Mechanism of Ligand Discrimination by the NMT1 Riboswitch

Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer

Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg2+) placement is a critical decision that impacts affinity predictions. When information regarding Mg2+ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg2+ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.

Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer

Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mitochondria-related genes (MRGs) for BC patients using Machine Learning. In this study, we analyzed GSE13507 datasets and identified 752 DE-MRGs in BC specimens. Functional enrichment analysis uncovered the significant roles of 752 DE-MRGs in key processes such as cellular and organ development, as well as gene regulation. The analysis revealed the crucial functions of these genes in transcriptional regulation and protein-DNA interactions. Then, we performed LASSO and SVM-RFE, and identified four critical diagnostic genes including GLRX2, NMT1, OXSM and TRAF3IP3. Based on the above four genes, we developed a novel diagnostic model whose diagnostic value was confirmed in GSE13507, GSE3167 and GSE37816 datasets. Moreover, we reported the expressing pattern of GLRX2, NMT1, OXSM and TRAF3IP3 in BC samples. Immune cell infiltration analysis revealed that the four genes were associated with several immune cells. Finally, we performed RT-PCR and confirmed NMT1 was highly expressed in BC cells. Functional experiments revealed that knockdown of NMT1 suppressed the proliferation of BC cells. Overall, we have formulated a diagnostic potential that offered a comprehensive framework for delving into the underlying mechanisms of BC. Before proceeding with clinical implementation, it is essential to undertake further investigative efforts to validate its diagnostic effectiveness in BC patients.

Adenine Methylation Enhances the Conformational Flexibility of an RNA Hairpin Tetraloop

The N6-methyladenosine modification is one of the most abundant post-transcriptional modifications in ribonucleic acid (RNA) molecules. Using molecular dynamics simulations and alchemical free-energy calculations, we studied the structural and energetic implications of incorporating this modification in an adenine mononucleotide and an RNA hairpin structure. At the mononucleotide level, we found that the syn configuration is more favorable than the anti configuration by 2.05 ± 0.15 kcal/mol. The unfavorable effect of methylation was due to the steric overlap between the methyl group and a nitrogen atom in the purine ring. We then probed the effect of methylation in an RNA hairpin structure containing an AUCG tetraloop, which is recognized by a "reader" protein (YTHDC1) to promote transcriptional silencing of long noncoding RNAs. While methylation had no significant conformational effect on the hairpin stem, the methylated tetraloop showed enhanced conformational flexibility compared to the unmethylated tetraloop. The increased flexibility was associated with the outward flipping of two bases (A6 and U7) which formed stacking interactions with each other and with the C8 and G9 bases in the tetraloop, leading to a conformation similar to that in the RNA/reader protein complex. Therefore, methylation-induced conformational flexibility likely facilitates RNA recognition by the reader protein.

Quantitative Assessment of Energetic Contributions of Residues in a SARS-CoV-2 Viral Enzyme/Nanobody Interface

The highly conserved protease enzyme from SARS-CoV-2 (MPro) is crucial for viral replication and is an attractive target for the design of novel inhibitory compounds. MPro is known to be conformationally flexible and has been stabilized in an extended conformation in a complex with a novel nanobody (NB2B4), which inhibits the dimerization of the enzyme via binding to an allosteric site. However, the energetic contributions of the nanobody residues stabilizing the MPro/nanobody interface remain unresolved. We probed these residues using all-atom MD simulations in combination with alchemical free energy calculations by studying the physical residue-residue interactions and discovered the role of hydrophobic and electrostatic interactions in stabilizing the complex. Specifically, we found via mutational analysis that three interfacial nanobody residues (Y59, R106, and L109) contributed significantly, two residues (L107 and P110) contributed moderately, and two residues (H112 and T113) contributed minimally to the overall binding affinity of the nanobody. We also discovered that the nanobody affinity could be enhanced via a charge-reversal mutation (D62R) that alters the local interfacial electrostatic environment of this residue in the complex. These findings are potentially useful in designing novel synthetic nanobodies as allosteric inhibitors of MPro.