NMR refinement of under-determined loop regions of the E200K variant of the human prion protein using database-derived distance constraints

Computational studies and research conducted in order to facilitate the understanding of the conversion of the normal cellular prion (PrP[Superscript c]) to the scrapie prion (PrP[Superscript Sc]) in prion diseases, are usually based on the structures determined by NMR. This is mainly attributed to the difficulties involved in crystallizing the prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrP[Superscript c] (residues 167-171), which is the potential binding site for the hypothized Protein X, is under-determined in most mammalian species. In this research, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop and some other secondary structural elements of the E200K variant of human PrP[Superscript c] can be refined into more generally realistic and acceptable structures within an ensemble, with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement with database derived distances constraints and the percentage of residues in the most favorable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. In NMR structures, a model with 90% or more residues lying in the most favorable regions of the Ramachandran plot, is considered a good quality model. Our results not only provide a significantly improved model of structures of the Human prion protein, that would hence facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement

Refinement of Under-Determined Loops of Human Prion Protein by Database-Derived Distance Constraints

Computational simulations of the conversion from the normal cellular prion (PrPc) to the scrapie prion (PrPSc) are usually based on the structures determined by NMR because of the difficulties in crystallizing prion protein. Due to insufficient experimental restraints, a biologically critical loop region in PrPc (residues 167–171), which is a potential binding site for Protein X, is under-determined in most mammalian species. Here, we show that by adding information about distance constraints derived from a database of high-resolution protein structures, this under-determined loop as well as other secondary structural elements of the E200K variant of human prion protein (hPrPc), a disease-related isoform, can be refined into more realistic structures in the structural ensemble with improved quality and increased accuracy. In particular, the ensemble becomes more compact after the refinement and the percentage of residues in the most favourable region of the Ramachandran diagram is increased to about 90% in the refined structures from the 80 to 85% range in the previously reported structures. Our results not only provide significantly improved structures of the prion protein and hence would facilitate insights into its conversion in the spongiform encephalopathies, but also demonstrate the strong potential for using databases of known protein structures for structure determination and refinement

Protein Structure Determination Using Chemical Shifts

In this PhD thesis, a novel method to determine protein structures using chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr

Distance-based protein structure modeling

Protein structure modeling can be studied based on the knowledge of interactions or distances between pairs of atoms, which is so-called distance-based protein structure modeling and this field includes problems of structure determination and refinement as well as analysis of protein dynamics. The distances for certain pairs of atoms in a protein can often be obtained based on our knowledge on various types of bond-lengths and bond-angles or from physical experiments such as nuclear magnetic resonance (NMR). The coordinates of the atoms and hence the protein structure can then be determined by using the known distances. However, it requires the solution of a mathematical problem called the distance geometry problem, which has been proven to be computationally intractable in general. On the other hand, due to insufficient distance data such as nuclear overhauser effect (NOE) data in NMR, the protein structures determined by conventional techniques usually are not as accurate as desired. Therefore, the uses of such protein structures in important applications including homology modeling and rational drug design have been severely limited. In this work, we have developed several efficient algorithms including theories for the solution of the distance geometry problem using a geometric build-up algorithm. We also introduced a knowledge-based method for protein structure refinement, in which we constructed a dedicated structural database for protein inter-atomic distance distributions and derived so-called mean force potentials to refine NMR-determined protein structures. We have participated in CASPR competition regarding comparative models and reported some substantial improvement using mean force potentials. Finally, an efficient and simple method called Local-DME calculations has been developed to study protein dynamics of NMR ensembles specifically

PIDD: database for Protein Inter-atomic Distance Distributions

Protein Inter-atomic Distance Distributions (PIDD) is a dedicated database and structural bio-informatics system for distance based protein modeling. The database is developed to host and analyze the statistical data for protein inter-atomic distances based on their distributions in databases of known protein structures such as in the Protein Data Bank (PDB). PIDD is capable of generating, caching, and displaying the statistical distributions of the distances of various types and ranges. The collected information can be used to extract geometric restraints or mean-force potentials for protein structure determination including nuclear magnetic resonance structure determination and comparative model refinement. PIDD is supported with a friendly designed web interface so that users can easily specify the distance types and ranges, and retrieve, visualize or download the distributions of the distances as they desire

Distance Matrix-Based Approach to Protein Structure Prediction

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [rij2] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance rij is greater or less than a cutoff value rcutoff .We have performed spectral decomposition of the distance matrices D=∑λkVkVTk , in terms of eigenvalues λk and the corresponding eigenvectors vk and found that it contains at most 5 nonzero terms. A dominant eigenvector is proportional to r2 - the square distance of points from the center of mass, with the next three being the principal components of the system of points. By knowing r2 we can approximate a distance matrix of a protein with an expected RMSD value of about 4.5Å. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement http://predictioncenter.org/caspR)

Critical assessment of methods of protein structure prediction: Progress and new directions in round XI

Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas