GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction.

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

On the origin and evolution of RNA editing in metazoans.

Extensive adenosine-to-inosine (A-to-I) editing of nuclear-transcribed mRNAs is the hallmark of metazoan transcriptional regulation. Here, by profiling the RNA editomes of 22 species that cover major groups of Holozoa, we provide substantial evidence supporting A-to-I mRNA editing as a regulatory innovation originating in the last common ancestor of extant metazoans. This ancient biochemistry process is preserved in most extant metazoan phyla and primarily targets endogenous double-stranded RNA (dsRNA) formed by evolutionarily young repeats. We also find intermolecular pairing of sense-antisense transcripts as an important mechanism for forming dsRNA substrates for A-to-I editing in some but not all lineages. Likewise, recoding editing is rarely shared across lineages but preferentially targets genes involved in neural and cytoskeleton systems in bilaterians. We conclude that metazoan A-to-I editing might first emerge as a safeguard mechanism against repeat-derived dsRNA and was later co-opted into diverse biological processes due to its mutagenic nature.

Engineering the Active Site Pocket to Enhance the Catalytic Efficiency of a Novel Feruloyl Esterase Derived From Human Intestinal Bacteria Dorea formicigenerans.

The human gut microbiota play essential roles in metabolism and human health, especially by enzymatically utilizing dietary fiber that the host cannot directly digest and releasing functional components including short-chain fatty acids (SCFAs) and hydroxycinnamic acids (e.g., ferulic acid). In our previous study, seven potential feruloyl esterase (FAE) genes were identified from the gut microbiota. In the current work, one of the genes encoding a novel FAE (DfFAE) from Dorea formicigenerans of Firmicutes was bacterially expressed, purified and characterized. The 30.5 kDa type-A DfFAE has an optimum pH and temperature of 8.4 and 40 °C, respectively, exhibiting a higher substrate specificity toward short-chain acyl-ester substrate (pNPA). The AlphaFold2 based ab initio structural modeling revealed a five α-helices cap domain that shaped an unusually narrow and deep active site pocket containing a specific substrate access tunnel in DfFAE. Furthermore, rational design strategy was subjected to the active site pocket in an aim of improving its enzymatic activities. The mutants V252A, N156A, W255A, P149A, and P186A showed 1.8 to 5.7-fold increase in catalytic efficiency toward pNPA, while W255A also exhibited altered substrate preference toward long-chain substrate pNPO (45.5-fold). This study highlighted an unusual active site architecture in DfFAE that influenced its substrate selectivity and illustrated the applicability of rational design for enhanced enzymatic properties.

A draft genome assembly of the eastern banjo frog Limnodynastes dumerilii dumerilii (Anura: Limnodynastidae).

Amphibian genomes are usually challenging to assemble due to their large genome size and high repeat content. The Limnodynastidae is a family of frogs native to Australia, Tasmania and New Guinea. As an anuran lineage that successfully diversified on the Australian continent, it represents an important lineage in the amphibian tree of life but lacks reference genomes. Here we sequenced and annotated the genome of the eastern banjo frog Limnodynastes dumerilii dumerilii to fill this gap. The total length of the genome assembly is 2.38 Gb with a scaffold N50 of 285.9 kb. We identified 1.21 Gb of non-redundant sequences as repetitive elements and annotated 24,548 protein-coding genes in the assembly. BUSCO assessment indicated that more than 94% of the expected vertebrate genes were present in the genome assembly and the gene set. We anticipate that this annotated genome assembly will advance the future study of anuran phylogeny and amphibian genome evolution.