Biology subjects

Di Ianni, A.

Publications and source records attributed to Di Ianni, A..

4 recordsLinked to original sources

bioRxiv2025

Protein Secondary Structure Patterns In Short-Range Cross-Link Atlas

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, -helices exhibit periodic cross-linking patterns consistent with their characteristic helical pitch, whereas coils and {beta}-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 AlphaFolds 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.

molecular biology↗

bioRxiv2024

Evaluating Cross-linking-driven integrative modeling in peptide-HLAII complexes prediction with insights for refining predictive accuracy

ABSTRACTIn silico prediction of peptide-HLAII (human leucocyte antigen class II) complexes has emerged as a crucial approach in bioinformatics for deciphering antigen presentation mechanisms. Several in silico tools have been developed to predict peptide binding to HLAII alleles, trying to deconvolute the intricate peptide-HLAII binding specificity. These approaches integrate bases from molecular modeling, machine learning, and bioinformatics to predict peptide-HLAII interactions. Initially, structure-based methods relying on molecular docking algorithms were widespread, utilizing structural data of HLAII molecules and peptides to infer plausible binding conformations. These methods often faced challenges in accuracy due to the dynamic nature of peptide-HLAII interactions. Besides, the high flexibility of peptide sidechains makes their placement into the HLA-binding site even more complex. In recent years, machine learning techniques have drawn attention to peptide-HLAII binding predictions. Supervised learning algorithms, such as support vector machines (SVMs), neural networks, and ensemble methods, have been considerably applied to discriminate patterns from large datasets of experimentally validated peptide-HLAII binding affinities (like Immune Epitope Data Base, IEDB) and more recently mass spectrometry- eluted ligands from MHC-associated peptide proteomics (MAPPs) assay. The role of experiment- assisted integrative modeling in aiding peptide-HLAII complexes prediction still needs to be clarified. In this work, we benchmarked the use of AlphaLink2 (AlphaFold2 + cross-links restraints) and compared it to AlphaFold2 Multimer in predicting correct peptide binding motifs. These results can pave the way to an integrated strategy for vaccine development and protein deimmunization or autoimmunity mitigation.

immunology↗

bioRxiv2024

Twisting Urea- to Imide-Based Mass Spectrometry-Cleavable Cross-Linkers Enables Affinity Tagging

Disuccinimidyl dibutyric urea (DSBU) is a mass spectrometry (MS)-cleavable cross-linker that has multiple applications in structural biology, ranging from isolated protein complexes to comprehensive system-wide interactomics. DSBU facilitates a rapid and reliable identification of cross-links through the dissociation of its urea group in the gas-phase. In this study, we further advance the structural capabilities of DSBU by twisting the urea group into an imide, thus introducing a novel class of cross-linkers. This modification preserves the MS-cleavability of the amide bond, granted by the two acyl groups of the imide function. The central nitrogen atom enables the introduction of affinity purification tags. Here, we introduce disuccinimidyl disuccinic imide (DSSI) as prototype of this class of cross-linkers. It features a phosphonate handle for immobilized metal ion affinity chromatography (IMAC) enrichment. We detail DSSI synthesis and describe its behavior in solution and in the gas-phase while cross-linking isolated proteins and human cell lysates. DSSI and DSBU cross-links are compared at the same enrichment depths to bridge these two cross-linker classes. We validate DSSI cross-links by mapping them in high-resolution structures of large protein assemblies. The cross-links observed yield insights into the morphology of intrinsically disordered proteins (IDPs) and their complexes. The DSSI linker might spearhead a novel class of MS-cleavable and enrichable cross-linkers.

biochemistry↗

bioRxiv2022

Structural Assessment of the Full-Length Wild-Type Tumor Suppressor Protein p53 by Mass Spectrometry-Guided Computational Modeling

The tetrameric tumor suppressor p53 represents a great challenge for 3D-structural analysis due to its high degree of intrinsic disorder (ca. 40%). We aim to shed light on the structural and functional roles of p53s C-terminal region in full-length, wild-type human p53 tetramer and their importance for DNA binding. For this, we employed complementary techniques of structural mass spectrometry (MS) in an integrated approach with AI-based computational modeling. Our results show no major conformational differences in p53 between DNA-bound and DNA-free states, but reveal a substantial compaction of p53s C-terminal region. This supports the proposed mechanism of unspecific DNA binding to the C-terminal region of p53 prior to transcription initiation by specific DNA binding to the core domain of p53. The synergies between complementary structural MS techniques and computational modeling as pursued in our integrative approach is envisioned to serve as general strategy for studying intrinsically disordered proteins (IDPs) and intrinsically disordered region (IDRs).

biochemistry↗