Structure refinement of membrane proteins via molecular dynamics simulations

Discovery of Highly Selective PARP7 Inhibitors with a Novel Scaffold for Cancer Immunotherapy

PARP7 plays a crucial role in cancer immunity. The inhibition of PARP7 has shown potential in boosting the immune response against cancer, making it an attractive target for cancer immunotherapy. Herein, we employed a rigid constraint strategy (reduction in molecular flexibility) to design and synthesize a series of novel indazole-7-carboxamide derivatives based on the structure of RBN-2397. Among these derivatives, (S)-XY-05 was identified as the most promising PARP7 inhibitor (IC50: 4.5 nM). Additionally, (S)-XY-05 showed enhanced selectivity toward PARP7 and improved pharmacokinetic properties (oral bioavailability: 94.60%) compared with RBN-2397 (oral bioavailability: 25.67%). In the CT26 syngeneic mouse model, monotherapy with (S)-XY-05 displayed a strong antitumor effect (TGI: 83%) by activating T-cell-mediated immunity within the tumor microenvironment. Collectively, we confirmed that (S)-XY-05 has profound effects on tumor immunity, which paves the way for future studies of PARP7 inhibitors that could be utilized in cancer immunotherapy.

Membrane-mediated interaction of non-conventional snake three-finger toxins with nicotinic acetylcholine receptors

Nicotinic acetylcholine receptor of α7 type (α7-nAChR) presented in the nervous and immune systems and epithelium is a promising therapeutic target for cognitive disfunctions and cancer treatment. Weak toxin from Naja kaouthia venom (WTX) is a non-conventional three-finger neurotoxin, targeting α7-nAChR with weak affinity. There are no data on interaction mode of non-conventional neurotoxins with nAChRs. Using α-bungarotoxin (classical three-finger neurotoxin with high affinity to α7-nAChR), we showed applicability of cryo-EM to study complexes of α7-nAChR extracellular ligand-binding domain (α7-ECD) with toxins. Using cryo-EM structure of the α7-ECD/WTX complex, together with NMR data on membrane active site in the WTX molecule and mutagenesis data, we reconstruct the structure of α7-nAChR/WTX complex in the membrane environment. WTX interacts at the entrance to the orthosteric site located at the receptor intersubunit interface and simultaneously forms the contacts with the membrane surface. WTX interaction mode with α7-nAChR significantly differs from α-bungarotoxin's one, which does not contact the membrane. Our study reveals the important role of the membrane for interaction of non-conventional neurotoxins with the nicotinic receptors.

Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.

Computational discovery of binding mode of anti-TRBC1 antibody and predicted key amino acids of TRBC1

Peripheral T-cell lymphoma (PTCL) is a type of non-Hodgkin lymphoma that progresses aggressively with poor survival rate. CAR T cell targeting T-cell receptor β-chain constant domains 1 (TRBC1) of malignant T cells has been developed recently by using JOVI.1 monoclonal antibody as a template. However, the mode of JOVI.1 binding is still unknown. This study aimed to investigate the molecular interaction between JOVI.1 antibody and TRBC1 by using computational methods and molecular docking. Therefore, the TRBC protein crystal structures (TRBC1 and TRBC2) as well as the sequences of JOVI.1 CDR were chosen as the starting materials. TRBC1 and TRBC2 epitopes were predicted, and molecular dynamic (MD) simulation was used to visualize the protein dynamic behavior. The structure of JOVI.1 antibody was also generated before the binding mode was predicted using molecular docking with an antibody mode. Epitope prediction suggested that the N3K4 region of TRBC1 may be a key to distinguish TRBC1 from TCBC2. MD simulation showed the major different surface conformation in this area between two TRBCs. The JOVI.1-TRBC1 structures with three binding modes demonstrated JOVI.1 interacted TRBC1 at N3K4 residues, with the predicted dissociation constant (K_d) ranging from 1.5 × 10⁸ to 1.1 × 10¹⁰ M. The analysis demonstrated JOVI.1 needed D1 residues of TRBC1 for the interaction formation to N3K4 in all binding modes. In conclusion, we proposed the three binding modes of the JOVI.1 antibody to TRBC1 with the new key residue (D1) necessary for N3K4 interaction. This data was useful for JOVI.1 redesign to improve the PTCL-targeting CAR T cell.

Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Computational studies of the mitochondrial carrier family SLC25. Present status and future perspectives

The members of the mitochondrial carrier family, also known as solute carrier family 25 (SLC25), are transmembrane proteins involved in the translocation of a plethora of small molecules between the mitochondrial intermembrane space and the matrix. These transporters are characterized by three homologous domains structure and a transport mechanism that involves the transition between different conformations. Mutations in regions critical for these transporters’ function often cause several diseases, given the crucial role of these proteins in the mitochondrial homeostasis. Experimental studies can be problematic in the case of membrane proteins, in particular concerning the characterization of the structure–function relationships. For this reason, computational methods are often applied in order to develop new hypotheses or to support/explain experimental evidence. Here the computational analyses carried out on the SLC25 members are reviewed, describing the main techniques used and the outcome in terms of improved knowledge of the transport mechanism. Potential future applications on this protein family of more recent and advanced in silico methods are also suggested.

Molecular Dynamics Simulations of Mitochondrial Uncoupling Protein 2

Molecular dynamics (MD) simulations of uncoupling proteins (UCP), a class of transmembrane proteins relevant for proton transport across inner mitochondrial membranes, represent a complicated task due to the lack of available structural data. In this work, we use a combination of homology modelling and subsequent microsecond molecular dynamics simulations of UCP2 in the DOPC phospholipid bilayer, starting from the structure of the mitochondrial ATP/ADP carrier (ANT) as a template. We show that this protocol leads to a structure that is impermeable to water, in contrast to MD simulations of UCP2 structures based on the experimental NMR structure. We also show that ATP binding in the UCP2 cavity is tight in the homology modelled structure of UCP2 in agreement with experimental observations. Finally, we corroborate our results with conductance measurements in model membranes, which further suggest that the UCP2 structure modeled from ANT protein possesses additional key functional elements, such as a fatty acid-binding site at the R60 region of the protein, directly related to the proton transport mechanism across inner mitochondrial membranes.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

A free-energy landscape for the glucagon-like peptide 1 receptor GLP1R

G-protein-coupled receptors (GPCRs) are among the most important receptors in human physiology and pathology. They serve as master regulators of numerous key processes and are involved in as well as cause debilitating diseases. Consequently, GPCRs are among the most attractive targets for drug design and pharmaceutical interventions (>30% of drugs on the market). The glucagon-like peptide 1 (GLP-1) hormone receptor GLP1R is closely involved in insulin secretion by pancreatic β-cells and constitutes a major druggable target for the development of anti-diabetes and obesity agents. GLP1R structure was recently solved, with ligands, allosteric modulators and as part of a complex with its cognate G protein. However, the translation of this structural data into structure/function understanding remains limited. The current study functionally characterizes GLP1R with special emphasis on ligand and cellular partner binding interactions and presents a free-energy landscape as well as a functional model of the activation cycle of GLP1R. Our results should facilitate a deeper understanding of the molecular mechanism underlying GLP1R activation, forming a basis for improved development of targeted therapeutics for diabetes and related disorders.

Methods for the Refinement of Protein Structure 3D Models

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.

Generalized Born Implicit Solvent Models for Biomolecules

It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.

Experimental accuracy in protein structure refinement via molecular dynamics simulations

Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement.