Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Assessing the Martini 3 protein model: A review of its path and potential

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Polymersomes in Drug Delivery─From Experiment to Computational Modeling

Polymersomes, composed of amphiphilic block copolymers, are self-assembled vesicles that have gained attention as potential drug delivery systems due to their good biocompatibility, stability, and versatility. Various experimental techniques have been employed to characterize the self-assembly behaviors and properties of polymersomes. However, they have limitations in revealing molecular details and underlying mechanisms. Computational modeling techniques have emerged as powerful tools to complement experimental studies and enabled researchers to examine drug delivery mechanisms at molecular resolution. This review aims to provide a comprehensive overview of the state of the art in the field of polymersome-based drug delivery systems, with an emphasis on insights gained from both experimental and computational studies. Specifically, we focus on polymersome morphologies, self-assembly kinetics, fusion and fission, behaviors in flow, as well as drug encapsulation and release mechanisms. Furthermore, we also identify existing challenges and limitations in this rapidly evolving field and suggest possible directions for future research.

Identification of inhibitors targeting the energy-coupling factor (ECF) transporters

The energy-coupling factor (ECF) transporters are a family of transmembrane proteins involved in the uptake of vitamins in a wide range of bacteria. Inhibition of the activity of these proteins could reduce the viability of pathogens that depend on vitamin uptake. The central role of vitamin transport in the metabolism of bacteria and absence from humans make the ECF transporters an attractive target for inhibition with selective chemical probes. Here, we report on the identification of a promising class of inhibitors of the ECF transporters. We used coarse-grained molecular dynamics simulations on Lactobacillus delbrueckii ECF-FolT2 and ECF-PanT to profile the binding mode and mechanism of inhibition of this novel chemotype. The results corroborate the postulated mechanism of transport and pave the way for further drug-discovery efforts.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness

Computational techniques applied to drug discovery have gained considerable popularity for their ability to filter potentially active drugs from inactive ones, reducing the time scale and costs of preclinical investigations. The main focus of these studies has historically been the search for compounds endowed with high affinity for a specific molecular target to ensure the formation of stable and long-lasting complexes. Recent evidence has also correlated the in vivo drug efficacy with its binding kinetics, thus opening new fascinating scenarios for ligand/protein binding kinetic simulations in drug discovery. The present article examines the state of the art in the field, providing a brief summary of the most popular and advanced ligand/protein binding kinetics techniques and evaluating their current limitations and the potential solutions to reach more accurate kinetic models. Particular emphasis is put on the need for a paradigm change in the present methodologies toward ligand and protein parametrization, the force field problem, characterization of the transition states, the sampling issue, and algorithms' performance, user-friendliness, and data openness.

Potential binding modes of the gut bacterial metabolite, 5-hydroxyindole, to the intestinal L-type calcium channels and its impact on the microbiota in rats

Intestinal microbiota and microbiota-derived metabolites play a key role in regulating the host physiology. Recently, we have identified a gut-bacterial metabolite, namely 5-hydroxyindole, as a potent stimulant of intestinal motility via its modulation of L-type voltage-gated calcium channels located on the intestinal smooth muscle cells. Dysregulation of L-type voltage-gated calcium channels is associated with various gastrointestinal motility disorders, including constipation, making L-type voltage-gated calcium channels an important target for drug development. Nonetheless, the majority of currently available drugs are associated with alteration of the gut microbiota. Using 16S rRNA sequencing this study shows that, when administered orally, 5-hydroxyindole has only marginal effects on the rat cecal microbiota. Molecular dynamics simulations propose potential-binding pockets of 5-hydroxyindole in the α1 subunit of the L-type voltage-gated calcium channels and when its stimulatory effect on the rat colonic contractility was compared to 16 different analogues, ex-vivo, 5-hydroxyindole stood as the most potent enhancer of the intestinal contractility. Overall, the present findings imply a potential role of microbiota-derived metabolites as candidate therapeutics for targeted treatment of slow intestinal motility-related disorders including constipation.

Differential interactions of resting, activated, and desensitized states of the α7 nicotinic acetylcholine receptor with lipidic modulators

The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion channel that modulates neuronal excitability, largely by allowing Ca2+ permeation. Agonist binding promotes transition from a resting state to an activated state, and then rapidly to a desensitized state. Recently, cryogenic electron microscopy (cryo-EM) structures of the human α7 receptor in nanodiscs were reported in multiple conformations. These were selectively stabilized by inhibitory, activating, or potentiating compounds. However, the functional annotation of these structures and their differential interactions with unresolved lipids and ligands remain incomplete. Here, we characterized their ion permeation, membrane interactions, and ligand binding using computational electrophysiology, free-energy calculations, and coarse-grained molecular dynamics. In contrast to nonconductive structures in apparent resting and desensitized states, the structure determined in the presence of the potentiator PNU-120596 was consistent with an activated state permeable to Ca2+. Transition to this state was associated with compression and rearrangement of the membrane, particularly in the vicinity of the peripheral MX helix. An intersubunit transmembrane site was implicated in selective binding of either PNU-120596 in the activated state or cholesterol in the desensitized state. This substantiates functional assignment of all three lipid-embedded α7-receptor structures with ion-permeation simulations. It also proposes testable models of their state-dependent interactions with lipophilic ligands, including a mechanism for allosteric modulation at the transmembrane subunit interface.

Transferable and Polarizable Coarse Grained Model for Proteins─ProMPT

The application of classical molecular dynamics (MD) simulations at atomic resolution (fine-grained level, FG), to most biomolecular processes, remains limited because of the associated computational complexity of representing all the atoms. This problem is magnified in the presence of protein-based biomolecular systems that have a very large conformational space, and MD simulations with fine-grained resolution have slow dynamics to explore this space. Current transferable coarse grained (CG) force fields in literature are either limited to only peptides with the environment encoded in an implicit form or cannot capture transitions into secondary/tertiary peptide structures from a primary sequence of amino acids. In this work, we present a transferable CG force field with an explicit representation of the environment for accurate simulations with proteins. The force field consists of a set of pseudoatoms representing different chemical groups that can be joined/associated together to create different biomolecular systems. This preserves the transferability of the force field to multiple environments and simulation conditions. We have added electronic polarization that can respond to environmental heterogeneity/fluctuations and couple it to protein's structural transitions. The nonbonded interactions are parametrized with physics-based features such as solvation and partitioning free energies determined by thermodynamic calculations and matched with experiments and/or atomistic simulations. The bonded potentials are inferred from corresponding distributions in nonredundant protein structure databases. We present validations of the CG model with simulations of well-studied aqueous protein systems with specific protein fold types─Trp-cage, Trpzip4, villin, WW-domain, and β-α-β. We also explore the applications of the force field to study aqueous aggregation of Aβ 16-22 peptides.

Improving Small-Molecule Force Field Parameters in Ligand Binding Studies

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Understanding insulin and its receptor from their three-dimensional structures

BACKGROUND

Insulin's discovery 100 years ago and its ongoing use since that time to treat diabetes belies the molecular complexity of its structure and that of its receptor. Advances in single-particle cryo-electron microscopy have over the past three years revolutionized our understanding of the atomic detail of insulin-receptor interactions.

SCOPE OF REVIEW

This review describes the three-dimensional structure of insulin and its receptor and details on how they interact. This review also highlights the current gaps in our structural understanding of the system.

MAJOR CONCLUSIONS

A near-complete picture has been obtained of the hormone receptor interactions, providing new insights into the kinetics of the interactions and necessitating a revision of the extant two-site cross-linking model of hormone receptor engagement. How insulin initially engages the receptor and the receptor's traversed trajectory as it undergoes conformational changes associated with activation remain areas for future investigation.