Search bioRxiv⌕ Search

Biology subjects

Acosta-Reyes, F. J.

Publications and source records attributed to Acosta-Reyes, F. J..

2 recordsLinked to original sources

Cryo-EM studies of the interplay between uS2 ribosomal protein and leaderless mRNA during bacterial translation initiation

In bacteriophage{lambda} lysogens, the{lambda} cI repressor is encoded by the leaderless transcript (lmRNA) initiated at the{lambda} pRM promoter. Translation is enhanced in rpsB mutants deficient in ribosomal protein uS2. Although translation initiation of lmRNA is conserved in bacteria, archaea, and eukaryotes, structural insight of a lmRNA translation initiation complex is missing. Here, we use cryo-EM to solve the structures of the uS2-deficient 70S ribosome of host E. coli mutant rpsB11 and the wild-type 70S complex with{lambda} cI lmRNA and fmet-tRNAfMet. Importantly, the uS2-deficient 70S ribosome also lacks protein bS21. The anti-Shine-Dalgarno (aSD) region is structurally supported by bS21, so that the absence of the latter causes the aSD to divert from the normal mRNA exit pathway, easing the exit of lmRNA. A {pi}-stacking interaction between the monitor base A1493 and A(+4) of lmRNA potentially acts as a recognition signal. Coulomb charge flow, along with peristalsis-like dynamics within the mRNA entry channel due to the increased 30S head rotation caused by the absence of uS2, are likely to facilitate the propagation of lmRNA through the ribosome. These findings lay the groundwork for future research on the mechanism of translation and the co-evolution of lmRNA and mRNA that includes the emergence of a defined ribosome-binding site of the transcript.

biophysics↗

Recovery of conformational continuum from single-particle cryo-EM data: Optimization of ManifoldEM informed by ground-truth studies

This work is based on the manifold-embedding approach to study biological molecules exhibiting continuous conformational changes. Previous work established a method capable of reconstructing 3D movies and accompanying energetics of atomic-level structures from single-particle cryo-EM images of macromolecules displaying multiple conformational degrees of freedom. Here, we introduce an unsupervised geometric machine learning approach that is informed by detailed heuristic analysis of manifolds formed by simulated heterogeneous cryo-EM datasets generated from an atomic structure. These simulated data were generated with increasing complexity to account for multiple conformational motions, state occupancies and typical microscope parameters in a wide range of signal-to-noise ratios. Using these datasets as ground-truth, we provide detailed exposition of our findings using several conformational motions while exploring the available parameter space. Guided by these insights, we build a framework to leverage the high-dimensional geometric information obtained towards reconstituting a quasi-continuum of conformational states in the form of a free-energy landscape and respective 3D density maps for all states therein. As shown by a direct comparison of results, this framework offers substantial improvements relative to the previous work.

biophysics↗