Structural and functional characterization of a frataxin from a thermophilic organism

Frataxins form an interesting family of iron‐binding proteins with an almost unique fold and are highly conserved from bacteria to primates. They have a pivotal role in iron–sulfur cluster biogenesis as regulators of the rates of cluster formation, as it is testified by the fact that frataxin absence is incompatible with life and reduced levels of the protein lead to the recessive neurodegenerative disease Friedreich's ataxia. Despite its importance, the structure of frataxin has been solved only from relatively few species. Here, we discuss the X‐ray structure of frataxin from the thermophilic fungus Chaetomium thermophilum, and the characterization of its interactions and dynamics in solution. We show that this eukaryotic frataxin has an unusual variation in the classical frataxin fold: the last helix is shorter than in other frataxins which results in a less symmetrical and compact structure. The stability of this protein is comparable to that of human frataxin, currently the most stable among the frataxin orthologues. We also characterized the iron‐binding mode of Ct frataxin and demonstrated that it binds it through a semiconserved negatively charged ridge on the first helix and beta‐strand. Moreover, this frataxin is also able to bind the bacterial ortholog of the desulfurase, which is central in iron–sulfur cluster synthesis, and act as its inhibitor

Identification of the first structurally validated covalent ligands of the small GTPase RAB27A

Rab27A is a small GTPase, which mediates transport and docking of secretory vesicles at the plasma membrane via protein–protein interactions (PPIs) with effector proteins. Rab27A promotes the growth and invasion of multiple cancer types such as breast, lung and pancreatic, by enhancing secretion of chemokines, metalloproteases and exosomes. The significant role of Rab27A in multiple cancer types and the minor role in adults suggest that Rab27A may be a suitable target to disrupt cancer metastasis. Similar to many GTPases, the flat topology of the Rab27A-effector PPI interface and the high affinity for GTP make it a challenging target for inhibition by small molecules. Reported co-crystal structures show that several effectors of Rab27A interact with the Rab27A SF4 pocket (‘WF-binding pocket’) via a conserved tryptophan–phenylalanine (WF) dipeptide motif. To obtain structural insight into the ligandability of this pocket, a novel construct was designed fusing Rab27A to part of an effector protein (fRab27A), allowing crystallisation of Rab27A in high throughput. The paradigm of KRas covalent inhibitor development highlights the challenge presented by GTPase proteins as targets. However, taking advantage of two cysteine residues, C123 and C188, that flank the WF pocket and are unique to Rab27A and Rab27B among the >60 Rab family proteins, we used the quantitative Irreversible Tethering (qIT) assay to identify the first covalent ligands for native Rab27A. The binding modes of two hits were elucidated by co-crystallisation with fRab27A, exemplifying a platform for identifying suitable lead fragments for future development of competitive inhibitors of the Rab27A-effector interaction interface, corroborating the use of covalent libraries to tackle challenging targets

Link-level throughput maximization using deep reinforcement learning

Abstract A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme (MCS) selection. Given the complex problem space, reward shaping is utilized instead of classical training procedures. The time-frame utilities are decomposed into subframe rewards, and a stepwise training procedure is proposed, starting from a simplified power allocation setup without MCS selection, incorporating MCS selection gradually, as the agents learn optimal power allocation. The proposed method outperforms both weighted minimum mean squared error (WMMSE) and Fractional Programming (FP) with idealized MCS selections

Link activation using variational graph autoencoders

Abstract An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from each interfering cluster is activated while the rest of the users in that cluster remain idle. The proposed framework is scalable, works across several network topologies such as device to device (D2D), and is close to the optimal solution in performance

Direct targeting of the Ras GTPase superfamily through structure-based design

The Ras superfamily of small monomeric GTPases includes some of the most prominent cancer targets for which no selective therapeutic agent has yet been successfully developed. The turn of the millennium saw a resurgence of efforts to target these enzymes using new and improved biophysical techniques to overcome the perceived difficulties of insurmountably high affinity for guanosine nucleotides and flat, flexible topology lacking suitable pockets for small molecule inhibitors. Further, recent investigations have begun to probe the dynamic conformational status of GTP-bound Ras, opening up new mechanisms of inhibition. While much of the literature has focused on the oncogenic Ras proteins, particularly K-Ras, these represent only a small minority of therapeutically interesting targets within the superfamily; for example, the Rab GTPases are the largest subfamily of about 70 members, and present an as yet untapped class of potential targets. The present review documents the key methodologies employed to date in structure-guided attempts to drug the Ras GTPases, and forecasts their transferability to other similarly challenging proteins in the superfamily