Biology subjects

Cazzaniga, A.

Publications and source records attributed to Cazzaniga, A..

9 recordsLinked to original sources

bioRxiv2026

Tumour evolution as ground truth for cancer whole-genome sequencing

Cancer genomes are shaped by evolutionary processes that couple mutagenesis, clonal selection, chromosomal instability, spatial growth and treatment response into structured genomic patterns, yet current benchmarking strategies largely ignore this evolutionary dependency. Here, we present SCOUT, a large-scale synthetic whole-genome sequencing resource of over 200 samples, designed for systematic benchmarking of tumour genomic analysis and evolutionary inference under controlled evolutionary ground truth. Unlike conventional task-specific simulations, SCOUT models tumour evolution as a latent generative process that simultaneously shapes mutations, copy-number alterations, variant allele frequencies, mutational signatures and clonal architectures. SCOUT recapitulates key features of solid and haematological malignancies, including driver mutations, chromosomal instability, intratumour heterogeneity, spatial sampling and treatment-associated evolutionary dynamics in tumour and matched-normal longitudinal and multi-region sequencing designs. Using SCOUT, we benchmarked widely used methods for somatic variant detection, copy-number analysis, mutational signature inference and tumour evolutionary reconstruction. Across analytical tasks, performance deteriorated in low-purity, highly subclonal and structurally complex tumours, while spatial sampling bias and hypermutation generated spurious evolutionary signals that confounded tumour interpretation across multiple inference layers. Evolutionary simulations further distinguished lineage-restricted genetic bottlenecks from multi-lineage resistance dynamics associated with tumour plasticity. Tumour purity consistently exerted a stronger effect on inference accuracy than sequencing depth. Together, our results establish evolutionary ground truth as a prerequisite for reproducible benchmarking and biologically interpretable analysis of cancer whole-genome sequencing data.

bioinformatics↗

bioRxiv2025

Evolutionary constraints guide AlphaFold2 prediction of alternative conformations and inform rational mutation design

Investigating structural variability is essential for understanding protein biological functions. Although AlphaFold2 accurately predicts static structures, it fails to capture the full spectrum of functional states. Recent methods have used AlphaFold2 to generate diverse structural ensembles, but they offer limited interpretability and overlook the evolutionary signals underlying predictions. In this work, we enhance the generation of conformational ensembles and identify sequence patterns that influence alternative fold predictions for several protein families. Building on prior research that clustered Multiple Sequence Alignments to predict fold-switching states, we introduce a refined clustering strategy that integrates protein language model representations with hierarchical clustering, overcoming limitations of density-based methods. Our strategy effectively identifies high-confidence alternative conformations and generates abundant sequence ensembles, providing a robust framework for applying Direct Coupling Analysis (DCA). Through DCA, we uncover key coevolutionary signals within the clustered alignments, leveraging them to design mutations that stabilize specific conformations, which we validate using alchemical free energy calculations from molecular dynamics. Notably, our method extends beyond fold-switching, effectively capturing a variety of conformational changes.

bioinformatics↗

bioRxiv2024

Unsupervised domain classification of AlphaFold2-predicted protein structures

AO_SCPLOWBSTRACTC_SCPLOWThe release of the AlphaFold database, which contains 214 million predicted protein structures, represents a major leap forward for proteomics and its applications. However, lack of comprehensive protein annotation limits its accessibility and usability. Here, we present DPCstruct, an unsupervised clustering algorithm designed to provide domain-level classification of protein structures. Using structural predictions from AlphaFold2 and comprehensive all-against-all local alignments from Foldseek, DPCstruct identifies and groups recurrent structural motifs into domain clusters. When applied to the Foldseek Cluster database, a representative set of proteins from the AlphaFoldDB, DPCstruct successfully recovers the majority of protein folds catalogued in established databases such as SCOP and CATH. Out of the 28,246 clusters identified by DPCstruct, 24% have no structural or sequence similarity to known protein families. Supported by a modular and efficient implementation, classifying 15 million entries in less than 48 hours, DPCstruct is well suited for large-scale proteomics and metagenomics applications. It also facilitates the rapid incorporation of updates from the latest structural prediction tools, ensuring that the classification remains up-to-date. The DPCstruct pipeline and associated database are freely available in a dedicated repository, enhancing the navigation of the AlphaFoldDB through domain annotations and enabling rapid classification of other protein datasets.

bioinformatics↗

bioRxiv2024

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting. We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. Code and data are available at https://github.com/RitAreaSciencePark/PLM4Muts.

bioinformatics↗

bioRxiv2023

Protein family annotation for the Unified Human Gastrointestinal Proteome by DPCfam clustering

Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the lack of sequence annotation impairs its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. It is our hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut by the release of a FAIR-compliant database easily accessible via a searchable web server and Zenodo repository.

bioinformatics↗

bioRxiv2022

The geometry of hidden representations of protein language models

Protein language models (pLMs) transform their input into a sequence of hidden representations whose geometric behavior changes across layers. Looking at fundamental geometric properties such as the intrinsic dimension and the neighbor composition of these representations, we observe that these changes highlight a pattern characterized by three distinct phases. This phenomenon emerges across many models trained on diverse datasets, thus revealing a general computational strategy learned by pLMs to reconstruct missing parts of the data. These analyses show the existence of low-dimensional maps that encode evolutionary and biological properties such as remote homology and structural information. Our geometric approach sets the foundations for future systematic attempts to understand the space of protein sequences with representation learning techniques.

bioinformatics↗

bioRxiv2022

Adversarial Attacks on Protein Language Models

Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically insignificant perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are often insensitive to biologically relevant mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3d structures.

bioinformatics↗

bioRxiv2022

The different effect of pharmacological or low-doses of IFN-γ in endothelial cells are mediated by different intracellular signaling pathways

Interferon (IFN)-{gamma} is a proinflammatory cytokine with a crucial role in intercellular communication during innate and acquired immune responses. IFN-{gamma} interacts with many cell types, among which endothelial cells. Here, we show that pharmacological and low-dose kinetically activated (SKA) IFN-{gamma} exert different effects on endothelial cells by activating different signal transduction pathways. Pharmacological concentrations of IFN-{gamma} activate JAK/STAT pathway, inducing the overexpression of the CDKN1A p21, which in turn inhibits cell growth. Conversely, low-dose SKA IFN-{gamma} does not activate the canonical JAK/STAT pathway but induces the phosphorylation of ERK. ERK activation is responsible for the induction of endothelial cell migration. Interestingly, ERK activation occurs only in the presence of kinetically activate low-dose IFN-{gamma}, underlying the importance of mechanical forces to potentiate IFN-{gamma} activity.

pharmacology and toxicology↗

bioRxiv2022

The presence of BBB hastens neuronal differentiation of cerebral organoids - the potential role of endothelial derived BDNF

Despite remaining the best in vitro model to resemble the human brain, a weakness of human cerebral organoids is the lack of the endothelial component that in vivo organizes in the blood brain barrier (BBB). Since the BBB is crucial to control the microenvironment of the nervous system, this study proposes a co-culture BBB and cerebral organoids. We utilized a BBB model consisting of primary brain microvascular endothelial cells and astrocytes in a transwell system. Starting from induced Pluripotent Stem Cells (iPSCs) we generated human cerebral organoids which were then cultured in the absence or presence of an in vitro model of BBB. We evaluated if the presence of the BBB influences the maturation of cerebral organoids. By morphological analysis, it emerges that in the presence of the BBB the cerebral organoids are better organized than controls in the absence of the BBB. This effect seems to be driven by Brain Derived Neurotrophic Factor (BDNF), a neurotrophic factor released by the endothelial component of the BBB, which is involved in neurodevelopment, neuroplasticity and neurosurvival. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/501119v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@483c47org.highwire.dtl.DTLVardef@6d5976org.highwire.dtl.DTLVardef@10c5545org.highwire.dtl.DTLVardef@b2610b_HPS_FORMAT_FIGEXP M_FIG C_FIG The current culture model of human cerebral organoids does not require the presence of a BBB (left side). However, the BBB is an important source of BDNF, which is crucial for neurodevelopment and brain health. The cerebral organoids co-cultured for 4 days in the presence of the BBB show a higher cortical organization than the organoids cultured in the absence of the BBB, as illustrated on the right.

cell biology↗