SYNBIP: synthetic binding proteins for research, diagnosis and therapy

Author:

Wang Xiaona1,Li Fengcheng2,Qiu Wenqi3,Xu Binbin1,Li Yanlin1,Lian Xichen2,Yu Hongyan1,Zhang Zhao1,Wang Jianxin4,Li Zhaorong5,Xue Weiwei1ORCID,Zhu Feng125ORCID

Affiliation:

1. School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China

2. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China

3. Department of Surgery, HKU-SZH & Faculty of Medicine, The University of Hong Kong, Hong Kong, China

4. School of Computer Science and Engineering, Central South University, Changsha 410083, China

5. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China

Abstract

Abstract The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

Funder

Entrepreneurship and Innovation Support Plan for Chinese Overseas Students of Chongqing

Natural Science Foundation of Zhejiang Province

National Natural Science Foundation of China

Fundamental Research Fund for the Central Universities

Key R&D Program of Zhejiang Province

Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare

Alibaba Cloud

Information Technology Center of Zhejiang University

Publisher

Oxford University Press (OUP)

Subject

Genetics

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