排名前五的药物类型 | 数量 |
---|---|
双特异性抗体 | 3 |
ADC | 2 |
合成多肽 | 1 |
治疗用放射药物 | 1 |
抗体偶联核素 | 1 |
靶点 |
作用机制 GLP-1R拮抗剂 |
在研机构 |
原研机构 |
非在研适应症- |
最高研发阶段临床1/2期 |
首次获批国家/地区- |
首次获批日期- |
靶点 |
作用机制 SSTR激动剂 |
在研机构 |
原研机构 |
在研适应症 |
非在研适应症- |
最高研发阶段临床1期 |
首次获批国家/地区- |
首次获批日期- |
靶点- |
作用机制- |
在研机构 |
原研机构 |
在研适应症 |
非在研适应症- |
最高研发阶段临床前 |
首次获批国家/地区- |
首次获批日期- |
开始日期2024-07-09 |
申办/合作机构 |
开始日期2024-06-18 |
申办/合作机构 |
Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspotID, a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We show that PPI-hotspotID outperformed FTMap and SPOTONE, the only available webservers for predicting PPI hotspots given free protein structures and sequences, respectively. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-HotspotID, yielded better performance than either method alone. Furthermore, we experimentally verified the PPI-hot spots of eukaryotic elongation factor 2 predicted by PPI-hotspotID. Notably, PPI-hotspotID unveils PPI-hot spots that are not obvious from complex structures, which only reveal interface residues, thus overlooking PPI-hot spots in indirect contact with binding partners. Thus, PPI-hotspotID serves as a valuable tool for understanding the mechanisms of PPIs and facilitating the design of novel drugs targeting these interactions. A freely accessible web server is available at https://ppihotspotid.limlab.dnsalias.org/ and the source code for PPI-hotspotID at https://github.com/wrigjz/ppihotspotid/.