MENSAdb: a thorough structural analysis of membrane protein dimers

Author:

Matos-Filipe Pedro1ORCID,Preto António J12,Koukos Panagiotis I3,Mourão Joana1ORCID,Bonvin Alexandre M J J3ORCID,Moreira Irina S45ORCID

Affiliation:

1. Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal

2. PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research, University of Coimbra, Coimbra, 3030-789, Portugal

3. Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands

4. Department of Life Sciences, University of Coimbra, Coimbra, 3000-456, Portugal

5. Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal

Abstract

Abstract Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration–approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π–π stacking, T-stacking and cation–π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

European Regional Development Fund

Fundação para a Ciência e a Tecnologia

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MPA‐Pred: A machine learning approach for predicting the binding affinity of membrane protein–protein complexes;Proteins: Structure, Function, and Bioinformatics;2023-11-10

2. MUG: A mutation overview of GPCR subfamily A17 receptors;Computational and Structural Biotechnology Journal;2023

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