PubMed Health⌕ Search

PubMed · 1303743

Time-resolved protein crystallography.

Abstract

Advances in synchrotron radiation technology have allowed exposure times from protein crystals of the order of milliseconds to be used routinely, and in exceptional circumstances exposure times of 100 ps have been obtained. However, many data sets take seconds to record because of the slow time scale of film change or crystal reorientation or translation when more than one exposure is required. This problem has been addressed by Amemiya et al. (1989). There has been considerable progress in methods to initiate reactions in protein crystals, especially the development of photolabile caged compounds but also temperature jump, pH jump, and diffusion. Although flash lamps deliver pulses of 100 mJ/ms, often several pulses are required to release sufficient product, and reaction initiation can take several seconds. Laser illumination can provide more powerful input, but the laser must be accommodated within the restricted space at the synchrotron station. The requirement to maintain synchrony among the molecules in the crystal lattice as the reaction proceeds and to ensure that the lifetime of intermediates is longer than data collection rates emphasizes the need for chemical characterization of the reaction under study. As Ringe advocated in the studies with chymotrypsin, it may be more profitable to devise conditions under which certain intermediates along the reaction pathway accumulate in the crystal and to record these in a series of discrete steps rather than continuous monitoring of the reaction. The Laue method is limited to those proteins that give well-ordered crystals and problems of transient disorder on initiation of reaction and problems of radiation damage need to be overcome or avoided by suitable experimental protocols.(ABSTRACT TRUNCATED AT 250 WORDS)

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L N Johnson. 1992. Time-resolved protein crystallography.. https://doi.org/10.1002/pro.5560011002

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.

Predicting accurate protein structures is essential for understanding molecular mechanisms, interpreting the impact of sequence variation, and supporting translational applications ranging from drug discovery to clinical genomics. Recent advances in deep-learning-based predictors such as AlphaFold2, OpenFold, and AlphaFold3 have transformed structural biology, enabling routine in silico modeling even for challenging or previously uncharacterized proteins. However, systematic benchmarking of these tools-especially for novel targets and single amino acid variants-remains limited. Conventional global metrics often fail to capture biologically meaningful discrepancies. By evaluating multiple implementations of AlphaFold2 and OpenFold, together with ColabFold and the AlphaFold3 server, across 10 different proteins and 222 single amino acid protein variants encompassing a wide range of sizes, structures, and functions, we show that although widely used global indicators-like mean pLDDT, pTM-score, and RMSD-frequently suggest comparable performance, substantial local-level differences remain elusive. To address this gap, we introduce a comparative framework leveraging Bland-Altman agreement analysis, to evaluate per-residue Cα-confidence differences and Per-Residue profiles (PRPs), complemented by Uniform Manifold Approximation and Projection (UMAP). This approach reveals marked localized divergences, particularly within flexible or intrinsically disordered regions, where both predictor choice and single-residue substitutions trigger the largest conformational shifts. We further demonstrate that using reduced homology databases has minimal impact on predicted structural quality, offering computationally efficient alternatives. Collectively, our findings underscore the importance of integrating global and residue-specific evaluations to more accurately assess robustness, agreement, and practical usability across contemporary protein structure prediction methods.

Proteins↗

Dynamic Protein Structure Paradox: An Integrative Framework for Endpoint-Conditioned Evidentiary Sufficiency in Structure-to-Function Claims.

Accurate coordinates for a represented protein state do not, by themselves, establish activity or any other condition-specific function. This article defines the Dynamic Protein Structure Paradox (DPSP) as the apparent conflict between structural accuracy and functional underdetermination and develops it as an integrative evidentiary assessment framework rather than a new theory or paradigm. The underlying problem has been longstanding, since structural genomics, function annotation, allostery, and disorder research each established that fold does not determine function and that function does not determine fold. DPSP consolidates those results into one endpoint-conditioned rule. Once a measurable endpoint is defined, it assesses four coupled dimensions: relevant-state completeness, context completeness, ensemble or kinetic dependence, and chemical dependence. A rubric rates each dimension as adequate, uncertain, or missing, and a materiality test determines which gaps influence the stated decision. The outcome is one of three mutually exclusive modes of utilization: geometry-led, conditional, or function-measured. The deliverable is a concise evidence statement delineating what the structure supports, which decisive variable remains unmeasured, and what corroboration is necessary. DPSP complements, rather than replaces, existing structural, ensemble, and computational approaches. The framework remains unvalidated, its thresholds are provisional, and the studies necessary to confirm or refute it are specified.

Proteins↗

The Lipid Interactome: an interactive and open access platform for exploring cellular lipid-protein interactions.

SUMMARY: Lipid-protein interactions play essential roles in cellular signaling and membrane dynamics, yet their systematic characterization has long been hindered by the inherent biochemical properties of lipids. Recent advances in functionalized lipid probes-equipped with photoactivatable crosslinkers, affinity handles, and photocleavable protecting groups-have enabled proteomics-based identification of lipid interacting proteins with unprecedented specificity and resolution. Despite the growing number of published lipid interactomes, there remains no centralized effort to harmonize, compare, or integrate these datasets. The Lipid Interactome addresses this gap by providing a structured, interactive web portal that adheres to FAIR data principles-ensuring that lipid interactome studies are Findable, Accessible, Interoperable, and Reusable. Through standardized data formatting, interactive visualizations, and direct cross-study comparisons, this resource enables researchers to systematically explore the protein-binding partners of diverse bioactive lipids. By consolidating and curating lipid interactome proteomics data from multiple studies, the Lipid Interactome database serves as a critical tool for deciphering the biological functions of lipids in cellularsystems. AVAILABILITY AND IMPLEMENTATION: This site can be viewed at LipidInteractome.org. All data are available for download. No user information is collected or necessary for data navigation, interaction, or download.

Proteins↗