Protein Secondary Structure Patterns in Short‐Range Cross‐Link Atlas

Abstract Cross‐linking mass spectrometry (XL‐MS) has become a powerful tool in structural biology for investigating protein structure, dynamics, and interactomics. However, short‐range cross‐links, defined as those connecting residues fewer than 20 positions apart, have traditionally been considered less informative and largely overlooked, leaving significant data unexplored in a systematic manner. Here, we present a system‐wide analysis of short‐range cross‐links, demonstrating their intrinsic correlation with protein secondary structure. We introduce the X‐SPAN (Cross‐link Structural Pattern Analyzer) software, which integrates publicly available XL‐MS datasets from system‐wide experiments with AlphaFold‐predicted protein structures. Our analysis reveals distinct cross‐linking patterns that reflect the spatial constraints imposed by secondary structural elements. Specifically, α‐helices exhibit periodic cross‐linking patterns consistent with their characteristic helical pitch, whereas coils and β‐strands display nearly monotonic distributions. A context‐dependent protein grammar reinforces short‐range cross‐link specificity. Short‐range cross‐links can enhance the statistical inference of secondary structures within integrative modeling workflows. Additionally, our work establishes a framework for benchmarking AlphaFold's local prediction accuracy and provides novel quality control criteria for XL‐MS experiments. We anticipate that X‐SPAN and our short‐range cross‐link database will serve as a valuable resource for exploring local secondary structure rearrangements and their potential roles in protein function and allosteric regulation.

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Publication Details

Journal
Angewandte Chemie
Published
2025-05-28
DOI
https://doi.org/10.1002/ange.202507348
Primary Topic
Machine Learning in Bioinformatics
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article
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article

Protein Secondary Structure Patterns in Short‐Range Cross‐Link Atlas

Alessio Di Ianni, Claudio Iacobucci, Alice Vetrano, Samantha Reale et al.
Angewandte Chemie
Machine Learning in Bioinformatics
article

Protein Secondary Structure Patterns in Short‐Range Cross‐Link Atlas

Alessio Di Ianni, Claudio Iacobucci, Alice Vetrano, Samantha Reale, Isabella Daidone, Gianluca Dell’Orletta, Nico Di Fonte
article en

Abstract

Abstract Cross‐linking mass spectrometry (XL‐MS) has become a powerful tool in structural biology for investigating protein structure, dynamics, and interactomics. However, short‐range cross‐links, defined as those connecting residues fewer than 20 positions apart, have traditionally been considered less informative and largely overlooked, leaving significant data unexplored in a systematic manner. Here, we present a system‐wide analysis of short‐range cross‐links, demonstrating their intrinsic correlation with protein secondary structure. We introduce the X‐SPAN (Cross‐link Structural Pattern Analyzer) software, which integrates publicly available XL‐MS datasets from system‐wide experiments with AlphaFold‐predicted protein structures. Our analysis reveals distinct cross‐linking patterns that reflect the spatial constraints imposed by secondary structural elements. Specifically, α‐helices exhibit periodic cross‐linking patterns consistent with their characteristic helical pitch, whereas coils and β‐strands display nearly monotonic distributions. A context‐dependent protein grammar reinforces short‐range cross‐link specificity. Short‐range cross‐links can enhance the statistical inference of secondary structures within integrative modeling workflows. Additionally, our work establishes a framework for benchmarking AlphaFold's local prediction accuracy and provides novel quality control criteria for XL‐MS experiments. We anticipate that X‐SPAN and our short‐range cross‐link database will serve as a valuable resource for exploring local secondary structure rearrangements and their potential roles in protein function and allosteric regulation.

Angewandte ChemieVol. 137(31)
University of L'Aquila (IT), Zero to Three (US)
Openalex Percentile: Top 89%
Machine Learning in Bioinformatics
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