MFIB 2.0: a major update of the database of protein complexes formed by mutual folding of the constituting protein chains

While the majority of proteins with available structures are able to fold independently and mediate interactions only after acquiring their folded state, a subset of the known protein complexes contains protein chains that are intrinsically disordered in isolation. The Mutual Folding Induced by Binding (MFIB) database collects and classifies protein complexes, wherein all constituent protein chains would be unstable/disordered in isolation but fold into a well-defined 3D complex structure upon binding. This phenomenon is often termed as cooperative folding and binding or mutual synergistic folding (MSF). Here we present a major update to the database: we collected and annotated hundreds of new protein complexes fulfilling the criteria of MSF, leading to an almost six-fold increase in the size of the database. Many novel features have also been introduced, such as clustering of the complexes based on structural similarity and domain types, assigning different evidence levels to each entry and adding the evidence coverage label that allowed us to include complexes of multi(sub)domain monomers with partial MSF. The MFIB 2.0 database is available at https://mfib.pbrg.hu.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2024-11-11
DOI
https://doi.org/10.1093/nar/gkae976
Citations
7
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
1.64

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MFIB 2.0: a major update of the database of protein complexes formed by mutual folding of the constituting protein chains

Zsófia E. Kálmán, Erzsébet Fichó, Csaba Magyar, Gábor Tusnády et al.
7 citations
Nucleic Acids Research
Protein Structure and Dynamics
1.64
article

MFIB 2.0: a major update of the database of protein complexes formed by mutual folding of the constituting protein chains

Zsófia E. Kálmán, Erzsébet Fichó, Csaba Magyar, Gábor Tusnády, Éva Schád, Rita Pancsa, Bálint Zoltán Németh, István Simon, László Dobson
article en
7 citations

Abstract

While the majority of proteins with available structures are able to fold independently and mediate interactions only after acquiring their folded state, a subset of the known protein complexes contains protein chains that are intrinsically disordered in isolation. The Mutual Folding Induced by Binding (MFIB) database collects and classifies protein complexes, wherein all constituent protein chains would be unstable/disordered in isolation but fold into a well-defined 3D complex structure upon binding. This phenomenon is often termed as cooperative folding and binding or mutual synergistic folding (MSF). Here we present a major update to the database: we collected and annotated hundreds of new protein complexes fulfilling the criteria of MSF, leading to an almost six-fold increase in the size of the database. Many novel features have also been introduced, such as clustering of the complexes based on structural similarity and domain types, assigning different evidence levels to each entry and adding the evidence coverage label that allowed us to include complexes of multi(sub)domain monomers with partial MSF. The MFIB 2.0 database is available at https://mfib.pbrg.hu.

Nucleic Acids ResearchVol. 53(D1)
Semmelweis University (HU), Pázmány Péter Catholic University (HU), HUN-REN Research Centre for Natural Sciences (HU)
Grundy Educational Trust, Magyar Tudományos Akadémia, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
Openalex Percentile: Top 16%
Protein Structure and Dynamics
1.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.