Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Molecular mechanism of Mad2 conformational conversion promoted by the Mad2-interaction motif of Cdc20

During mitosis, unattached kinetochores trigger the spindle assembly checkpoint by promoting the assembly of the mitotic checkpoint complex, a heterotetramer comprising Mad2, Cdc20, BubR1, and Bub3. Critical to this process is the kinetochore-mediated catalysis of an intrinsically slow conformational conversion of Mad2 from an open (O-Mad2) inactive state to a closed (C-Mad2) active state bound to Cdc20. These Mad2 conformational changes involve substantial remodeling of the N-terminal β1 strand and C-terminal β7/β8 hairpin. In vitro, the Mad2-interaction motif (MIM) of Cdc20 (Cdc20MIM) triggers the rapid conversion of O-Mad2 to C-Mad2, effectively removing the kinetic barrier for MCC assembly. How Cdc20MIM directly induces Mad2 conversion remains unclear. In this study, we demonstrate that the Cdc20MIM-binding site is inaccessible in O-Mad2. Time-resolved NMR and molecular dynamics simulations show how Mad2 conversion involves sequential conformational changes of flexible structural elements in O-Mad2, orchestrated by Cdc20MIM. Conversion is initiated by the β7/β8 hairpin of O-Mad2 transiently unfolding to expose a nascent Cdc20MIM-binding site. Engagement of Cdc20MIM to this site promotes the release of the β1 strand. We propose that initial conformational changes of the β7/β8 hairpin allow binding of Cdc20MIM to a transient intermediate state of Mad2, thereby lowering the kinetic barrier to Mad2 conversion.

Scaling Graph Neural Networks to Large Proteins

Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting high accuracy in predicting complex energies and forces based on atomic identities and Cartesian coordinates. To expand the applicability of GNNs, and machine learning force fields more broadly, optimizing their computational efficiency is critical, especially for large biomolecular systems in classical molecular dynamics simulations. In this study, we address key challenges in existing GNN benchmarks by introducing a dataset, DISPEF, which comprises large, biologically relevant proteins. DISPEF includes 207,454 proteins with sizes up to 12,499 atoms and features diverse chemical environments, spanning folded and disordered regions. The implicit solvation free energies, used as training targets, represent a particularly challenging case due to their many-body nature, providing a stringent test for evaluating the expressiveness of machine learning models. We benchmark the performance of seven GNNs on DISPEF, emphasizing the importance of directly accounting for long-range interactions to enhance model transferability. Additionally, we present a novel multiscale architecture, termed Schake, which delivers transferable and computationally efficient energy and force predictions for large proteins. Our findings offer valuable insights and tools for advancing GNNs in protein modeling applications.

Application of modern artificial intelligence techniques in the development of organic molecular force fields

The molecular force field (FF) determines the accuracy of molecular dynamics (MD) and is one of the major bottlenecks that limits the application of MD in molecular design. Recently, artificial intelligence (AI) techniques, such as machine-learning potentials (MLPs), have been rapidly reshaping the landscape of MD. Meanwhile, organic molecular systems feature unique characteristics, and require more careful treatment in both model construction, optimization, and validation. While an accurate and generic organic molecular force field is still missing, significant progress has been made with the facilitation of AI, warranting a promising future. In this review, we provide an overview of the various types of AI techniques used in molecular FF development and discuss both the advantages and weaknesses of these methodologies. We show how AI methods provide unprecedented capabilities in many tasks such as potential fitting, atom typification, and automatic optimization. Meanwhile, it is also worth noting that more efforts are needed to improve the transferability of the model, develop a more comprehensive database, and establish more standardized validation procedures. With these discussions, we hope to inspire more efforts to solve the existing problems, eventually leading to the birth of next-generation generic organic FFs.

Fine-tuning molecular mechanics force fields to experimental free energy measurements

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data. In this study, we treat a pretrained GNN force field-here, espaloma-0.3.2-as a foundation simulation model and fine-tune its charge model using limited quantities of experimental hydration free energy data, with the goal of assessing the degree to which this can systematically improve the prediction of other related free energies. We demonstrate that a highly efficient "one-shot fine-tuning" method using an exponential (Zwanzig) reweighting free energy estimator can improve prediction accuracy without the need to resimulate molecular configurations. To achieve this "one-shot" improvement, we demonstrate the importance of using effective sample size (ESS) regularization strategies to retain good overlap between initial and fine-tuned force fields. Moreover, we show that leveraging low-rank projections of embedding vectors can achieve comparable accuracy improvements as higher-dimensional approaches in a variety of data-size regimes. Our results demonstrate that linearly-perturbative fine-tuning of foundation model electrostatic parameters to limited experimental data offers a cost-effective strategy that achieves state-of-the-art performance in predicting hydration free energies on the FreeSolv dataset.

Tuning Colloidal Reactions

The precise control of complex reactions is critical for biological processes, yet our inability to design for specific outcomes limits the development of synthetic analogs. Here, we leverage differentiable simulators to design nontrivial reaction pathways in colloidal assemblies. By optimizing over external structures, we achieve controlled disassembly and particle release from colloidal shells. Lastly, we characterize the role of configurational entropy in the structure via both forward calculations and optimization, inspiring new parameterizations of designed colloidal reactions.

Metal-Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information.

The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling

Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

We develop ∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently released HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

Elucidating the Interaction of Indole-3-Propionic Acid and Calf Thymus DNA: Multispectroscopic and Computational Modeling Approaches

Indole-3-propionic acid (IPA) is a plant growth regulator with good specificity and long action. IPA may be harmful to human health because of its accumulation in vegetables and fruits. Therefore, in this study, the properties of the interaction between calf thymus DNA (ctDNA) and IPA were systematically explored using multispectroscopic and computational modeling approaches. Analysis of fluorescence spectra showed that IPA binding to ctDNA to spontaneously form a complex was mainly driven by hydrogen bonds and hydrophobic interaction. DNA melting analysis, viscosity analysis, DNA cleavage study, and circular dichroism measurement revealed the groove binding of IPA to ctDNA and showed that the binding did not significantly change ctDNA confirmation. Furthermore, molecular docking found that IPA attached in the A-T rich minor groove region of the DNA. Molecular dynamics simulation showed that DNA and IPA formed a stable complex and IPA caused slight fluctuations for the residues at the binding site. Gel electrophoresis experiments showed that IPA did not significantly disrupt the DNA structure. These findings may provide useful information on the potential toxicological effects and environmental risk assessments of IPA residue in food at the molecular level.