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1. Molecular Dynamics to Predict Cryo-EM: Capturing Transitions and Short-Lived Conformational States of Biomolecules

   https://doi.org/10.3389/fmolb.2021.641208  

   Nierzwicki, Łukasz; Palermo, Giulia (April 2021, Frontiers in Molecular Biosciences)

   null (Ed.)

   Single-particle cryogenic electron microscopy (cryo-EM) has revolutionized the field of the structural biology, providing an access to the atomic resolution structures of large biomolecular complexes in their near-native environment. Today’s cryo-EM maps can frequently reach the atomic-level resolution, while often containing a range of resolutions, with conformationally variable regions obtained at 6 Å or worse. Low resolution density maps obtained for protein flexible domains, as well as the ensemble of coexisting conformational states arising from cryo-EM, poses new challenges and opportunities for Molecular Dynamics (MD) simulations. With the ability to describe the biomolecular dynamics at the atomic level, MD can extend the capabilities of cryo-EM, capturing the conformational variability and predicting biologically relevant short-lived conformational states. Here, we report about the state-of-the-art MD procedures that are currently used to refine, reconstruct and interpret cryo-EM maps. We show the capability of MD to predict short-lived conformational states, finding remarkable confirmation by cryo-EM structures subsequently solved. This has been the case of the CRISPR-Cas9 genome editing machinery, whose catalytically active structure has been predicted through both long-time scale MD and enhanced sampling techniques 2 years earlier than cryo-EM. In summary, this contribution remarks the ability of MD to complement cryo-EM, describing conformational landscapes and relating structural transitions to function, ultimately discerning relevant short-lived conformational states and providing mechanistic knowledge of biological function. 

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2. Graph theory approaches for molecular dynamics simulations

   https://doi.org/10.1017/S0033583524000143  

   Patel, Amun C; Sinha, Souvik; Palermo, Giulia (January 2024, Quarterly Reviews of Biophysics)

   Abstract Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems. 

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3. Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets

   https://doi.org/10.1021/acs.jctc.3c00040  

   Kleiman, Diego E.; Shukla, Diwakar (April 2023, Journal of Chemical Theory and Computation)

   Rapid computational exploration of the free energy landscape of biological molecules remains an active area of research due to the difficulty of sampling rare state transitions in molecular dynamics (MD) simulations. In recent years, an increasing number of studies have exploited machine learning (ML) models to enhance and analyze MD simulations. Notably, unsupervised models that extract kinetic information from a set of parallel trajectories have been proposed including the variational approach for Markov processes (VAMP), VAMPNets, and time-lagged variational autoencoders (TVAE). In this work, we propose a combination of adaptive sampling with active learning of kinetic models to accelerate the discovery of the conformational landscape of biomolecules. In particular, we introduce and compare several techniques that combine kinetic models with two adaptive sampling regimes (least counts and multiagent reinforcement learning- based adaptive sampling) to enhance the exploration of conformational ensembles without introducing biasing forces. Moreover, inspired by the active learning approach of uncertainty-based sampling, we also present MaxEnt VAMPNet. This technique consists of restarting simulations from the microstates that maximize the Shannon entropy of a VAMPNet trained to perform the soft discretization of metastable states. By running simulations on two test systems, the WLALL pentapeptide and the villin headpiece subdomain, we empirically demonstrate that MaxEnt VAMPNet results in faster exploration of conformational landscapes compared with the baseline and other proposed methods. 

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4. Molecular Dynamics–Based Thermodynamic and Kinetic Characterization of Membrane Protein Conformational Transitions

   https://doi.org/10.1007/978-1-0716-1394-8_16  

   Ogden, Dylan; Moradi, Mahmoud (April 2021, Methods in molecular biology)

   Schmidt-Krey, Ingeborg; Gumbart, James C. (Ed.)

   Molecular dynamics (MD) simulations are routinely used to study structural dynamics of membrane proteins. However, conventional MD is often unable to sample functionally important conformational transitions of membrane proteins such as those involved in active membrane transport or channel activation process. Here we describe a combination of multiple MD based techniques that allows for a rigorous characterization of energetics and kinetics of large-scale conformational changes in membrane proteins. The methodology is based on biased, nonequilibrium, collective-variable based simulations including nonequilibrium pulling, string method with swarms of trajectories, bias-exchange umbrella sampling, and rate estimation techniques. 

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5. Gaussian accelerated molecular dynamics: Principles and applications

   https://doi.org/10.1002/wcms.1521  

   Wang, Jinan; Arantes, Pablo R.; Bhattarai, Apurba; Hsu, Rohaine V.; Pawnikar, Shristi; Huang, Yu‐ming M.; Palermo, Giulia; Miao, Yinglong (March 2021, WIREs Computational Molecular Science)

   Abstract Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., “Gaussian approximation”). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica‐exchange GaMD (rex‐GaMD) and replica‐exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new “selective GaMD” algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep‐GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small‐molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein–protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design. This article is categorized under:Structure and Mechanism > Computational Biochemistry and BiophysicsMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsMolecular and Statistical Mechanics > Free Energy Methods 

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