Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Visible Light Activates Coumarin-Caged mRNA for Cytoplasmic Cap Methylation in Cells.

Protein synthesis is important and regulated by various mechanisms in the cell. Translation initiation in eukaryotes starts at the 5' cap and is the most complex of the three phases of mRNA translation. It requires methylation of the N7 position of the terminal guanosine (m7 G). The canonical capping occurs in the nucleus, however, cytoplasmic recapping has been discovered. It functions in switching mRNAs between translating and non-translating states, but the individual steps are difficult to dissect. We targeted cytoplasmic cap methylation as the ultimate step of cytoplasmic recapping. We present an N7G photocaged 5' cap that can be activated for cytoplasmic methylation by visible light. We report chemical and chemo-enzymatic synthesis of this 5' cap with 7-(diethylamino)-4-methyl-coumarin (DEACM) at the N7G and validate that it is not bound by translation initiation factor 4E (eIF4E). We demonstrate incorporation into mRNA, the release of unmethylated cap analog and enzymatic remethylation to functional cap 0 after irradiation at 450 nm. In cells, irradiation triggers translation of mRNAs with the N7G photocaged 5' cap via cytoplasmic cap methylation.

Genetic complementation screening and molecular docking give new insight on phosphorylation-dependent Mastl kinase activation.

Mastl is a mitotic kinase that is essential for error-free chromosome segregation. It is an atypical member of AGC kinase family, possessing a unique non-conserved middle region. The mechanism of Mastl activation has been studied extensively in vitro. Phosphorylation of several residues were identified to be crucial for activation. These sites correspond to T193 and T206 in the activation loop and S861 in the C-terminal tail of mouse Mastl. To date, the significance of these phosphosites was not confirmed in intact mammalian cells. Here, we utilize a genetic complementation approach to determine the essentials of mammalian Mastl kinase activation. We used tamoxifen-inducible conditional knockout mouse embryonic fibroblasts to delete endogenous Mastl and screened various mutants for their ability to complement its loss. S861A mutant was able to complement endogenous Mastl loss. In parallel, we performed computational molecular docking studies to evaluate the significance of this residue for kinase activation. Our in-depth sequence and structure analysis revealed that Mastl pS861 does not belong to a conformational state, where the phosphoresidue contributes to C-tail docking. C-tail of Mastl is relatively short and it lacks a hydrophobic (HF) motif that would otherwise help its anchoring over N-lobe, required for the final steps of kinase activation. Our results show that phosphorylation of Mastl C-tail turn motif (S861) is dispensable for kinase function in cellulo.Communicated by Ramaswamy H. Sarma.

Enzymatic Generation of Double-Modified AdoMet Analogues and Their Application in Cascade Reactions with Different Methyltransferases.

Methyltransferases (MTases) have become an important tool for site-specific alkylation and biomolecular labelling. In biocatalytic cascades with methionine adenosyltransferases (MATs), transfer of functional moieties has been realized starting from methionine analogues and ATP. However, the widespread use of S-adenosyl-l-methionine (AdoMet) and the abundance of MTases accepting sulfonium centre modifications limit selective modification in mixtures. AdoMet analogues with additional modifications at the nucleoside moiety bear potential for acceptance by specific MTases. Here, we explored the generation of double-modified AdoMets by an engineered Methanocaldococcus jannaschii MAT (PC-MjMAT), using 19 ATP analogues in combination with two methionine analogues. This substrate screening was extended to cascade reactions and to MTase competition assays. Our results show that MTase targeting selectivity can be improved by using bulky substituents at the N6 of adenine. The facile access to >10 new AdoMet analogues provides the groundwork for developing MAT-MTase cascades for orthogonal biomolecular labelling.

How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?

In all living organisms, protein kinases regulate various cell signaling events through phosphorylation. The phosphorylation occurs upon transferring an ATP's terminal phosphate to a target residue. Because of the central role of protein kinases in several proliferative pathways, point mutations occurring within the kinase's ATP-binding site can lead to a constitutively active enzyme, and ultimately, to cancer. A select set of these point mutations can also make the enzyme drug resistant toward the available kinase inhibitors. Because of technical and economical limitations, rapid experimental exploration of the impact of these mutations remains to be a challenge. This underscores the importance of kinase-ligand binding affinity prediction tools that are poised to measure the efficacy of inhibitors in the presence of kinase mutations. To this end, here, we compare the performances of six web-based scoring tools (DSX-ONLINE, KDEEP, HADDOCK2.2, PDBePISA, Pose&Rank, and PRODIGY-LIG) in assessing the impact of kinase mutations on their interactions with their inhibitors. This assessment is carried out on a new structure-based BINDKIN benchmark we compiled. BINDKIN contains wild-type and mutant structure pairs of kinase-inhibitor complexes, together with their corresponding experimental binding affinities (in the form of IC50, K d, and K i). The performance of various web servers over BINDKIN shows that they cannot predict the binding affinities (ΔGs) of wild-type and mutant cases directly. Still, they could catch whether a mutation improves or worsens the ligand binding (ΔΔGs) where the highest Pearson's R correlation coefficient is reached by DSX-ONLINE over the K i dataset. When homology models are used instead of K i-associated crystal structures, DSX-ONLINE loses its predictive capacity. These results highlight that there is room to improve the available scoring functions to estimate the impact of protein kinase point mutations on inhibitor binding. The BINDKIN benchmark with all related results is freely accessible online (https://github.com/CSB-KaracaLab/BINDKIN).