Large-Scale Quantitative Cross-Linking and Mass Spectrometry Provides New Insight on Protein Conformational Plasticity within Organelles, Cells, and Tissues DOI Creative Commons
Andrew Keller, Anna Bakhtina, James E. Bruce

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Many proteins can exist in multiple conformational states vivo to achieve distinct functional roles. These include alternative conformations, variable PTMs, and association with interacting protein, nucleotide, ligand partners. Quantitative chemical cross-linking of live cells, organelles, or tissues together mass spectrometry provides the relative abundance cross-link levels formed two more compared samples, which depends both on existent protein samples as well likelihood originating from each. Because state preferences vary widely, one expects intra-protein high plasticity display divergent quantitation among differing ensembles. Here we use large volume quantitative data available public XLinkDB database cluster cross-links according their many diverse provide first widescale glimpse grouped state(s) they predominantly originate. We further demonstrate how be aligned any structure assess that were derived it.

Language: Английский

Large-Scale Quantitative Cross-Linking and Mass Spectrometry Provide New Insight into Protein Conformational Plasticity within Organelles, Cells, and Tissues DOI
Andrew Keller, Anna Bakhtina, James E. Bruce

et al.

Journal of Proteome Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Many proteins can exist in multiple conformational states vivo to achieve distinct functional roles. These include alternative conformations, variable post-translational modifications (PTMs), and associations with interacting protein, nucleotide, ligand partners. Quantitative chemical cross-linking of live cells, organelles, or tissues together mass spectrometry provides the relative abundance cross-link levels formed two more compared samples, which depends both on existent protein samples likelihood originating from each. Because state preferences vary widely, one expects intraprotein high plasticity display divergent quantitation among differing ensembles. Here we use large volume quantitative data available public XLinkDB database cluster cross-links according their many diverse provide first widescale glimpse grouped state(s) they predominantly originate. We further demonstrate how be aligned any structure assess that were derived it.

Language: Английский

Citations

0
EnsembleFlex: Protein Structure Ensemble Analysis Made Easy DOI
Melanie Schneider,

José Antonio Márquez,

Andrew R. Leach

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
PDBe tools for an in‐depth analysis of small molecules in the Protein Data Bank DOI Creative Commons
Preeti Choudhary, Ibrahim Roshan Kunnakkattu, Sreenath Nair

et al.

Protein Science, Journal Year: 2025, Volume and Issue: 34(4)

Published: March 18, 2025

Abstract The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, nucleic acids. PDB contains over 47,000 unique small molecules bound to macromolecules. Despite extensive data available, complexity small‐molecule in necessitates specialized tools effective analysis visualization. PDBe has developed a number tools, including CCDUtils ( https://github.com/PDBeurope/ccdutils ) accessing enriching ligand data, Arpeggio https://github.com/PDBeurope/arpeggio analyzing interactions between ligands macromolecules, RelLig https://github.com/PDBeurope/rellig identifying functional roles (such as reactants, cofactors, or drug‐like molecules) within protein–ligand complexes. enhanced annotations generated by these are presented on novel PDBe‐KB pages, offering comprehensive overview providing valuable insights into contexts (example page Imatinib: https://pdbe.org/chem/sti ). By improving standardization identification, adding various annotations, advanced visualization capabilities, help researchers navigate complexities systems, facilitating mechanistic understanding functions. ongoing enhancements resources designed support scientific community gaining applications across fields, drug discovery, molecular biology, systems structural pharmacology.

Language: Английский

Citations

0
Quantum Mechanics Paradox in Protein Structure Prediction: Intrinsically Linked to Sequence yet Independent of it DOI Creative Commons
Sarfaraz K. Niazi

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100039 - 100039

Published: April 1, 2025

Language: Английский

Citations

0
Comparative evaluation of methods for the prediction of protein–ligand binding sites DOI Creative Commons
Javier S. Utgés, Geoffrey J. Barton

Journal of Cheminformatics, Journal Year: 2024, Volume and Issue: 16(1)

Published: Nov. 11, 2024

The accurate identification of protein-ligand binding sites is critical importance in understanding and modulating protein function. Accordingly, ligand site prediction has remained a research focus for over three decades with 50 methods developed change paradigm from geometry-based to machine learning. In this work, we collate 13 predictors, spanning 30 years, focusing on the latest learning-based such as VN-EGNN, IF-SitePred, GrASP, PUResNet, DeepPocket compare them established P2Rank, PRANK fpocket earlier like PocketFinder, Ligsite Surfnet. We benchmark against human subset our new curated reference dataset, LIGYSIS. LIGYSIS comprehensive complex dataset comprising 30,000 proteins bound ligands which aggregates biologically relevant unique interfaces across biological units multiple structures same protein. an improvement testing datasets sc-PDB, PDBbind, MOAD, COACH420 HOLO4K either include 1:1 complexes or consider asymmetric units. Re-scoring predictions by display highest recall (60%) whilst IF-SitePred presents lowest (39%). demonstrate detrimental effect that redundant performance well beneficial impact stronger pocket scoring schemes, improvements up 14% (IF-SitePred) 30% precision (Surfnet). Finally, propose top-N+2 universal metric urge authors share not only source code their methods, but also benchmark.Scientific contributionsThis study conducts largest date, comparing original 15 variants using 10 informative metrics. introduced, highlights demonstrates significant through schemes. proposed prediction, recommendation open-source sharing both benchmarks.

Language: Английский

Citations

1
Comparative evaluation of methods for the prediction of protein-ligand binding sites DOI Creative Commons
Javier S. Utgés, Geoffrey J. Barton

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 8, 2024

Abstract The accurate identification of protein-ligand binding sites is critical importance in understanding and modulating protein function. Accordingly, ligand site prediction has remained a research focus for over three decades with 50 methods developed since the early 1990s. Over this time, paradigm changed from geometry-based to machine learning. In work, we collate 11 predictors, spanning 30 years, focusing on latest learning-based such as VN-EGNN, IF-SitePred, GrASP, PUResNet, DeepPocket compare them established P2Rank or fpocket earlier like PocketFinder, Ligsite Surfnet. We benchmark against human subset new curated reference dataset, LIGYSIS. LIGYSIS comprehensive complex dataset comprising 30,000 proteins bound ligands which aggregates biologically relevant unique interfaces across biological units multiple structures same protein. an improvement testing datasets sc-PDB, PDBbind, MOAD, COACH420 HOLO4K either include 1:1 complexes consider asymmetric units. Re-scoring predictions by PRANK display highest recall (60%) whilst VN-EGNN (46%) IF-SitePred (39%) present lowest recall. demonstrate detrimental effect that redundant performance well beneficial impact stronger pocket scoring schemes, improvements up 14% (IF-SitePred) 30% precision (Surfnet). Methods predicting few pockets per protein, e.g., GrASP PUResNet are very precise (> 90%) but limited Finally, propose universal metric urge authors share not only source code their methods, also benchmark.

Language: Английский

Citations

0
PDBe tools for an in-depth analysis of small molecules in the Protein Data Bank DOI Creative Commons
Preeti Choudhary, Ibrahim Roshan Kunnakkattu, Sreenath Nair

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

Abstract The Protein Data Bank (PDB) is the primary global repository for experimentally determined 3D structures of biological macromolecules and their complexes with ligands, proteins, nucleic acids. PDB contains over 47,000 unique small molecules bound to macromolecules. Despite extensive data available, complexity molecule in necessitates specialised tools effective analysis visualisation. PDBe has developed a number tools, including CCDUtils ( https://github.com/PDBeurope/ccdutils ) accessing enriching ligand data, Arpeggio https://github.com/PDBeurope/arpeggio analysing interactions between ligands macromolecules, RelLig https://github.com/PDBeurope/rellig identifying functional roles (such as reactants, cofactors, or drug-like molecules) within protein-ligand complexes. Furthermore, enhanced annotations generated by these are presented comprehensive view on novel PDBe-KB pages, providing holistic that enables establishment contexts (Example page Imatinib: https://wwwdev.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/STI ). By improving standardisation identification, adding various annotations, offering advanced visualisation capabilities, help researchers navigate complexities systems, facilitating mechanistic understanding functions. ongoing enhancements resources designed support scientific community gaining valuable insights into applications across fields, drug discovery, molecular biology, systems structural pharmacology.

Language: Английский

Citations

0
Introduction to the Special Issue Tribute to Olga Kennard (1924–2023) DOI Creative Commons
John R. Helliwell

Structural Dynamics, Journal Year: 2024, Volume and Issue: 11(4)

Published: July 1, 2024

The Cambridge Structure Database, hosted at the Crystallographic Data Centre (CCDC), was instigated in 1965 by Olga Kennard thus implementing a vision first set out John Desmond Bernal that collection of crystal structures would open new insights, and knowledge, more than individual alone.In 2015 50th Anniversary celebration CCDC held Cambridge, Kennard's lecture 1 conveyed inspiration its achievements over decades.

Language: Английский

Citations

0
Large-Scale Quantitative Cross-Linking and Mass Spectrometry Provides New Insight on Protein Conformational Plasticity within Organelles, Cells, and Tissues DOI Creative Commons
Andrew Keller, Anna Bakhtina, James E. Bruce

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Many proteins can exist in multiple conformational states vivo to achieve distinct functional roles. These include alternative conformations, variable PTMs, and association with interacting protein, nucleotide, ligand partners. Quantitative chemical cross-linking of live cells, organelles, or tissues together mass spectrometry provides the relative abundance cross-link levels formed two more compared samples, which depends both on existent protein samples as well likelihood originating from each. Because state preferences vary widely, one expects intra-protein high plasticity display divergent quantitation among differing ensembles. Here we use large volume quantitative data available public XLinkDB database cluster cross-links according their many diverse provide first widescale glimpse grouped state(s) they predominantly originate. We further demonstrate how be aligned any structure assess that were derived it.

Language: Английский

Citations

0