Sequential action of a tRNA base editor in conversion of cytidine to pseudouridine

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

AlphaFold2 structures template ligand discovery

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

Molecular Coping Mechanisms: Reprogramming tRNAs To Regulate Codon-Biased Translation of Stress Response Proteins

As part of the classic central dogma of molecular biology, transfer RNAs (tRNAs) are integral to protein translation as the adaptor molecules that link the genetic code in messenger RNA (mRNA) to the amino acids in the growing peptide chain. tRNA function is complicated by the existence of 61 codons to specify 20 amino acids, with most amino acids coded by two or more synonymous codons. Further, there are often fewer tRNAs with unique anticodons than there are synonymous codons for an amino acid, with a single anticodon able to decode several codons by "wobbling" of the base pairs arising between the third base of the codon and the first position on the anticodon. The complications introduced by synonymous codons and wobble base pairing began to resolve in the 1960s with the discovery of dozens of chemical modifications of the ribonucleotides in tRNA, which, by analogy to the epigenome, are now collectively referred to as the epitranscriptome for not changing the genetic code inherent to all RNA sequences. tRNA modifications were found to stabilize codon-anticodon interactions, prevent misinitiation of translation, and promote translational fidelity, among other functions, with modification deficiencies causing pathological phenotypes. This led to hypotheses that modification-dependent tRNA decoding efficiencies might play regulatory roles in cells. However, it was only with the advent of systems biology and convergent "omic" technologies that the higher level function of synonymous codons and tRNA modifications began to emerge.Here, we describe our laboratories' discovery of tRNA reprogramming and codon-biased translation as a mechanism linking tRNA modifications and synonymous codon usage to regulation of gene expression at the level of translation. Taking a historical approach, we recount how we discovered that the 8-10 modifications in each tRNA molecule undergo unique reprogramming in response to cellular stresses to promote translation of mRNA transcripts with unique codon usage patterns. These modification tunable transcripts (MoTTs) are enriched with specific codons that are differentially decoded by modified tRNAs and that fall into functional families of genes encoding proteins necessary to survive the specific stress. By developing and applying systems-level technologies, we showed that cells lacking specific tRNA modifications are sensitized to certain cellular stresses by mistranslation of proteins, disruption of mitochondrial function, and failure to translate critical stress response proteins. In essence, tRNA reprogramming serves as a cellular coping strategy, enabling rapid translation of proteins required for stress-specific cell response programs. Notably, this phenomenon has now been characterized in all organisms from viruses to humans and in response to all types of environmental changes. We also elaborate on recent findings that cancer cells hijack this mechanism to promote their own growth, metastasis, and chemotherapeutic resistance. We close by discussing how understanding of codon-biased translation in various systems can be exploited to develop new therapeutics and biomanufacturing processes.

A tRNA modification in Mycobacterium tuberculosis facilitates optimal intracellular growth

Diverse chemical modifications fine-tune the function and metabolism of tRNA. Although tRNA modification is universal in all kingdoms of life, profiles of modifications, their functions, and physiological roles have not been elucidated in most organisms including the human pathogen, Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. To identify physiologically important modifications, we surveyed the tRNA of Mtb, using tRNA sequencing (tRNA-seq) and genome-mining. Homology searches identified 23 candidate tRNA modifying enzymes that are predicted to create 16 tRNA modifications across all tRNA species. Reverse transcription-derived error signatures in tRNA-seq predicted the sites and presence of nine modifications. Several chemical treatments prior to tRNA-seq expanded the number of predictable modifications. Deletion of Mtb genes encoding two modifying enzymes, TruB and MnmA, eliminated their respective tRNA modifications, validating the presence of modified sites in tRNA species. Furthermore, the absence of mnmA attenuated Mtb growth in macrophages, suggesting that MnmA-dependent tRNA uridine sulfation contributes to Mtb intracellular growth. Our results lay the foundation for unveiling the roles of tRNA modifications in Mtb pathogenesis and developing new therapeutics against tuberculosis.

Fluorescent labeling of tRNA for rapid kinetic interaction studies with tRNA-binding proteins

Transfer RNA (tRNA) plays a critical role during translation and interacts with numerous proteins during its biogenesis, functional cycle and degradation. In particular, tRNA is extensively post-transcriptionally modified by various tRNA modifying enzymes which each target a specific nucleotide at different positions within tRNAs to introduce different chemical modifications. Fluorescent assays can be used to study the interaction between a protein and tRNA. Moreover, rapid mixing fluorescence stopped-flow assays provide insights into the kinetics of the tRNA-protein interaction in order to elucidate the tRNA binding mechanism for the given protein. A prerequisite for these studies is a fluorescently labeled molecule, such as fluorescent tRNA, wherein a change in fluorescence occurs upon protein binding. In this chapter, we discuss the utilization of tRNA modifications in order to introduce fluorophores at particular positions within tRNAs. Particularly, we focus on in vitro thiolation of a uridine at position 8 within tRNAs using the tRNA modification enzyme ThiI, followed by labeling of the thiol group with fluorescein. As such, this fluorescently labeled tRNA is primarily unmodified, with the exception of the thiolation modification to which the fluorophore is attached, and can be used as a substrate to study the binding of different tRNA-interacting factors. Herein, we discuss the example of studying the tRNA binding mechanism of the tRNA modifying enzymes TrmB and DusA using internally fluorescein-labeled tRNA.

Functional analysis of tRNA modification enzymes using mutational profiling

Transfer RNA (tRNA) delivers amino acids to the ribosome and functions as an essential adapter molecule for decoding codons on the messenger RNA (mRNA) during protein synthesis. Before attaining their proper activity, tRNAs undergo multiple post-transcriptional modifications with highly diversified roles such as stabilization of the tRNA structure, recognition of aminoacyl tRNA synthetases, precise codon-anticodon recognition, support of viral replication and onset of immune responses. The synthesis of the majority of modified nucleosides is catalyzed by a site-specific tRNA modification enzyme. This chapter provides a detailed protocol for using mutational profiling to analyze the enzymatic function of a tRNA methyltransferase in a high-throughput manner. In a previous study, we took tRNA m1A22 methyltransferase TrmK from Geobacillus stearothermophilus as a model tRNA methyltransferase and applied this protocol to gain mechanistic insights into how TrmK recognizes the substrate tRNAs. In theory, this protocol can be used unaltered for studying enzymes that catalyze modifications at the Watson-Crick face such as 1-methyladenosine (m1A), 3-methylcytosine (m3C), 3-methyluridine (m3U), 1-methylguanosine (m1G), and N2,N2-dimethylguanosine (m22G).

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.