Biology subjects

Zea, D. J.

Publications and source records attributed to Zea, D. J..

9 recordsLinked to original sources

bioRxiv2026

AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformations

Proteins are dynamic molecules capable of adopting multiple conformations. However, AlphaFold2 predominantly generates models around a single conformation, usually representing a ligand-bound state. To address this limitation, we developed AlphaConformers, a structure-guided pipeline that steers AlphaFold2 toward alternative conformations. It is based on the idea that protein structure databases can capture the structural space accessible to members of a protein family. Given a target protein, AlphaConformers retrieves structures from structurally similar proteins. These structures are organized into structure-based alignments and template sets, which are supplied to AlphaFold2 as conformational hypotheses. The resulting models are clustered and filtered, facilitating their analysis. Evaluated on a curated benchmark of 88 proteins with known ligand-bound and unbound conformations, AlphaConformers expanded AlphaFold2 conformational sampling and recovered alternative states missed by AlphaFold2 and other state-of-the-art methods. AlphaConformers ranked first for modelling subtle conformational changes commonly observed between ligand-bound and unbound states. These results show that structural information from protein databases can be leveraged to steer AlphaFold2 toward alternative conformations.

bioinformatics↗

bioRxiv2026

PDZD-8 interacts with LGG-2/LC3 to promote autolysosome formation

LGG-2/LC3 and LGG-1/GABARAP are members of the Atg8 ubiquitin-like protein family, playing central roles in autophagy. Through a yeast two-hybrid screen, we identified the endoplasmic reticulum protein PDZD-8 as a novel interactor of the C. elegans autophagy protein LGG-2/LC3. We demonstrate that PDZD-8 binds specifically to LGG-2 and not LGG-1 via a canonical LC3-interacting region (LIR) and colocalizes with LGG-2-positive autophagosomes in vivo. Loss of PDZD-8 leads to a transient accumulation of enlarged LGG-2-positive autophagosomes that remain closely associated with lysosomes. Genetic disruption of core autophagy genes suppresses the pdzd-8 mutant phenotype, indicating that this phenotype is dependent on autophagy. Moreover, loss of rab-7, which blocks autophagosome-lysosome fusion, also suppresses the pdzd-8 phenotype, suggesting that the RAB-7-dependent fusion step is epistatic to PDZD-8 and that PDZD-8 acts downstream of autophagosome-lysosome maturation. Microscopy approaches (TEM, CLEM, and FIB-SEM) reveal an accumulation of fused autophagosome-lysosome structures, pointing to a transient block in autophagic flux in pdzd-8 mutants. Furthermore, PDZD-8 lacking either the SMP lipid-transfer domain or the LIR motif phenocopies the pdzd-8 null mutant, underscoring the importance of both domains. Our findings support a model in which PDZD-8, in conjunction with LGG-2/LC3, mediates ER-autophagosome/autolysosome contacts that are critical for subsequent autolysosome completion. Significance StatementAutophagy is a conserved cellular degradation pathway essential for maintaining homeostasis and responding to stress. Although the molecular machinery driving autophagosome formation and fusion with lysosomes is relatively well-characterized, the contribution of membrane contact sites to autophagic flux remains poorly understood. Here, we identify PDZD-8, an endoplasmic reticulum (ER) protein, as a novel interactor of the Caenorhabditis elegans LC3/Atg8 homolog LGG-2. We show that PDZD-8 regulates the completion of autolysosome maturation by bridging ER-autophagosome/autolysosome contacts. Loss of PDZD-8 results in the accumulation of enlarged autophagosomes adjacent to lysosomes and a transient block in autophagic flux, a phenotype that depends on the RAB-7-mediated tethering step. These findings reveal a previously unrecognized role for PDZD-8 in coordinating ER-autophagosome/autolysosome dynamics during autophagy.

cell biology↗

bioRxiv2023

Charting the alternative usage of protein repeated regions in evolution and its implication for protein interactions

Alternative splicing of repeats in proteins provides a mechanism for rewiring and fine-tuning protein interaction networks. In this work, we developed a robust and versatile method, ASPRING, to identify alternatively spliced protein repeats from gene annotations. ASPRING leverages evolutionary meaningful alternative splicing-aware hierarchical graphs to provide maps between protein repeats sequences and 3D structures. We re-think the definition of repeats by explicitly accounting for transcript diversity across several genes/species. Using a stringent sequence-based similarity criterion, we detected over 5,000 evolutionary conserved repeats by screening virtually all human protein-coding genes and their orthologs across a dozen species. Through a joint analysis of their sequences and structures, we extracted specificity-determining sequence signatures and assessed their implication in experimentally resolved and modelled protein interactions. Our findings demonstrate the widespread alternative usage of protein repeats in modulating protein interactions and open avenues for targeting repeat-mediated interactions. HighlightsO_LIRobust detection of alternatively used repeated protein regions in evolution C_LIO_LIThe approach relies on sequence similarity and identifies conserved signatures C_LIO_LIMapping of the repeats onto protein isoform 3D models predicted by AlphaFold C_LIO_LI5 000 repeats detected over the human coding fraction, about one third disordered C_LIO_LIAssessment of the structural coverage of their interactions with protein partners C_LI

bioinformatics↗

bioRxiv2021

Impact of protein conformational diversity on AlphaFold predictions

After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here we address the performance of AlphaFold2 predictions under this ensemble paradigm. Using a curated collection of apo-holo conformations, we found that AlphaFold2 predicts the holo form of a protein in 70% of the cases, being unable to reproduce the observed conformational diversity with an equivalent error than in the estimation of a single conformation. More importantly, we found that AlphaFold2s performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions.

bioinformatics↗

bioRxiv2020

Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs

Understanding how protein function has evolved and diversified is of great importance for human genetics and medicine. Here, we tackle the problem of describing the whole transcript variability observed in several species by generalising the definition of splicing graph. We provide a practical solution to building parsimonious evolutionary splicing graphs where each node is a minimal transcript building block defined across species. We show a clear link between the functional relevance, tissue-regulation and conservation of AS events on a set of 50 genes. By scaling up to the whole human protein-coding genome, we identify a few thousands of genes where alternative splicing modulates the number and composition of pseudo-repeats. We have implemented our approach in ThorAxe, an efficient, versatile, and robust computational tool freely available at https://github.com/PhyloSofS-Team/thoraxe. The results are accessible and can be browsed interactively at http://www.lcqb.upmc.fr/ThorAxe.

bioinformatics↗

bioRxiv2020

Intrinsically disordered protein ensembles shape evolutionary rates revealing conformational patterns

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs. Significance StatementIntrinsically disordered proteins (IDPs) challenge the structure-function relationship paradigm. In this work we found that individual sites of IDPs evolve under a strong rate heterogeneity, mainly due to the structural constraints imposed by contacts between their residues. This can be better explained if the contacts are taken from selected subsets of their alternative native conformations, rather than from individual conformations or the whole native ensemble. From an evolutionary point of view, this result indicates that experimentally-based ensembles are redundant. We also observed that the evolutionary rates follow the structural variability between conformers, unveiling conformational preferences. Our results set the stage for establishing novel evolutionary-based methods to study IDP ensembles.

bioinformatics↗

bioRxiv2017

How is structural divergence related to evolutionary information?

Conservation and covariation measures, as other evolutionary analysis, require a high number of distant homologous sequences, therefore a lot of structural divergence can be expected in such divergent alignments. However, most works linking evolutionary and structural information use a single structure ignoring the structural variability inside a protein family. That common practice seems unrealistic to the light of this work.\n\nIn this work we studied how structural divergence affects conservation and covariation estimations. We uncover that, within a protein family, ~51% of multiple sequence alignment columns change their exposed/buried status between structures. Also, ~53% of residue pairs that are in contact in one structure are not in contact in another structure from the same family. We found out that residue conservation is not directly related to the relative solvent accessible surface area of a single protein structure. Using information from all the available structures rather than from a single representative structure gives more confidence in the structural interpretation of the evolutionary signals. That is particularly important for diverse multiple sequence alignments, where structures can drastically differ. High covariation scores tend to indicate residue contacts that are conserved in the family, therefore, are not suitable to find protein/conformer specific contacts.\n\nOur results suggest that structural divergence should be considered for a better understanding of protein function, to transfer annotation by homology and to model protein evolution.

bioinformatics↗

bioRxiv2017

Co-mutation and exclusion analysis in human tumors, a means for cancer biology studies and treatment design

BackgroundMalignant tumors originate from genomic and epigenomic alterations, which lead to loss of control of the cellular circuitry. These alterations relate with each other in patterns of mutual exclusion and co-occurrence that affect prognosis and treatment response and highlight the need for multitargeted therapy. However, to the best of our knowledge, there are no systematic reports in the literature of co-dependent and mutually exclusive mutations across all types of cancer. In addition, the studies reported so far generally deal with whole genes instead of specific mutations, ignoring the fact that different alterations in the same gene can have widely different effects.\n\nResultsHere we present a systematic analysis of co-dependencies of somatic mutations across all cancer types. Combining multi testing with conditional and expected mutational probabilities, we have found pairs and networks of co-mutations and exclusions, some of them in particular types of cancer and others widespread. We have also determined that driver loci are present in more types of cancer than non driver loci, that they tend to pair within a single gene and that those pairs are more often exclusions than co-mutations.\n\nConclusionsBased on this properties, we propose new drivers that warrant experimental validation. Our analysis is potentially relevant for cancer biology and classification, as well as for the rational selection of multitargeted therapeutic approaches.

cancer biology↗

bioRxiv2017

Homology Modeling In A Dynamical World

A key concept in template-based modeling is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair Template-based modelling (TMB) results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.\n\nAuthor summaryTemplate-based modelling (TBM) is the most reliable and fastest approach to obtain protein structural models. TBM relies in the high correlation between sequence and structural divergence, with the practical consequence that proteins that are similar at the sequence level will also be similar at the structural level, allowing in this way the selection of the better template to obtain the 3D model of the target sequence. However, protein native state could be described by a collection of conformers in equilibrium where their structural differences are called conformational diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We firstly found that the extent of conformational diversity can be as high as the maximum structural differences reached by families differing in their sequences. In these proteins with higher conformational diversity levels, the well-established correlation between sequence and structural divergence is nosier than previously suggested due to the presence of structural change without sequence variation. This lack of correlation could impair TBM results due to the uncertainty in the correct template selection. Finally, we also found that the presence of order/disorder can provide useful beforehand information for better TBM performance.

bioinformatics↗