PubMed Health⌕ Search

Biomedical subjects

Bimal K Rath

Publications and source records attributed to Bimal K Rath.

2 recordsLinked to original sources

Functional conformations of the L11-ribosomal RNA complex revealed by correlative analysis of cryo-EM and molecular dynamics simulations.

The interaction between the GTPase-associated center (GAC) and the aminoacyl-tRNA.EF-Tu.GTP ternary complex is of crucial importance in the dynamic process of decoding and tRNA accommodation. The GAC includes protein L11 and helices 43-44 of 23S rRNA (referred to as L11-rRNA complex). In this study, a method of fitting based on a systematic comparison between cryo-electron microscopy (cryo-EM) density maps and structures obtained by molecular dynamics simulations has been developed. This method has led to the finding of atomic models of the GAC that fit the EM maps with much improved cross-correlation coefficients compared with the fitting of the X-ray structure. Two types of conformations of the L11-rRNA complex, produced by the simulations, match the cryo-EM maps representing the states either bound or unbound to the aa-tRNA.EF-Tu.GTP ternary complex. In the bound state, the N-terminal domain of L11 is extended from its position in the crystal structure, and the base of nucleotide A1067 in the 23S ribosomal RNA is flipped out. This position of the base allows the RNA to reach the elbow region of the aminoacyl-tRNA when the latter is bound in the A/T site. In the unbound state, the N-terminal domain of L11 is rotated only slightly, and A1067 of the RNA is flipped back into the less-solvent-exposed position, as in the crystal structure. By matching our experimental cryo-EM maps with much improved cross-correlation coefficients compared to the crystal structure, these two conformations prove to be strong candidates of the two functional states.

Amino Acid Sequence↗

Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.

Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80% of the particles that an expert would select with less than 10% false positives.

Algorithms↗