Structural Characterization of Membrane Protein Dimers

Preto, António; Matos-Filipe, Pedro; Koukos, Panagiotis I.; Renault, Pedro; Sousa, Sérgio F.; Moreira, Irina S.

doi:10.1007/978-1-4939-9161-7_21

Cited by 1 publication

(1 citation statement)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The available Python code allows users to perform feature extraction using a pre-processed PDB file easily. For detailed information on all the pre-processing steps (trimming of non-TM residues, removal of heteroatoms, mutation of exotic residues, modelling of incomplete structures and dimer extraction from the structure files), please see Preto et al ( 37 ). The addition of hydrogens was implemented within the pipeline available in the GitHub repository.…”

Section: Introductionmentioning

confidence: 99%

MENSAdb: a thorough structural analysis of membrane protein dimers

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%