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Massimiliano Bonomi

Publications and source records attributed to Massimiliano Bonomi.

12 recordsLinked to original sources

Making PLUMED fly: a tutorial on optimizing performance

PLUMED is an open-source software package that is widely used for analyzing and enhancing molecular dynamics simulations that works in conjunction with most available molecular dynamics softwares. While the computational cost of PLUMED calculations is typically negligible compared to the molecular dynamics code's force evaluation, the software is increasingly being employed for more computationally demanding tasks where performance optimization becomes critical. In this tutorial, we describe a recently implemented tool that can be used to reliably measure code performance. We then use this tool to generate detailed performance benchmarks that show how calculations of large-numbers of distances, angles or torsions can be optimized by using vector-based commands rather than individual scalar operations. We then present benchmarks that illustrate how to optimize calculations of atomic order parameters and secondary structure variables. Throughout the tutorial and in our implementations we endeavor to explain the algorithmic tricks that are being used to optimize the calculations so others can make use of these prescriptions both when they are using PLUMED and when they are writing their own codes.

physics.comp-ph↗

From Possibility to Precision in Macromolecular Ensemble Prediction

Proteins and other macromolecules exist not in a single state but as dynamic ensembles of interconverting conformations, which are essential for catalysis, allosteric regulation, and molecular recognition. While AI-based structure predictors like AlphaFold have revolutionized static structure prediction, they are not yet capable of capturing conformational ensembles. Progress towards the next generation of AI models capable of ensemble prediction is currently limited by the lack of accurate, high-resolution ground truth ensembles at the scale required for training and validation. This is due to the fact that no single experimental technique can fully resolve the atomistic complexity of conformational landscapes, and fundamental challenges remain in defining, representing, comparing, and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and how to leverage these new methodologies to build benchmarks and establish ensemble-specific validation protocols. Finally, we discuss how ensemble predictions will be an interactive cycle of experimental and computational innovation. Establishing this ecosystem will allow structural biology to move beyond static snapshots toward a dynamic understanding of molecular behavior that captures the full complexity of biological systems.

q-bio.BM↗

The need to implement FAIR principles in biomolecular simulations

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/) Now published as: Amaro, R.E., et al. The need to implement FAIR principles in biomolecular simulations. Nat Methods (2025) https://doi.org/10.1038/s41592-025-02635-0

q-bio.BM↗

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

physics.ed-ph↗

RNA Dynamics from Experimental and Computational Approaches

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.

q-bio.BM↗

Bonds and Bytes: the Odyssey of Structural Biology

Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of Structural Biology, no single method comprehensively reveals the behavior of biological systems across various spatio-temporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative Structural Biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in Structural Biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatio-temporal modeling, and provides an outlook into future directions of this field.

q-bio.QM↗

Multi-replica biased sampling for photoswitchable pi-conjugated polymers

In recent years, pi-conjugated polymers are attracting considerable interest in view of their light-dependent torsional reorganization around the pi-conjugated backbone, which determines peculiar light-emitting properties. Motivated by the interest in designing conjugated polymers with tunable photoswitchable pathways, we devised a computational framework to enhance the sampling of the torsional conformational space and at the same time estimate ground to excited-state free-energy differences. This scheme is based on a combination of Hamiltonian Replica Exchange (REM), Parallel Bias metadynamics, and free-energy perturbation theory. In our scheme, each REM replica samples an intermediate unphysical state between the ground and the first two excited states, which are characterized by TD-DFT simulations at the B3LYP/6-31G* level of theory. We applied the method to a 5-mer of 9,9-dioctylfluorene and found that upon irradiation this system can undergo a dihedral inversion from 155 to -155 degrees crossing a barrier that decreases from 0.1 eV in the ground state (S0) to 0.05 eV and 0.04 eV in the first (S1) and second (S2) excited states. Furthermore, S1 and even more S2 were predicted to stabilize coplanar dihedrals, with a local free-energy minimum located at +-44 degrees. The presence of a free-energy barrier of 0.08 eV for the S1 and 0.12 eV for the S2 state can trap this conformation in a basin far from the global free-energy minimum located at 155 degrees. The simulation results were compared with the experimental emission spectrum, showing a quantitative agreement with the predictions provided by our framework.

physics.chem-ph↗

A practical guide to the simultaneous determination of protein structure and dynamics using metainference

Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.

q-bio.QM↗

Determination of protein structural ensembles using cryo-electron microscopy

Achieving a comprehensive understanding of the behaviour of proteins is greatly facilitated by the knowledge of their structures, thermodynamics and dynamics. All this information can be provided in an effective manner in terms of structural ensembles. A structural ensemble can be obtained by determining the structures, populations and interconversion rates for all the main states that a protein can occupy. To achieve this goal, integrative methods that combine experimental and computational approaches provide powerful tools. Here we focus on cryo-electron microscopy, which has become over recent years an invaluable resource to bridge the gap from order to disorder in structural biology. In this review, we provide a perspective of the current challenges and opportunities in determining protein structural ensembles using integrative approaches that can combine cryo-electron microscopy data with other available sources of information, along with an overview of the tools available to the community.

q-bio.QM↗

Metainference: A Bayesian Inference Method for Heterogeneous Systems

Modelling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model, and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system populates simultaneously an ensemble of different states and experimental data are measured as averages over such states. To address this problem we present a Bayesian inference method, called metainference, that is able to deal with errors in experimental measurements as well as with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modelling based on the maximum entropy principle. To illustrate the method we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to model complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.

physics.comp-ph↗

PLUMED 2: New feathers for an old bird

Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardwares, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here - a complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field.

physics.comp-ph↗