AiPD

AiPD is a database of autoinhibited proteins and their cis-regulatory elements (autoinhibitory domains)

 Autoinhibition, a crucial allosteric self-regulation mechanism in cell signaling, ensures signal propagation exclusively in the presence of specific molecular inputs. The heightened focus on autoinhibited proteins stems from their implication in human diseases, positioning them as potential causal factors or therapeutic targets. However, the absence of a comprehensive knowledgebase impedes a thorough understanding of their roles and applications in drug discovery. Addressing this gap, we introduce AiPD, a curated database standardizing information on autoinhibited proteins. AiPD encompasses details on autoinhibitory elements (AiEs), their targets, regulatory mechanisms, experimental validation methods, and implications in diseases, including associated mutations and post-translational modifications. AiPD comprises 578 AiEs from 441 experimentally characterized autoinhibited proteins and 738 AiEs from their 583 homologs, which were retrieved from 754 published articles. AiPD also includes 42,840 AiEs of computationally predicted autoinhibited proteins. In addition, AiPD facilitates users in investigating potential AiEs within a query sequence through comparisons with documented autoinhibited proteins. As the inaugural autoinhibited protein repository, AiPD significantly aids researchers studying autoinhibition mechanisms and their alterations in human diseases. It is equally valuable for developing computational models, analyzing allosteric protein regulation, predicting new drug targets, and understanding intervention mechanisms. AiPD serves as a valuable resource for diverse researchers, contributing to the understanding and manipulation of autoinhibition in cellular processes. AiPD is freely accessible at http://aipd.lile.bio.

Citation: Autoinhibited Protein Database: a curated database of autoinhibitory domains and their autoinhibition mechanisms

Daeahn Cho, Hyang-Mi Lee, Ji Ah Kim, Jae Gwang Song, Su-hee Hwang, Bomi Lee, Jinsil Park, Kha Mong Tran, Jiwon Kim, Phuong Ngoc Lam Vo, Jooeun Bae, Teerapat Pimt, Kangseok Lee, Jörg Gsponer*, Hyung Wook Kim*, and Dokyun Na* (2024).

The Journal of Biological Databases and Curation, 2024, baae085.

PubMed: 39192607 DOI: 10.1093/database/baae085