Biomedical subjects

Navraj S Pannu

Publications and source records attributed to Navraj S Pannu.

9 recordsLinked to original sources

PubMed2005

Extending the resolution and phase-quality limits in automated model building with iterative refinement.

Previously, the direct use of prior phase information from a single-wavelength anomalous diffraction (SAD) experiment with a multivariate likelihood function applied to automated model building with iterative refinement has been proposed [Skubák et al. (2004), Acta Cryst. D60, 2196-2201]. In this approach, the anomalous information from the experimental data is used in refinement to derive phase information in a maximum-likelihood formalism and provided a more theoretically valid way of incorporating prior phase information compared with current approaches. In the present work, the SAD multivariate likelihood function that directly uses prior phase information is tested against currently used functions on many different SAD data sets which exhibit a wide range of resolution limits and anomalous signal. The results clearly show the importance of the more theoretically valid utilization of prior phase information: the SAD function extends the resolution and phase-quality limits needed for successful automated model building with iterative refinement. Indeed, the multivariate likelihood function reduces the overfitting in the refinement procedure and performs consistently better than the current refinement targets in terms of the quality of the models obtained and the number of residues built.

Computational Biology↗

PubMed2004

Direct incorporation of experimental phase information in model refinement.

The incorporation of prior phase information into a maximum-likelihood formalism has been shown to strengthen model refinement. However, the currently available likelihood refinement target using prior phase information has shortcomings; the 'phased' refinement target considers experimental phase information indirectly and statically in the form of Hendrickson-Lattman coefficients. Furthermore, the current refinement target implicitly assumes that the prior phase information is independent of the calculated model structure factor. This paper describes the derivation of a multivariate likelihood function that overcomes these shortcomings and directly incorporates experimental phase information from a single-wavelength anomalous diffraction (SAD) experiment. This function, which simultaneously refines heavy-atom and model parameters, has been implemented in the refinement program REFMAC5. The SAD function used in conjunction with the automated model-building procedures of ARP/wARP leads to a successful solution when current likelihood functions fail in a test case shown.

Algorithms↗

PubMed2004

Crystal structure of an empty capsid of turnip yellow mosaic virus.

Empty capsids (artificial top component) of turnip yellow mosaic virus were co-crystallized with an encapsidation initiator RNA hairpin. No clear density was observed for the RNA, but there were clear differences in the conformation of a loop of the coat protein at the opening of the pentameric capsomer (formed by five A-subunits) protruding from the capsid, compared to the corresponding loop in the intact virus. Further differences were found at the N terminus of the A-subunit. These differences have implications for the mechanism of decapsidation of the virus, required for infection.

Capsid↗

PubMed2004

Structure of an anti-Lewis X Fab fragment in complex with its Lewis X antigen.

The Lewis X trisaccharide is pivotal in mediating specific cell-cell interactions. Monoclonal antibody 291-2G3-A, which was generated from mice infected with schistosomes, has been shown to recognize the Lewis X trisaccharide. Here we describe the structure of the Fab fragment of 291-2G3-A, with Lewis X, to 1.8 A resolution. The crystallographic analysis revealed that the antigen binding site is a rather shallow binding pocket, and residues from all six complementary determining regions of the antibody contact all sugar residues. The high specificity of the binding pocket does not result in high affinity; the K(D) determined by isothermal calorimetry is 11 microM. However, this affinity is in the same range as for other sugar-antibody complexes. The detailed understanding of the antibody-Lewis X interaction revealed by the crystal structure may be helpful in the design of better diagnostic tools for schistosomiasis and for studying Lewis X-mediated cell-cell interactions by antibody interference.

Base Sequence↗

PubMed2004

CRANK: new methods for automated macromolecular crystal structure solution.

CRANK is a novel suite for automated macromolecular structure solution and uses recently developed programs for substructure detection, refinement, and phasing. CRANK utilizes methods for substructure detection and phasing and combines them with existing crystallographic programs for density modification and automated model building in a convenient and easy-to-use CCP4i graphical interface. The data model used conforms to the XML eXtensible Markup Language specification and works as a common language to communicate data between many different applications inside and outside of the suite. The application of CRANK on various test cases has yielded promising results: with minimal user input, CRANK can produce better quality solutions over currently available programs.

Crystallography↗

PubMed2003

The application of multivariate statistical techniques improves single-wavelength anomalous diffraction phasing.

Recently, there has been a resurgence in phasing using the single-wavelength anomalous diffraction (SAD) experiment; data from a single wavelength in combination with techniques such as density modification have been used to solve macromolecular structures, even with a very small anomalous signal. Here, a formulation for SAD phasing and refinement employing multivariate statistical techniques is presented. The equation developed accounts explicitly for the correlations among the observed and calculated Friedel mates in a SAD experiment. The correlated SAD equation has been implemented and test cases performed on real diffraction data have revealed better results compared with currently used programs in terms of correlation with the final map and obtaining more reliable phase probability statistics.

Crystallography, X-Ray↗

PubMed2003

Application of the complex multivariate normal distribution to crystallographic methods with insights into multiple isomorphous replacement phasing.

Probabilistic methods involving maximum-likelihood parameter estimation have become a powerful tool in computational crystallography. At the centre of these methods are the relevant probability distributions. Here, equations are developed based on the complex multivariate normal distribution that generalize the distributions currently used in maximum-likelihood model and heavy-atom refinement. In this treatment, the effects of various sources of error in the experiment are considered separately and allowance is made for correlations among sources of error. The multivariate distributions presented are closely related to the distributions previously derived in ab initio phasing and can be applied to many different aspects of a crystallographic structure-determination process including model refinement, density modification, heavy-atom phasing and refinement or combinations of them. The underlying probability distributions for multiple isomorphous replacement are re-examined using these techniques. The re-analysis requires the underlying assumptions to be made explicitly and results in a variance term that, unlike those previously used for maximum-likelihood multiple isomorphous replacement phasing, is expressed explicitly in terms of structure-factor covariances. Test cases presented show that the newly derived multiple isomorphous replacement likelihood functions perform satisfactorily compared with currently used programs.

Crystallography, X-Ray↗

PubMed2003

Crystallization and preliminary X-ray analysis of an anti-LewisX Fab fragment with and without its LewisX antigen.

LewisX-containing glycoconjugates are abundantly expressed by schistosomes and are assumed to be of prime importance for the survival of the parasite within the human host. Monoclonal antibody 291-2G3-A, which was generated from mice infected with schistosomes, was found to interact with monomers, dimers and trimers of the LewisX trisaccharide. The Fab fragment of monoclonal antibody 291-2G3-A has been crystallized and soaked with its LewisX antigen. X-ray data sets were recorded for the different Fab crystals with and without LewisX. Crystals grown from 25% polyethylene glycol 3350, 0.17 M ammonium sulfate and 15% glycerol belong to the triclinic space group P1, with unit-cell parameters a = 67.4, b = 71.6, c = 104.8 A, alpha = 86.5, beta = 71.3, gamma = 83.3 degrees for the native crystals and with slightly different unit-cell parameters a = 67.3, b = 72.4, c = 104.8 A, alpha = 85.8, beta = 71.3, gamma = 83.3 degrees for the crystals containing bound LewisX. Crystals grown from 14% PEG 3350, 50 mM Tris pH 8 and soaked with LewisX also belong to the triclinic space group P1, but with different unit-cell parameters a = 45.1, b = 60.8, c = 91.6 A, alpha = 96.0, beta = 95.4, gamma = 101.8 degrees.

Animals↗

PubMed2003

How vitronectin binds PAI-1 to modulate fibrinolysis and cell migration.

The interaction of the plasma protein vitronectin with plasminogen activator inhibitor-1 (PAI-1) is central to human health. Vitronectin binding extends the lifetime of active PAI-1, which controls hemostasis by inhibiting fibrinolysis and has also been implicated in angiogenesis. The PAI-1-vitronectin binding interaction also affects cell adhesion and motility. For these reasons, elevated PAI-1 activities are associated both with coronary thrombosis and with a poor prognosis in many cancers. Here we show the crystal structure at a resolution of 2.3 A of the complex of the somatomedin B domain of vitronectin with PAI-1. The structure of the complex explains how vitronectin binds to and stabilizes the active conformation of PAI-1. It also explains the tissue effects of PAI-1, as PAI-1 competes for and sterically blocks the interaction of vitronectin with cell surface receptors and integrins. Structural understanding of the essential biological roles of the interaction between PAI-1 and vitronectin opens the prospect of specifically designed blocking agents for the prevention of thrombosis and treatment of cancer.

Amino Acid Sequence↗