Rapid protein structure determination using experimental NMR data

The knowledge of the tridimensional structure of a protein is essential to design drugs, to predict protein function and to study mechanism of protein function. 3D structure can be determined using two experimental techniques: X-Ray and NMR. However, these techniques have limitations: they are time consuming, manually intensive and sometime technically difficult. Due to these limitations, different approaches that combine the strength of computer and sparse experimental NMR data such as backbone chemical shifts, NOEs distances and residual dipolar couplings have been proposed for the determination of 3D protein structure. Among these experimental NMR data, backbone chemical shifts are the experimental NMR data that can be rapidly, easily and accurately measured. Thus different approaches that use sparse chemical shifts such as CS-Rosetta have been designed. Unfortunately, chemical shift-based 3D structure determination approaches are limited by the size and complexity of proteins (limited to molecular proteins that weight at most 15 kDa). This limitation can be understood by the fact that every folding trajectory during sampling is completely independent of every other. To overcome this limitation, additional sparse NMR data such as homology information, NOEs distances and/or RDCs are needed. CS-HM-Rosetta that combines incomplete chemical shifts and information derived from homologous structures have been developed in this purpose. CS-NOE-RDC-Rosetta, PHAISTOS have been also developed to guide structures determination using chemical shifts, NOEs distances and RDCs. Despite the fact that experimental data are invaluable for guiding sampling to the vicinity of the global energy minimum, for larger proteins, these data failed to guide sampling to the native minimum state. That is why, in a number of cases the improved sampling methodology makes a larger contribution than incorporation of additional experimental data. New sampling protocol named Resolution Adapted Structural RECombination (RASREC) has been therefore designed to overcome size limitation when using CS-Rosetta protocol. Unfortunately, most of these methods are not fully automated since they require manual assignment of experimental NMR data. Several automated methods such as AUDANA, CYANA 2015, AutoNOE-RASREC-CS-ROSETTA and J-UNIO that automatically assigned NOESY cross peaks, and chemical resonance assignment for J-UNIO have been developed. Up to now, they have not been compared together. In addition, they require expertise in computer science. Therefore, I am working toward getting fully automated approach, designed for non computer science and NMR experts that combines J-UNIO and AutoNOE-RASREC-CS-ROSETTA for rapid (4 weeks) protein 3D structure determination using unassigned backbone chemical shifts, NOESY spectra and RDCs spectra as input. During the 15th edition of the YBMRS meeting, I will compare CS-Rosetta, CS-RASREC-Rosetta, CS-HM-Rosetta and Homology modeling approches based on the quality of derived 3D structures for proteins for which chemical shifts are available

Comparison of rapid protein structure determination approaches driven by experimental NMR data

The knowledge of the tridimensional structure of a protein is essential to design drugs, to predict protein function and to study mechanism of protein function. 3D structure can be determined using two experimental techniques: X-Ray and NMR. However, these techniques have limitations: they are time consuming, manually intensive and sometime technically difficult. Due to these limitations, different approaches that combine the strength of computer and sparse experimental NMR data have been proposed to determine rapidly 3D protein structure. Thus different approaches that use sparse NMR experimental NMR data such as backbone chemical shifts, incomplete sets of NOEs distances and residual dipolar couplings of backbone have been designed. To assess whether if these automated methods can indeed produce structures that closely match those manually refined by experts using the same experimental data, Critical Assessment of Automated Structure Determination of Proteins from NMR Data [1] (CASD-NMR) was created. CASD-NMR concept closely resembles to Critical Assessment of Automated Structure Prediction [2] (CASP) that aim to assess performance of protein structure prediction methods from sequence. Accordingly to CASD-NMR 2013 [3] recommandations, we are working on the comparison of different automated protein structure determination methods driven by backbone chemical shifts and homology modeling in terms of the fitness of resulting 3D structures with experimental NMR data. For homology modeling, we have been using two approaches: I-TASSER [4] and MODELLER [5] that are best public CASP-certified protein structure prediction servers. Structure calculation guided by NMR backbone chemical shifts were done using different approaches: (i) ROSETTA method that is widely used by the scientific community. Different rosetta based methods exist that used only backbone chemical shifts as experimental data. Moreover, ROSETTA method is probably the most backbone chemical shifts based structure calculation method used and cited by the community. For our comparison, three ROSETTA-family methods have been used: CS-ROSETTA [6], CS-HM-ROSETTA [7] and RASREC CS-ROSETTA [8]. (ii) Cheshire which is the first backbone chemical shifts based method which performed as well as CS-HM-ROSETTA during CASD-NMR-2013, and (iii) CS23D which is a web server that performed 1000-10,000 times faster than competing methods (Cheshire [9] and CS-Rosetta). Each of these approaches were used to determine 3D structure of a benchmark of 50 proteins. These 50 proteins were randomly selected within proteins for which NMR backbone chemical shifts are available in the BMRB data bank excluding proteins that have been used during CASD or CASP. In addition, we will applied these methods on cold shock proteins for which we have our own experimental data

Protein structure modeling using backbone chemical shifts

The knowledge of the tridimensional structure of a protein is essential to study its interactions and understand its mode of action. The Purpose of our work is to quickly and easily determine the structure of proteins using the backbone chemical shifts. Backbone chemical shifts data are NMR parameters that can be rapidly, easily and accurately measured. This parameter is very sensitive to the conformation of amino acids and is used to deduct the secondary structure (TALOS, RCI,...). We therefore plan to use backbone chemical shifts as constraints on dihedral angles to quickly and easily determine protein structure. Several « de novo » methods like CS-Rosetta , CS23D et CHESHIRE have been recently developed in this purpose. We will use proteins of different sizes for which, the structure (X-ray or NMR structure) and chemical shifts backbone are available for testing the three softwares. Knowing that each of these softwares predicted a large number of low energy models on the one hand, and that the deployment and use of these tools constitutes obstacles for users who are not experts in computer science on the other hand, our goal will be to develop a platform that can easily compare these three methods based on quality of the structure produced

Quantification of 2-disulfide bonded isomers of apamin, a peptidic toxin, leads to the observation of a structural rearrangement

Apamin (APA1) interacts strongly with SK channels and blocks corresponding SK currents. Apamin is an octadecapeptide with four cysteine residues assembled in two disulfide bridges. In the context of our program related to the development of selective blockers of SK channels, we were interested in assessing the biological activity of two isomers of apamin (APA2 and APA3). These peptides present another disulfide bond connectivity. These peptides were produced by chemical synthesis. Before these biological evaluations, the quantification of peptide content in our samples was needed. Thus, solution of these peptides were analyzed by NMR techniques