Learning a generalized graph transformer for protein function prediction in dissimilar sequences.

Background: In the face of a growing disparity between high-throughput sequence data and low-throughput experimental studies, the emerging field of deep learning stands as a promising alternative. Generally, many data-driven approaches are capable of facilitating fast and accurate predictions of protein functions. Nevertheless, the inherent statistical nature of deep learning techniques may limit their generalization capabilities when applied to novel nonhomologous proteins that diverge significantly from existing ones.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking.

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate.

Hierarchical graph transformer with contrastive learning for protein function prediction.

Motivation: In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy.

[Discussion on Strengthening the Hospital Management of in Vitro Diagnostic Reagents].

This article explored practical management experience of in vitro diagnostic reagents, continuously improved the informatization of in vitro diagnostic reagents and carried out cost-benefit analysis further, through studying "in vitro diagnostic reagents Registration" issued by China Food and Drug Administration in 2014. So that we achieved a unified centralized management of in vitro diagnostic reagents, improved the working efficiency and provided patients with more accurate and efficient service.