Structure of a 30S pre-initiation complex stalled by GE81112 reveals structural parallels in bacterial and eukaryotic protein synthesis initiation pathways

In bacteria, the start site and the reading frame of the messenger RNA are selected by the small ribosomal subunit (30S) when the start codon, typically an AUG, is decoded in the P-site by the initiator tRNA in a process guided and controlled by three initiation factors. This process can be efficiently inhibited by GE81112, a natural tetrapeptide antibiotic that is highly specific toward bacteria. Here GE81112 was used to stabilize the 30S pre-initiation complex and obtain its structure by cryo-electron microscopy. The results obtained reveal the occurrence of changes in both the ribosome conformation and initiator tRNA position that may play a critical role in controlling translational fidelity. Furthermore, the structure highlights similarities with the early steps of initiation in eukaryotes suggesting that shared structural features guide initiation in all kingdoms of life

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

A Guide to Human Zinc Absorption: General Overview and Recent Advances of In Vitro Intestinal Models

Zinc absorption in the small intestine is one of the main mechanisms regulating the systemic homeostasis of this essential trace element. This review summarizes the key aspects of human zinc homeostasis and distribution. In particular, current knowledge on human intestinal zinc absorption and the influence of diet-derived factors on bioaccessibility and bioavailability as well as intrinsic luminal and basolateral factors with an impact on zinc uptake are discussed. Their investigation is increasingly performed using in vitro cellular intestinal models, which are continually being refined and keep gaining importance for studying zinc uptake and transport via the human intestinal epithelium. The vast majority of these models is based on the human intestinal cell line Caco-2 in combination with other relevant components of the intestinal epithelium, such as mucin-secreting goblet cells and in vitro digestion models, and applying improved compositions of apical and basolateral media to mimic the in vivo situation as closely as possible. Particular emphasis is placed on summarizing previous applications as well as key results of these models, comparing their results to data obtained in humans, and discussing their advantages and limitations.DFG, 316442145, FOR 2558: Interaktionen von essenziellen Spurenelementen in gesunden und erkrankten älteren Menschen (TraceAge)DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli