# 每日论文简报

- 生成时间：2026-04-12 22:15:33 (Asia/Shanghai)
- 检索窗口：最近 24 小时
- 命中概览：LLM=0, Vision=0, PubMed AI=1, OpenAlex AI=0
- 排序策略：hybrid (relevance first, published_at tie-break)

## 今日重点

- 主题「Language Model」：命中 1 篇，覆盖 PubMed AI，代表论文包括 《Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.》。

## 主题聚焦

### Language Model

- 命中篇数：1
- 覆盖分组：PubMed AI
- 代表论文：《Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.》
- 主题速读：
  - 《Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.》〔数据 / 方法〕：We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods inc…

## LLM 观察

今日没有新的命中文献。

## Vision 观察

今日没有新的命中文献。

## PubMed AI 观察

### 本组速览

- 《Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.》〔数据 / 方法〕：We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods inc…

### 论文速览

1. [Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.](https://pubmed.ncbi.nlm.nih.gov/41965370/)
   - Entered：2026-04-12 07:04
   - 作者：H Zhao，C Velez，A Naravane，A Saha，J Feldman，J Skolnick 等
   - 来源：pubmed
   - 相关性分数：48
   - 命中原因：summary matched "language model"; has DOI; has rich summary; has complete metadata
   - 分类：Journal Article
   - 标签：数据 / 方法
   - 主题词：Language Model
   - 摘要：We report on the integration of three methods that predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy, which uses a protein language model, and ZEPPI, which uses evolutionary information to evaluate protein-protein interfaces. Testing on the high-quality HINT database of binary PPIs reveals that the integrated method has better performance and identifies more high-confidence interactions than any of the component methods. The AF3Complex algorithm is used to predict the structures of 374 PPIs with a large fraction having at least partially overlapping interfaces with PrePPI models of the same complex. Clustering of the high-confidence E. coli interactome yields 385 subnetworks which have high functional coherence. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail.

## OpenAlex AI 观察

今日没有新的命中文献。