract the membrane proteins from the cellular membrane. However, this process often leads to denaturation. In fact, membrane proteins have proven to be so difficult to crystallize that of the more than 66,000 structures deposited in the Protein Data Bank,48 less than 1% are for membrane proteins, with even fewer present at high resolution (< 2Å)4,6,49 and only a handful are human membrane proteins.49 A variety of strategies including detergent solubilization50-53 and the use of artificial membrane-like environments have been developed to circumvent this challenge.43,53-55 In recent years, the use of a lipidic mesophase as a medium for crystallizing membrane proteins has been demonstrated to increase success for a wide range of membrane proteins, including human receptor proteins.54,56-62 This in meso method for membrane protein crystallization, however, is still by no means routine due to challenges related to sample preparation at sub-microliter volumes and to crystal harvesting and X-ray data collection. This dissertation presents various aspects of the development of a microfluidic platform to enable high throughput in meso membrane protein crystallization at a level beyond the capabilities of current technologies. Microfluidic platforms for protein crystallization and other lab-on-a-chip applications have been well demonstrated.9,63-66 These integrated chips provide fine control over transport phenomena and the ability to perform high throughput analyses via highly integrated fluid networks. However, the development of microfluidic platforms for in meso protein crystallization required the development of strategies to cope with extremely viscous and non-Newtonian fluids. A theoretical treatment of highly viscous fluids in microfluidic devices is presented in Chapter 3, followed by the application of these strategies for the development of a microfluidic mixer capable of preparing a mesophase sample for in meso crystallization at a scale of less than 20 nL in Chapter 4. This approach was validated with the successful on chip in meso crystallization of the membrane protein bacteriorhodopsin. In summary, this is the first report of a microfluidic platform capable of performing in meso crystallization on-chip, representing a 1000x reduction in the scale at which mesophase trials can be prepared. Once protein crystals have formed, they are typically harvested from the droplet they were grown in and mounted for crystallographic analysis. Despite the high throughput automation present in nearly all other aspects of protein structure determination, the harvesting and mounting of crystals is still largely a manual process. Furthermore, during mounting the fragile protein crystals can potentially be damaged, both from physical and environmental shock. To circumvent these challenges an X-ray transparent microfluidic device architecture was developed to couple the benefits of scale, integration, and precise fluid control with the ability to perform in situ X-ray analysis (Chapter 5). This approach was validated successfully by crystallization and subsequent on-chip analysis of the soluble proteins lysozyme, thaumatin, and ribonuclease A and will be extended to microfluidic platforms for in meso membrane protein crystallization. The ability to perform in situ X-ray analysis was shown to provide extremely high quality diffraction data, in part as a result of not being affected by damage due to physical handling of the crystals. As part of the work described in this thesis, a variety of data collection strategies for in situ data analysis were also tested, including merging of small slices of data from a large number of crystals grown on a single chip, to allow for diffraction analysis at biologically relevant temperatures. While such strategies have been applied previously,57,59,61,67 they are potentially challenging when applied via traditional methods due to the need to grow and then mount a large number of crystals with minimal crystal-to-crystal variability. The integrated nature of microfluidic platforms easily enables the generation of a large number of reproducible crystallization trials. This, coupled with in situ analysis capabilities has the potential of being able to acquire high resolution structural data of proteins at biologically relevant conditions for which only small crystals, or crystals which are adversely affected by standard cryocooling techniques, could be obtained (Chapters 5 and 6). While the main focus of protein crystallography is to obtain three-dimensional protein structures, the results of typical experiments provide only a static picture of the protein. The use of polychromatic or Laue X-ray diffraction methods enables the collection of time resolved structural information. These experiments are very sensitive to crystal quality, however, and often suffer from severe radiation damage due to the intense polychromatic X-ray beams. Here, as before, the ability to perform in situ X-ray analysis on many small protein crystals within a microfluidic crystallization platform has the potential to overcome these challenges. An automated method for collecting a "single-shot" of data from a large number of crystals was developed in collaboration with the BioCARS team at the Advanced Photon Source at Argonne National Laboratory (Chapter 6). The work described in this thesis shows that, even more so than for traditional structure determination efforts, the ability to grow and analyze a large number of high quality crystals is critical to enable time resolved structural studies of novel proteins. In addition to enabling X-ray crystallography experiments, the development of X-ray transparent microfluidic platforms also has tremendous potential to answer other scientific questions, such as unraveling the mechanism of in meso crystallization. For instance, the lipidic mesophases utilized during in meso membrane protein crystallization can be characterized by small angle X-ray diffraction analysis. Coupling in situ analysis with microfluidic platforms capable of preparing these difficult mesophase samples at very small volumes has tremendous potential to enable the high throughput analysis of these systems on a scale that is not reasonably achievable using conventional sample preparation strategies (Chapter 7). In collaboration with the LS-CAT team at the Advanced Photon Source, an experimental station for small angle X-ray analysis coupled with the high quality visualization capabilities needed to target specific microfluidic samples on a highly integrated chip is under development. Characterizing the phase behavior of these mesophase systems and the effects of various additives present in crystallization trials is key for developing an understanding of how in meso crystallization occurs. A long term goal of these studies is to enable the rational design of in meso crystallization experiments so as to avoid or limit the need for high throughput screening efforts. In summary, this thesis describes the development of microfluidic platforms for protein crystallization with in situ analysis capabilities. Coupling the ability to perform in situ analysis with the small scale, fine control, and the high throughput nature of microfluidic platforms has tremendous potential to enable a new generation of crystallographic studies and facilitate the structure determination of important biological targets. The development of platforms for in meso membrane protein crystallization is particularly significant because they enable the preparation of highly viscous mixtures at a previously unachievable scale. Work in these areas is ongoing and has tremendous potential to improve not only current the methods of protein crystallization and crystallography, but also to enhance our knowledge of the structure and function of proteins which could have a significant scientific and medical impact on society as a whole. The microfluidic technology described in this thesis has the potential to significantly advance our understanding of the structure and function of membrane proteins, thereby aiding the elucidation of human biology, the development of pharmaceuticals with fewer side effects for a wide range of diseases. References (1) Quick, M.; Javitch, J. A. P Natl Acad Sci USA 2007, 104, 3603. (2) Trubetskoy, V. S.; Burke, T. J. Am Lab 2005, 37, 19. (3) Pecina, P.; Houstkova, H.; Hansikova, H.; Zeman, J.; Houstek, J. Physiol Res 2004, 53, S213. (4) Arinaminpathy, Y.; Khurana, E.; Engelman, D. M.; Gerstein, M. B. Drug Discovery Today 2009, 14, 1130. (5) Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. Nat Rev Drug Discov 2006, 5, 993. (6) Dauter, Z.; Lamzin, V. S.; Wilson, K. S. Current Opinion in Structural Biology 1997, 7, 681. (7) Hansen, C.; Quake, S. R. Current Opinion in Structural Biology 2003, 13, 538. (8) Govada, L.; Carpenter, L.; da Fonseca, P. C. A.; Helliwell, J. R.; Rizkallah, P.; Flashman, E.; Chayen, N. E.; Redwood, C.; Squire, J. M. J Mol Biol 2008, 378, 387. (9) Hansen, C. L.; Skordalakes, E.; Berger, J. M.; Quake, S. R. P Natl Acad Sci USA 2002, 99, 16531. (10) Leng, J.; Salmon, J.-B. Lab Chip 2009, 9, 24. (11) Zheng, B.; Gerdts, C. J.; Ismagilov, R. F. Current Opinion in Structural Biology 2005, 15, 548. (12) Lorber, B.; Delucas, L. J.; Bishop, J. B. J Cryst Growth 1991, 110, 103. (13) Talreja, S.; Perry, S. L.; Guha, S.; Bhamidi, V.; Zukoski, C. F.; Kenis, P. J. A. The Journal of Physical Chemistry B 2010, 114, 4432. (14) Chayen, N. E. Current Opinion in Structural Biology 2004, 14, 577. (15) He, G. W.; Bhamidi, V.; Tan, R. B. H.; Kenis, P. J. A.; Zukoski, C. F. Cryst Growth Des 2006, 6, 1175. (16) Zheng, B.; Tice, J. D.; Roach, L. S.; Ismagilov, R. F. Angew Chem Int Edit 2004, 43, 2508. (17) Li, L.; Mustafi, D.; Fu, Q.; Tereshko, V.; Chen, D. L. L.; Tice, J. D.; Ismagilov, R. F. P Natl Acad Sci USA 2006, 103, 19243. (18) Song, H.; Chen, D. L.; Ismagilov, R. F. Angew Chem Int Edit 2006, 45, 7336. (19) van der Woerd, M.; Ferree, D.; Pusey, M. Journal of Structural Biology 2003, 142, 180. (20) Ng, J. D.; Gavira, J. A.; Garcia-Ruiz, J. M. Journal of Structural Biology 2003, 142, 218. (21) Talreja, S.; Kenis, P. J. A.; Zukoski, C. F. Langmuir 2007, 23, 4516. (22) Hansen, C. L.; Quake, S. R.; Berger, J. M. US, 2007. (23) Newman, J.; Fazio, V. J.; Lawson, B.; Peat, T. S. Cryst Growth Des 2010, 10, 2785. (24) Newman, J.; Xu, J.; Willis, M. C. Acta Crystallographica Section D 2007, 63, 826. (25) Collingsworth, P. D.; Bray, T. L.; Christopher, G. K. J Cryst Growth 2000, 219, 283. (26) Durbin, S. D.; Feher, G. Annu Rev Phys Chem 1996, 47, 171. (27) Talreja, S.; Kim, D. Y.; Mirarefi, A. Y.; Zukoski, C. F.; Kenis, P. J. A. J Appl Crystallogr 2005, 38, 988. (28) Yoshizaki, I.; Nakamura, H.; Sato, T.; Igarashi, N.; Komatsu, H.; Yoda, S. J Cryst Growth 2002, 237, 295. (29) Anderson, M. J.; Hansen, C. L.; Quake, S. R. P Natl Acad Sci USA 2006, 103, 16746. (30) Hansen, C. L.; Sommer, M. O. A.; Quake, S. R. P Natl Acad Sci USA 2004, 101, 14431. (31) Lounaci, M.; Rigolet, P.; Abraham, C.; Le Berre, M.; Chen, Y. Microelectron Eng 2007, 84, 1758. (32) Zheng, B.; Roach, L. S.; Ismagilov, R. F. J Am Chem Soc 2003, 125, 11170. (33) Zhou, X.; Lau, L.; Lam, W. W. L.; Au, S. W. N.; Zheng, B. Anal. Chem. 2007. (34) Cherezov, V.; Caffrey, M. J Appl Crystallogr 2003, 36, 1372. (35) Qutub, Y.; Reviakine, I.; Maxwell, C.; Navarro, J.; Landau, E. M.; Vekilov, P. G. J Mol Biol 2004, 343, 1243. (36) Rummel, G.; Hardmeyer, A.; Widmer, C.; Chiu, M. L.; Nollert, P.; Locher, K. P.; Pedruzzi, I.; Landau, E. M.; Rosenbusch, J. P. Journal of Structural Biology 1998, 121, 82. (37) Gavira, J. A.; Toh, D.; Lopez-Jaramillo, J.; Garcia-Ruiz, J. M.; Ng, J. D. Acta Crystallogr D 2002, 58, 1147. (38) Stevens, R. C. Current Opinion in Structural Biology 2000, 10, 558. (39) Baker, M. Nat Methods 2010, 7, 429. (40) McPherson, A. In Current Topics in Membranes, Volume 63; Volume 63 ed.; DeLucas, L., Ed.; Academic Press: 2009, p 5. (41) Gabrielsen, M.; Gardiner, A. T.; Fromme, P.; Cogdell, R. J. In Current Topics in Membranes, Volume 63; Volume 63 ed.; DeLucas, L., Ed.; Academic Press: 2009, p 127. (42) Page, R. In Methods in Molecular Biology: Structural Proteomics - High Throughput Methods; Kobe, B., Guss, M., Huber, T., Eds.; Humana Press: Totowa, NJ, 2008; Vol. 426, p 345. (43) Caffrey, M. Ann Rev Biophys 2009, 38, 29. (44) Doerr, A. Nat Methods 2006, 3, 244. (45) Brostromer, E.; Nan, J.; Li, L.-F.; Su, X.-D. Biochemical and Biophysical Research Communications 2009, 386, 634. (46) Li, G.; Chen, Q.; Li, J.; Hu, X.; Zhao, J. Anal Chem 2010, 82, 4362. (47) Jia, Y.; Liu, X.-Y. The Journal of Physical Chemistry B 2006, 110, 6949. (48) RCSB Protein Data Bank. http://www.rcsb.org/ (July 11, 2010). (49) Membrane Proteins of Known 3D Structure. http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html (July 11, 2010). (50) Michel, H. Trends Biochem Sci 1983, 8, 56. (51) Rosenbusch, J. P. Journal of Structural Biology 1990, 104, 134. (52) Garavito, R. M.; Picot, D. Methods 1990, 1, 57. (53) Kulkarni, C. V. 2010; Vol. 12, p 237. (54) Landau, E. M.; Rosenbusch, J. P. P Natl Acad Sci USA 1996, 93, 14532. (55) Pebay-Peyroula, E.; Rummel, G.; Rosenbusch, J. P.; Landau, E. M. Science 1997, 277, 1676. (56) Cherezov, V.; Liu, W.; Derrick, J. P.; Luan, B.; Aksimentiev, A.; Katritch, V.; Caffrey, M. Proteins: Structure, Function, and Bioinformatics 2008, 71, 24. (57) Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen, S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Kuhn, P.; Weis, W. I.; Kobilka, B. K.; Stevens, R. C. Science 2007, 318, 1258. (58) Cherezov, V.; Yamashita, E.; Liu, W.; Zhalnina, M.; Cramer, W. A.; Caffrey, M. J Mol Biol 2006, 364, 716. (59) Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.; Chien, E. Y. T.; Lane, J. R.; IJzerman, A. P.; Stevens, R. C. Science 2008, 322, 1211. (60) Rosenbaum, D. M.; Cherezov, V.; Hanson, M. A.; Rasmussen, S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Yao, X. J.; Weis, W. I.; Stevens, R. C.; Kobilka, B. K. Science 2007, 318, 1266. (61) Wacker, D.; Fenalti, G.; Brown, M. A.; Katritch, V.; Abagyan, R.; Cherezov, V.; Stevens, R. C. J Am Chem Soc 2010, 132, 11443. (62) Höfer, N.; Aragão, D.; Caffrey, M. Biophys J 2010, 99, L23. (63) Li, L.; Ismagilov, R. F. Ann Rev Biophys 2010. (64) Pal, R.; Yang, M.; Lin, R.; Johnson, B. N.; Srivastava, N.; Razzacki, S. Z.; Chomistek, K. J.; Heldsinger, D. C.; Haque, R. M.; Ugaz, V. M.; Thwar, P. K.; Chen, Z.; Alfano, K.; Yim, M. B.; Krishnan, M.; Fuller, A. O.; Larson, R. G.; Burke, D. T.; Burns, M. A. Lab Chip 2005, 5, 1024. (65) Jayashree, R. S.; Gancs, L.; Choban, E. R.; Primak, A.; Natarajan, D.; Markoski, L. J.; Kenis, P. J. A. J Am Chem Soc 2005, 127, 16758. (66) Wootton, R. C. R.; deMello, A. J. Chem Commun 2004, 266. (67) McPherson, A. J Appl Crystallogr 2000, 33, 397.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Three-dimensional solution structure of alpha-conotoxin MII by NMR spectroscopy: Effects of solution environment on helicity

alpha-Conotoxin MII, a 16-residue polypeptide from the venom of the piscivorous cone snail Conus magus, is a potent and highly specific blocker of mammalian neuronal nicotinic acetylcholine receptors composed of alpha 3 beta 2 subunits. The role of this receptor type in the modulation of neurotransmitter release and its relevance to the problems of addiction and psychosis emphasize the importance of a structural understanding of the mode of interaction of MII with the alpha 3 beta 2 interface. Here we describe the three-dimensional solution structure of MIT determined using 2D H-1 NMR spectroscopy. Structural restraints consisting of 376 interproton distances inferred from NOEs and 12 dihedral restraints derived from spin-spin coupling constants were used as input for simulated annealing calculations and energy minimization in the program X-PLOR. The final set of 20 structures is exceptionally well-defined with mean pairwise rms differences over the whole molecule of 0.07 Angstrom for the backbone atoms and 0.34 Angstrom for all heavy atoms. MII adopts a compact structure incorporating a central segment of alpha-helix and beta-turns at the N- and C-termini. The molecule is stabilized by two disulfide bonds, which provide cross-links between the N-terminus and both the middle and C-terminus of the structure. The susceptibility of the structure to conformational change was examined using several different solvent conditions. While the global fold of MII remains the same, the structure is stabilized in a more hydrophobic environment provided by the addition of acetonitrile or trifluoroethanol to the aqueous solution. The distribution of amino acid side chains in MII creates distinct hydrophobic and polar patches on its surface that may be important for the specific interaction with the alpha 3 beta 2 neuronal nAChR. A comparison of the structure of MII with other neuronal-specific alpha-conotoxins provides insights into their mode of interaction with these receptors.

Role of the 6-20 disulfide bridge in the structure and activity of epidermal growth factor

Two synthetic analogues of murine epidermal. growth factor, [Abu6, 20] mEGF4-48 (where Abu denotes amino-butyric acid) and [G1, M3, K21, H40] mEGF1-48, have been investigated by NMR spectroscopy. [Abu6, 20] mEGF4-48 was designed to determine the contribution of the 6-20 disulfide bridge to the structure and function of mEGF The overall structure of this analogue was similar to that of native mEGF, indicating that the loss of the 6-20 disulfide bridge did not affect the global fold of the molecule. Significant structural differences were observed near the N-terminus, however, with the direction of the polypeptide chain between residues four and nine being altered such that these residues were now located on the opposite face of the main beta-sheet from their position in native mEGF Thermal denaturation experiments also showed that the structure of [Abu6, 20] mEGF4-48 was less stable than that of mEGF. Removal of this disulfide bridge resulted in a significant loss of both mitogenic activity in Balb/c 3T3 cells and receptor binding on A431 cells compared with native mEGF and mEGF4-48, implying that the structural changes in [Abu6, 20] mEGF4-48, although limited to the N-terminus, were sufficient to interfere with receptor binding. The loss of binding affinity probably arose mainly from steric interactions of the dislocated N-terminal region with part of the receptor binding surface of EGF [G1, M3, K21, H40] mEGF1-48 was also synthesized in order to compare the synthetic polypeptide with the corresponding product of recombinant expression. Its mitogenic activity in Balb/c 3T3 cells was similar to that of native mEGF and analysis of its H-1 chemical shifts suggested that its structure was also very similar to native.

Conotoxin TVIIA, a novel peptide from the venom of Conus tulipa - 2. Three-dimensional solution structure

The three-dimensional solution structure of conotoxin TVIIA, a 30-residue polypeptide from the venom of the piscivorous cone snail Conus tulipa, has been determined using 2D H-1 NMR spectroscopy. TVIIA contains six cysteine residues which form a 'four-loop' structural framework common to many peptides from Conus venoms including the omega-, delta-, kappa-, and mu O-conotoxins. However, TVIIA does not belong to these well-characterized pharmacological classes of conotoxins, but displays high sequence identity with conotoxin GS, a muscle sodium channel blocker from Conus geographus. Structure calculations were based on 562 interproton distance restraints inferred from NOE data, together with 18 backbone and nine side-chain torsion angle restraints derived from spin-spin coupling constants. The final family of 20 structures had mean pairwise rms differences over residues 2-27 of 0.18 +/- 0.05 Angstrom for the backbone atoms and 1.39 +/- 0.33 Angstrom for all heavy atoms. The structure consists of a triple-stranded, antiparallel beta sheet with +2x, -1 topology (residues 7-9, 16-20 and 23-27) and several beta turns. The core of the molecule is formed by three disulfide bonds which form a cystine knot motif common to many toxic and inhibitory polypeptides. The global fold, molecular shape and distribution of amino-acid sidechains in TVIIA is similar to that previously reported for conotoxin GS, and comparison with other four-loop conotoxin structures provides further indication that TVIIA and GS represent a new and distinct subgroup of this structural family. The structure of TVIIA determined in this study provides the basis for determining a structure-activity relationship for these molecules and their interaction with target receptors.

Solution structure of the sodium channel antagonist conotoxin GS: A new molecular caliper for probing sodium channel geometry

Background: The venoms of Conus snails contain small, disulfide-rich inhibitors of voltage-dependent sodium channels. Conotoxin GS is a 34-residue polypeptide isolated from Conus geographus that interacts with the extracellular entrance of skeletal muscle sodium channels to prevent sodium ion conduction. Although conotoxin GS binds competitively with mu conotoxin GIIIA to the sodium channel surface, the two toxin types have little sequence identity with one another, and conotoxin GS has a four-loop structural framework rather than the characteristic three-loop mu-conotoxin framework. The structural study of conotoxin GS will form the basis for establishing a structure-activity relationship and understanding its interaction with the pore region of sodium channels. Results: The three-dimensional structure of conotoxin GS was determined using two-dimensional NMR spectroscopy. The protein exhibits a compact fold incorporating a beta hairpin and several turns. An unusual feature of conotoxin GS is the exceptionally high proportion (100%) of cis-imide bond geometry for the three proline or hydroxyproline residues. The structure of conotoxin GS bears little resemblance to the three-loop mu conotoxins, consistent with the low sequence identity between the two toxin types and their different structural framework. However, the tertiary structure and cystine-knot motif formed by the three disulfide bonds is similar to that present in several other polypeptide ion channel inhibitors. Conclusions: This is the first three-dimensional structure of a 'four-loop' sodium channel inhibitor, and it represents a valuable new structural probe for the pore region of voltage-dependent sodium channels. The distribution of amino acid sidechains in the structure creates several polar and charged patches, and comparison with the mu conotoxins provides a basis for determining the binding surface of the conotoxin GS polypeptide.

The structure of a novel insecticidal neurotoxin, omega-atracotoxin-HV1, from the venom of an Australian funnel web spider

A family of potent insecticidal toxins has recently been isolated from the venom of Australian funnel web spiders. Among these is the 37-residue peptide omega-atracotoxin-HV1 (omega-ACTX-HV1) from Hadronyche versuta. We have chemically synthesized and folded omega-ACTX-HV1, shown that it is neurotoxic, ascertained its disulphide bonding pattern, and determined its three-dimensional solution structure using NMR spectroscopy. The structure consists of a solvent-accessible beta-hairpin protruding from a disulphide-bonded globular core comprising four beta-turns. The three intramolecular disulphide bonds form a cystine knot motif similar to that seen in several other neurotoxic peptides. Despite limited sequence identity, omega-ACTX-HV1 displays significant structural homology with the omega-agatoxins and omega-conotoxins, both of which are vertebrate calcium channel antagonists; however, in contrast with these toxins, we show that omega-ACTX-HV1 inhibits insect, but not mammalian, voltage-gated calcium channel currents.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.

Assessment of the ability to model proteins with leucine-rich repeats in light of the latest structural information

The three-dimensional structures of leucine-rich repeat (LRR) -containing proteins from five different families were previously predicted based on the crystal structure of the ribonuclease inhibitor. using an approach that combined homology-based modeling, structure-based sequence alignment of LRRs, and several rational assumptions. The structural models have been produced based on very limited sequence similarity, which, in general. cannot yield trustworthy predictions. Recently, the protein structures from three of these five families have been determined. In this report we estimate the quality of the modeling approach by comparing the models with the experimentally determined structures. The comparison suggests that the general architecture, curvature, interior/exterior orientations of side chains. and backbone conformation of the LRR structures can be predicted correctly. On the other hand. the analysis revealed that, in some cases. it is difficult to predict correctly the twist of the overall super-helical structure. Taking into consideration the conclusions from these comparisons, we identified a new family of bacterial LRR proteins and present its structural model. The reliability of the LRR protein modeling suggests that it would be informative to apply similar modeling approaches to other classes of solenoid proteins.

The periodic table: contest and exhibition

The systemization and organization of ideas and concepts is an integral part of science. In chemistry, the organization of the periodic table of the chemical elements in the 1860s was one of the greatest scientific breakthroughs ever made and in fact during the 20th century it became a universally recognized scientific icon (1). The periodic table is the fundamental classificatory scheme of the elements and summarizes the realm of chemistry (2). Simply knowing the position of an element in the periodic table tells us about its properties and is usually enough to predict how the element will behave in a wide variety of different situations or reactions (1). Based on this potential mine of information, it is possible to make reliable predictions of the properties of the compounds that each element forms. Nowadays, the concept of the periodic table is starting to interact with other sciences and reports of periodic tables of amino acids (3), genetic codes (4), protein structures (5), and biology (6) can be found in the specialized literature. Symbiosis between science and art, for example, “The Periodic Table of The Elephants” (7), can also be seen. To appeal to a better understanding of the periodic table, the Instituto Superior de Engenharia do Instituto Politécnico do Porto and the Centro de Química da Universidade do Porto promoted a contest and exhibit with the goal of stimulating a wide and heterogeneous audience, ranging from young children and their parents to graduate students from several disciplines, to explore the nature of this icon. Imaginative educational activities such as contests (8–10), games (11, 12), and puzzles (13–15) provided a way to communicate with the general public with the goal of attracting students to science. This also constituted an interesting, informative, and entertaining alternative to non-interactive lectures. Simultaneously, artistic creativity was combined with scientific knowledge.

Production optimization of rotavirus-like particles: a system biology approach

Dissertation presented to obtain a Ph.D. degree in Engineering and Technology Sciences, Systems Biology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Artificial antibodies for troponin T by its imprinting on the surface of multiwalled carbon nanotubes: Its use as sensory surfaces

A novel artificial antibody for troponin T (TnT) was synthesized by molecular imprint (MI) on the surface of multiwalled carbon nanotubes (MWCNT). This was done by attaching TnT to the MWCNT surface, and filling the vacant spaces by polymerizing under mild conditions acrylamide (monomer) in N,N′-methylenebisacrylamide (cross-linker) and ammonium persulphate (initiator). After removing the template, the obtained biomaterial was able to rebind TnT and discriminate it among other interfering species. Stereochemical recognition of TnT was confirmed by the non-rebinding ability displayed by non-imprinted (NI) materials, obtained by imprinting without a template. SEM and FTIR analysis confirmed the surface modification of the MWCNT. The ability of this biomaterial to rebind TnT was confirmed by including it as electroactive compound in a PVC/plasticizer mixture coating a wire of silver, gold or titanium. Anionic slopes of 50 mV decade−1 were obtained for the gold wire coated with MI-based membranes dipped in HEPES buffer of pH 7. The limit of detection was 0.16 μg mL−1. Neither the NI-MWCNT nor the MWCNT showed the ability to recognize the template. Good selectivity was observed against creatinine, sucrose, fructose, myoglobin, sodium glutamate, thiamine and urea. The sensor was tested successfully on serum samples. It is expected that this work opens new horizons on the design of new artificial antibodies for complex protein structures.

Conservation of complex knotting and slipknotting patterns in proteins.

While analyzing all available protein structures for the presence of knots and slipknots, we detected a strict conservation of complex knotting patterns within and between several protein families despite their large sequence divergence. Because protein folding pathways leading to knotted native protein structures are slower and less efficient than those leading to unknotted proteins with similar size and sequence, the strict conservation of the knotting patterns indicates an important physiological role of knots and slipknots in these proteins. Although little is known about the functional role of knots, recent studies have demonstrated a protein-stabilizing ability of knots and slipknots. Some of the conserved knotting patterns occur in proteins forming transmembrane channels where the slipknot loop seems to strap together the transmembrane helices forming the channel.