Biology subjects

Jimenez-Garcia, B.

Publications and source records attributed to Jimenez-Garcia, B..

7 recordsLinked to original sources

bioRxiv2025

HADDOCK3: A modular and versatile platform for integrative modelling of biomolecular complexes

HADDOCK is a widely used resource for integrative modelling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we introduce HADDOCK3, the new modular version of the program, in which the original, parameterisable albeit rigid pipeline has been first broken down in a catalogue of independent modules and then enriched with powerful analysis tools and third-party integrations. Thanks to this increased flexibility, HADDOCK3 can now handle multiple integrative modelling scenarios, providing a valuable, physics-based tool to enrich and complement the predictions made by machine learning algorithms in the post-AlphaFold era. We present examples of successful applications of HADDOCK3 that were not feasible with the previous versions of HADDOCK, highlighting its expanded capabilities. The HADDOCK3 software source code is freely available from the GitHub repository (https://github.com/haddocking/haddock3) and comes with an online user guide (www.bonvinlab.org/haddock3-user-manual). All example data described in this manuscript are available at https://github.com/haddocking/haddock3-paper-data.

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bioRxiv2024

Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

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bioRxiv2020

Integrative Modeling of Membrane-associated Protein Assemblies

Historically, membrane protein systems have been considered as one of the most challenging systems to study with experimental structural biology techniques. Over the past years, increased number of experimental structures of membrane proteins have become available thanks in particular to advances in solid-state NMR spectroscopy and cryo-electron microscopy. This has opened the route to modeling the complexes that those membrane proteins form by methods such as docking. Most approaches developed to date are, however, not capable of incorporating the topological information provided by the membrane into the modeling process. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies, specifically complexes consisting of a membrane-embedded protein and a soluble partner. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We make use of an equilibrated coarse-grained lipid bilayer to represent the information encoded in the membrane in the form of artificial beads, which allows to target the docking towards the binding-competent regions. We demonstrate the performance of this membrane-driven protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we evaluate how different membrane definitions impact the performance of the docking protocol and provide a comparison, in terms of success rate, to another state-of-the-art docking software, ZDOCK. Finally, we discuss the quality of the generated models and propose possible future developments. Our membrane docking protocol should allow to shed light on the still rather dark fraction of the interactome consisting of membrane proteins.

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bioRxiv2020

proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

Monoclonal antibodies (mAbs) are essential tools in the contemporary therapeutic armoury. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalysing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes. Here we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK. The proABC-2 server is freely available at: https://bianca.science.uu.nl/proabc2/.

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bioRxiv2019

MuGVRE. A virtual research environment for 3D/4D genomics

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available at https://vre.multiscalegenomics.eu and documentation and general information at https://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

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bioRxiv2019

LightDock goes information-driven

The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces. We present a novel two-step method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein-Protein Docking Benchmark 5, using different amounts of information. Even with incomplete information, a significant improvement in performance is obtained compared to blind ab initio docking.\n\nThe software is supported and freely available from https://github.com/brianjimenez/lightdock and analysis data from https://github.com/brianjimenez/lightdock_bm5.

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bioRxiv2018

SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

MotivationUnderstanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.\n\nResultsWe present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish detectable binding.\n\nAvailabilityThe database is available at https://life.bsc.es/pid/skempi2/

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