Biology subjects

Sorzano, C. O. S.

Publications and source records attributed to Sorzano, C. O. S..

18 recordsLinked to original sources

bioRxiv2026

BCell-Epitope-Prediction: A local, reproducible pipeline for multi-epitope vaccine design: integration and structural validation against a panel of PDB antigen-antibody complexes

Epitope-based subunit vaccine design requires chaining together heterogeneous analyses: B-cell antigenicity, topological accessibility, self-tolerance, allergenicity, T-helper/T-cytotoxic immunogenicity, and literature corroboration, historically scattered across separate, often web-only tools. This work presents a computational pipeline that integrates fourteen external tools across eight main phases, runs locally without network calls, and uses content-based self-caching. B-cell antigenicity is assessed via four engines (BepiPred-3.0, EpiDope, DiscoTope-3.0, and ScanNet), whose per-residue scores are fused into a single signal before thresholding. Validation used a panel of seventeen real antigen-antibody complexes from the Protein Data Bank spanning five pathogenic species, with ground truth from structural contact and independent literature verification. Fusing the four engines' scores, completed to a fixed candidate count, outperforms the best individual engine in F1 while reducing predicted residue volume. T-helper and T-cytotoxic immunogenicity are evaluated over each antigen's complete sequence, independently of the B-cell candidates. The final construct retains a B-cell epitope in 13 of 17 structures, and a T-cell block in 13 of 17 antigens, of which 6 match literature-documented epitopes and 2 more show partial correspondence.

bioinformatics↗

bioRxiv2026

Ab initio CryoEM maps minimizing preferred orientation problems in heterogeneous mixtures: ReconSIREN

Cryo-Electron Microscopy (CryoEM) Single Particle Analysis (SPA) is pivotal for elucidating protein structures and their conformational dynamics. Several critical steps are required to obtain accurate maps. First, the \textit{ab initio} relative orientations and shifts between images must be determined. Second, the common issue of specimens presenting a preferred orientation should be adequately addressed. Third, all of the above must account for particle flexibility and sample heterogeneity. Additionally, these steps are interrelated, making the search for all these parameters a real challenge when considering convergence robustness. In this context, we present ReconSIREN, a novel deep learning framework that addresses the key steps outlined before in an \textit{ab initio} manner while greatly minimizing one of the most important practical burdens in SPA, namely the errors introduced in the CryoEM maps by the existence of preferred orientations; the latter is achieved by a change of the optimization approach that avoids the so-called ``attraction problem'', and by the introduction of a new type of geometrical priors. It does so fully \textit{ab initio}, using deep learning training/inference steps without resorting to more traditional optimization approaches, and generating highly accurate initial reconstructions. We present maps as both homogeneous (consensus) maps and a full conformational landscape for analyzing flexibility and heterogeneity. Thanks to its Mixture-of-Experts (MoE) architecture, ReconSIREN distributes the estimation workload across independent experts, improving optimization efficiency and simplifying the process.

bioinformatics↗

bioRxiv2026

AlignPCA-2D: PCA-Reduced Euclidean Vector Alignment for 2D Classification in Cryo-EM

Cryogenic Electron Microscopy (cryo-EM) has transformed structural biology by enabling high-resolution reconstruction of macromolecular complexes from noisy projection images. However, the intrinsic heterogeneity and low signal-to-noise ratio of cryo-EM datasets make 2D classification a critical and computationally demanding step in the processing work-flow. Here, we introduce AlignPCA-2D, a PCA-space Euclidean vector alignment method for fast, interpretable 2D classification in cryo-EM. By projecting particle images and class representations into a compressed latent PCA space, AlignPCA-2D reduces data dimensionality while pre-serving meaningful structural variability. The image-to-class assignment is then performed using Euclidean distance, enabling efficient and accurate classification. We benchmark AlignPCA-2D against established cryo-EM software, such as RELION and cryoSPARC, and demonstrate that it achieves competitive alignment accuracy while substantially reducing computational cost. This approach provides a lightweight alternative for large-scale 2D classification tasks, and its modular design makes it compatible with existing cryo-EM processing pipelines. O_TBL View this table: org.highwire.dtl.DTLVardef@1e384org.highwire.dtl.DTLVardef@2474org.highwire.dtl.DTLVardef@15966c1org.highwire.dtl.DTLVardef@6934adorg.highwire.dtl.DTLVardef@1017b4e_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 2.C_FLOATNO O_TABLECAPTIONParticle retention and overlap among 2D classification methods C_TABLECAPTION C_TBL

biophysics↗

bioRxiv2025

The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

biophysics↗

bioRxiv2025

How many (distinguishable) classes can we identify in Single Particle Analysis?

Heterogeneity in cryoEM is essential for capturing macromolecule structural variability, reflecting their functional states and biological significance. However, estimating heterogeneity remains challenging due to particle misclassification and algorithmic biases, which can lead to reconstructions that blend distinct conformations or fail to resolve subtle differences. Furthermore, the low signal-to-noise ratio (SNR) inherent in cryo-EM data makes it nearly impossible to detect minute structural changes, as noise often obscures subtle variations in macromolecular projections. In this paper, we investigate the use of p-values associated with the null hypothesis that the observed classification differs from a random partition of the input dataset, thereby providing a statistical framework for determining the number of distinguishable classes present in a given dataset.

molecular biology↗

bioRxiv2025

Merging conformational landscapes in a single consensus space with FlexConsensus algorithm

The analysis of structural heterogeneity in CryoEM is experimenting a significant breakthrough toward estimating more accurate, richer, and interpretable conformational landscapes directly derived from experimental data. The increasing number of new methods designed to tackle the heterogeneity challenge reflects these new paradigms, allowing users to understand protein dynamics better. However, a question remains on how the estimation of different heterogeneity algorithms compare, which is essential to properly determine the reliability, stability, and correctness of the conformational landscapes and the structural transitions extracted from the CryoEM images. The intrinsic differences arising from the approximation and implementation of every heterogeneity algorithm make the definition of a consensus complex, a problem that remains unsolved. To overcome the challenges of comparing heterogeneity algorithms, we introduce our new FlexConsenus algorithm in this work. FlexConsensus relies on a multi-autoencoder architecture to learn the commonalities and differences of several conformational landscapes, allowing one to place them in a shared consensus space with enhanced reliability. In addition, FlexConsensus enables the derivation of error metrics from the consensus space to measure the reproducibility of the heterogeneity estimations. Thanks to the previous consensus metrics, it is possible to clean the original particle datasets further based on the differences in the estimation of their structural variability among different algorithms and/or different runs, allowing users to focus their analysis only on those particles with a stable estimation of their structural variability.

bioinformatics↗

bioRxiv2024

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the images signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation and L1 denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

bioinformatics↗

bioRxiv2023

Unraveling the Power of NAP-CNB's Machine Learning-enhanced Tumor Neoantigen Prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNAseq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in vivo following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA-identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies

immunology↗

bioRxiv2022

Growth hormone remodels the 3D-structure of the mitochondria of inflammatory macrophages and promotes metabolic reprogramming

Macrophages are a heterogeneous population of innate immune cells that support tissue homeostasis through their involvement in tissue development and repair, and pathogen defense. Emerging data reveal that metabolism may control macrophage polarization and function and, conversely, phenotypic polarization may drive metabolic reprogramming. Here, using biochemical analysis, correlative cryogenic fluorescence microscopy and cryo-focused ion-beam scanning electron microscopy, we demonstrate that growth hormone (GH) functions as a metabolic modulator to reprogram inflammatory GM-CSF-primed monocyte-derived macrophages (GM-MO). We found that exogenous treatment of GM-MO with recombinant human GH suppressed glycolysis, lactate production and non-mitochondrial respiration, and enhanced mitochondrial oxidative phosphorylation. Likewise, GH treatment augmented mitochondrial volume and altered mitochondrial dynamics, including the remodeling of the inner membrane to increase the density of cristae. Our data demonstrate that GH likely serves a modulatory role in the metabolism of inflammatory macrophages and suggest that metabolic reprogramming of macrophages should be considered a new target to intervene in multiple inflammatory diseases.

immunology↗

bioRxiv2022

Automatic determination of the handedness of Single-Particle maps of macromolecules solved by CryoEM

Single-Particle Analysis by Cryo-Electron Microscopy is a well-established technique to elucidate the three-dimensional (3D) structure of biological macromolecules. The orientation of the acquired projection images must be initially estimated without any reference to the final structure. In this step, algorithms may find a mirrored version of all the orientations resulting in a mirrored 3D map. It is as compatible with the acquired images as its unmirrored version from the image processing point of view, only that it is not biologically plausible. In this article, we introduce HaPi (Handedness Pipeline), the first method to automatically determine the hand of electron density maps of macromolecules solved by CryoEM. HaPi is built by training two 3D convolutional neural networks. The first determines -helices in a map, and the second determines whether the -helix is left-handed or right-handed. A consensus strategy defines the overall map hand. The pipeline is trained on simulated and experimental data. The handedness can be detected only for maps whose resolution is better than 5[A]. HaPi can identify the hand in 89% of new simulated maps correctly. Moreover, we evaluated all the maps deposited at the Electron Microscopy Data Bank and 11 structures uploaded with the incorrect hand were identified.

bioinformatics↗

bioRxiv2020

NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

Lack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope immunogenicity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the systems predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.

bioinformatics↗

bioRxiv2020

DeepEMhacer: a deep learning solution for cryo-EM volume post-processing

Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to obtain much cleaner and more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.

bioinformatics↗

bioRxiv2020

Improvements on marker-free images alignment for electron tomography

Electron tomography is a technique to obtain three-dimensional structural information of samples. However, the technique is limited by shifts occurring during acquisition that need to be corrected before the reconstruction process. In 2009, we proposed an approach for post-acquisition alignment of tilt series images. This approach was marker-free, based on patch tracking and integrated in free software. Here, we present improvements to the method to make it more reliable, stable and accurate. In addition, we modified the image formation model underlying the alignment procedure to include different deformations occurring during acquisition. We propose a new way to correct these computed deformations to obtain reconstructions with reduced artifacts. The new approach has demonstrated to improve the quality of the final 3D reconstruction, giving access to better defined structures for different transmission electron tomography methods: resin embedded STEM-tomography and cryo-TEM tomography. The method is freely available in TomoJ software.

molecular biology↗

bioRxiv2020

FSC-Q: A CryoEM map-to-atomic model quality validation based on the local Fourier Shell Correlation

In recent years, advances in cryoEM have dramatically increased the resolution of Coulomb potential maps and, with it, the number of solved atomic models. It is widely accepted that the quality of cryoEM maps varies locally; therefore, the evaluation of the maps-derived structural models must be done locally as well. In this article, a method for the local analysis of the map-to-model fit is presented. The algorithm uses a comparison of two local resolution maps. The first is the local FSC (Fourier shell correlation) between the full map and the model, while the second is calculated between the half maps normally used in typical single particle analysis workflows. We call the new quality measure "FSC-Q", and it is a quantitative estimation of how much of the model is supported by the signal content of the map. Furthermore, we show that FSC-Q may be helpful to avoid overfitting. It can be used to complement other methods, such as the Q-score method that estimates the resolvability of atoms.

biophysics↗

bioRxiv2019

MicrographCleaner: a python package for cryo-EM micrograph segmentation using deep learning

Cryo-EM Single Particle Analysis workflows require from tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hurdling the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate which regions of micrographs are suitable for particle picking and which are not in an automatic fashion. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on about one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.

bioinformatics↗

bioRxiv2018

Automatic local resolution-based sharpening of cryo-EM maps.

Recent technological advances and computational developments, have allowed the reconstruction of cryo-EM maps at near-atomic resolution structures. Cryo-EM maps benefit significantly of a \"postprocessing\" step, normally referred to as \"sharpening\", that tends to increase signal at medium/high resolution. Here, we propose a new method for local sharpening of volumes generated by cryo-EM. The algorithm (LocalDeblur) is based on a local resolution-guided Wiener restoration approach, does not need any prior atomic model and it avoids artificial structure 1 factor corrections. LocalDeblur is fully automatic and parameter free. We show that the new method significantly and quantitatively improving map quality and interpretability, especially in cases of broad local resolution changes (as is often the case of membrane proteins).

biophysics↗

bioRxiv2018

ENRICH: a fast method to improve the quality of flexible macromolecular reconstructions

Cryo-electron microscopy using single particle analysis requires the computational averaging of thousands of projection images captured from identical macromolecules. However, macromolecules usually present some degree of flexibility showing different conformations. Computational approaches are then required to classify heterogeneous single particle images into homogeneous sets corresponding to different structural states. Nonetheless, sometimes the attainable resolution of reconstructions obtained from these smaller homogeneous sets is compromised because of reduced number of particles or lack of images at certain macromolecular orientations. In these situations, the current solution to improve map resolution is returning to the electron microscope and collect more data. In this work, we present a fast approach to partially overcome this limitation for heterogeneous data sets. Our method is based on deforming and then moving particles between different conformations using an optical flow approach. Particles are then merged into a unique conformation obtaining reconstructions with improved resolution, contrast and signal-to-noise ratio, then, partially circumventing many issues that impact obtaining high quality reconstructions from small data sets. We present experimental results that show clear improvements in the quality of obtained 3D maps, however, there are also limits to this approach, which we discuss in the manuscript.

bioengineering↗

bioRxiv2016

Quantitative analysis of 3D alignment quality: itsimpact on soft-validation, particle pruning andhomogeneity analysis

Single Particle Analysis using cryo-electron microscopy is a structural biology technique to capture the three-dimensional conformation of biological macromolecules. The projection images used to construct the 3D density map are characterized by a very low signal-to-noise ratio to minimize radiation damage in the samples. As a consequence, the 3D alignment process is a challenging and error prone task and this job usually determines the success or failure of the macromolecule reconstruction. In this work, we present a soft-alignment validation approach, which can quantify the alignment precision and accuracy as well as the data homogeneity of the single particles when they are confronted with the resultant reconstructed 3DEM map. We have also applied this method to data homogeneity analysis and particle pruning, improving the data quality and as a consequence the final map resolution.

molecular biology↗