Molecular Dynamics Simulations of Membrane Proteins: An Overview

Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.

Spontaneous Dimerization and Distinct Packing Modes of Transmembrane Domains in Receptor Tyrosine Kinases

The insulin receptor (IR) and the insulin-like growth factor-1 receptor (IGF1R) are homodimeric transmembrane glycoproteins that transduce signals across the membrane on binding of extracellular peptide ligands. The structures of IR/IGF1R fragments in apo and liganded states have revealed that the extracellular subunits of these receptors adopt Λ-shaped configurations to which are connected the intracellular tyrosine kinase (TK) domains. The binding of peptide ligands induces structural transitions in the extracellular subunits leading to potential dimerization of transmembrane domains (TMDs) and autophosphorylation in TKs. However, the activation mechanisms of IR/IGF1R, especially the role of TMDs in coordinating signal-inducing structural transitions, remain poorly understood, in part due to the lack of structures of full-length receptors in apo or liganded states. While atomistic simulations of IR/IGF1R TMDs showed that these domains can dimerize in single component membranes, spontaneous unbiased dimerization in a plasma membrane having physiologically representative lipid composition has not been observed. We address this limitation by employing coarse-grained (CG) molecular dynamics simulations to probe the dimerization propensity of IR/IGF1R TMDs. We observed that TMDs in both receptors spontaneously dimerized independent of their initial orientations in their dissociated states, signifying their natural propensity for dimerization. In the dimeric state, IR TMDs predominantly adopted X-shaped configurations with asymmetric helical packing and significant tilt relative to the membrane normal, while IGF1R TMDs adopted symmetric V-shaped or parallel configurations with either no tilt or a small tilt relative to the membrane normal. Our results suggest that IR/IGF1R TMDs spontaneously dimerize and adopt distinct dimerized configurations.

Effect of GM1 concentration change on plasma membrane: molecular dynamics simulation and analysis

Ganglioside GM1 is a class of glycolipids predominantly located in the nervous system. Comprising a ceramide anchor and an oligosaccharide chain containing sialic acid, GM1 plays a pivotal role in various cellular processes, including signal transduction, cell adhesion, and membrane organization. Moreover, GM1 has been implicated in the pathogenesis of several neurological disorders, such as Parkinson's disease, Alzheimer's disease, and stroke. In this study, by creating a neural cell model membrane simulation system and employing rigorous molecular models, we utilize a coarse-grained molecular dynamics approach to explore the structural and dynamic characteristics of multi-component neuronal plasma membranes at varying GM1 ganglioside concentrations. The simulation results reveal that as GM1 concentration increases, a greater number of hydrogen bonds form between GM1 molecules, resulting in the formation of larger clusters, which leads to reduced membrane fluidity, increased lipid ordering, decreased membrane thickness and surface area and higher levels of GM1 dissociation. Through a meticulous analysis, while considering GM1's structural attributes, we offer valuable insights into the structural and dynamic traits of the cell membrane. This study provides a robust methodology for exploring membrane characteristics and enhances our comprehension of GM1 molecules, serving as a resource for both experimental and computational researchers in this field.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Interactions and Transport of a Bioconjugated Peptide Targeting the Mitomembrane

The Szeto-Schiller (SS) peptides are a subclass of cell-penetrating peptides that can specifically target mitochondria and mediate conditions caused by mitochondrial dysfunction. In this work, we constructed an iron-chelating SS peptide and studied its interaction with a mitochondrial-mimicking membrane using atomistic molecular dynamics (MD) simulations. We report that the peptide/membrane interaction is thermodynamically favorable, and the localization of the peptide to the membrane is driven by electrostatic interactions between the cationic residues and the anionic phospholipid headgroups. The insertion of the peptide into the membrane is driven by hydrophobic interactions between the aromatic side chains in the peptide and the lipid acyl tails. We also probed the translocation of the peptide across the membrane by applying nonequilibrium steered MD simulations and resolved the translocation pathway, free energy profile, and metastable states. We explored four distinct orientations of the peptide along the translocation pathway and found that one orientation was energetically more favorable than the other orientations. We tested a significantly slower pulling velocity on the most thermodynamically favorable system and compared metastable states during peptide translocation. We found that the peptide can optimize hydrophobic interactions with the membrane by having aromatic side chains interacting with the lipid acyl tails instead of forming π-π interactions with each other. The mechanistic insights emerging from our work will potentially facilitate improved peptide design with enhanced activity.

van der Waals Parameter Scanning with Amber Nucleic Acid Force Fields: Revisiting Means to Better Capture the RNA/DNA Structure through MD

Molecular dynamics simulations can be used in combination with experimental techniques to uncover the intricacies of biomolecular structure, dynamics, and the resulting interactions. However, many noncanonical nucleic acid structures have proven to be challenging to replicate in accurate agreement with experimental data, often attributed to known force field deficiencies. A common force field criticism is the handling of van der Waals (vdW) parameters, which have not been updated since the regular use of Ewald's methods became routine. This work dives into the effects of minute vdW radii shifts on RNA tetranucleotide, B-DNA, and Z-DNA model systems described by commonly used Amber force fields. Using multidimensional replica exchange molecular dynamics (M-REMD), the GACC RNA tetranucleotide demonstrated changes in the structural distribution between the NMR minor and anomalous structure populations based on the O2' vdW radii scanning. However, no significant change in the NMR Major conformation population was observed. There were minimal changes in the B-DNA structure but there were more substantial improvements in Z-DNA structural descriptions, specifically with the Tumuc1 force field. This occurred with both LJbb vdW radii adjustments and incorporation of the CUFIX nonbonded parameter modifications. Though the limited vdW modifications tested did not provide a universal fix to the challenge of simulating the various known nucleic acid structures, they do provide direction and a greater understanding for future force field development efforts.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Structure-function Studies of GABA (A) Receptors and Related computer-aided Studies

The γ-aminobutyric acid type A receptor (GABA (A) receptor) is a membrane protein activated by the neurotransmitter GABA. Structurally, this major inhibitory neurotransmitter receptor in the human central nervous system is a pentamer that can be built from a selection of 19 subunits consisting of α(1,2,3,4,5 or 6), β (1,2 or 3), γ (1,2 or 3), ρ (1,2 or 3), and δ, π, θ, and ε. This creates several possible pentameric arrangements, which also influence the pharmacological and physiological properties of the receptor. The complexity and heterogeneity of the receptors are further increased by the addition of short and long splice variants in several subunits and the existence of multiple allosteric binding sites and expansive ligands that can bind to the receptors. Therefore, a comprehensive understanding of the structure and function of the receptors is required to gain novel insights into the consequences of receptor dysfunction and subsequent drug development studies. Notably, advancements in computational-aided studies have facilitated the elucidation of residual interactions and exploring energy binding, which may otherwise be challenging to investigate. In this review, we aim to summarize the current understanding of the structure and function of GABA (A) receptors obtained from advancements in computational-aided applications.

Phase-Separated Giant Liposomes for Stable Elevation of α-Hemolysin Concentration in Lipid Membranes

Staphylococcus aureus α-hemolysin (αHL) is one of the most popular proteins in nanopore experiments within lipid membranes. Higher concentrations of αHL within the lipid membrane are desirable to enhance the mass transport capacity through nanopores. However, the reconstitution of αHL at high concentrations is associated with the problem of membrane lytic disruption. In this study, we present a method that effectively increases αHL concentration while maintaining membrane stability. This method is achieved by using phase-separated giant liposomes, where coexisting liquid-disordered (Ld) and liquid-ordered phases (Lo) are enriched in unsaturated lipids and saturated lipids with cholesterol (Chol), respectively. Fluorescence observation of αHL in liposomes revealed that the presence of Chol facilitates αHL insertion into the membrane. Despite the preferential localization of αHL in the Ld phase rather than the Lo phase, the coexistence of both Lo and Ld phases prevents membrane disruption in the presence of concentrated αHL. We have explained this stabilization mechanism considering the lower membrane tension exhibited by phase-separated liposomes compared to homogeneous liposomes. Under hypertonic conditions, we have successfully increased the local concentration of αHL by invagination of the lipid-only region in the Ld phase, leaving αHL behind. This method exhibits potential for the reconstitution of various nanochannels and membrane proteins that prefer the Ld phase over the Lo phase, thus enabling the production of giant liposomes at high concentrations and the replication of the membrane-crowding condition observed in cells.

A dynamic biomimetic model of the membrane-bound CD4-CD3-TCR complex during pMHC disengagement

The coordinated (dis)engagement of the membrane-bound T cell receptor (TCR)-CD3-CD4 complex from the peptide-major histocompatibility complex (pMHC) is fundamental to TCR signal transduction and T cell effector function. As such, an atomic-scale understanding would not only enhance our basic understanding of the adaptive immune response but would also accelerate the rational design of TCRs for immunotherapy. In this study, we explore the impact of the CD4 coreceptor on the TCR-pMHC (dis)engagement by constructing a molecular-level biomimetic model of the CD3-TCR-pMHC and CD4-CD3-TCR-pMHC complexes within a lipid bilayer. After allowing the system complexes to equilibrate (engage), we use steered molecular dynamics to dissociate (disengage) the pMHC. We find that 1) the CD4 confines the pMHC closer to the T cell by 1.8 nm at equilibrium; 2) CD4 confinement shifts the TCR along the MHC binding groove engaging a different set of amino acids and enhancing the TCR-pMHC bond lifetime; 3) the CD4 translocates under load increasing the interaction strength between the CD4-pMHC, CD4-TCR, and CD4-CD3; and 4) upon dissociation, the CD3-TCR complex undergoes structural oscillation and increased energetic fluctuation between the CD3-TCR and CD3-lipids. These atomic-level simulations provide mechanistic insight on how the CD4 coreceptor impacts TCR-pMHC (dis)engagement. More specifically, our results provide further support (enhanced bond lifetime) for a force-dependent kinetic proofreading model and identify an alternate set of amino acids in the TCR that dominate the TCR-pMHC interaction and could thus impact the design of TCRs for immunotherapy.

Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

ClpXP complex is an ATP-dependent mitochondrial matrix protease that binds, unfolds, translocates, and subsequently degrades specific protein substrates. Its mechanisms of operation are still being debated, and several have been proposed, including the sequential translocation of two residues (SC/2R), six residues (SC/6R), and even long-pass probabilistic models. Therefore, it has been suggested to employ biophysical-computational approaches that can determine the kinetics and thermodynamics of the translocation. In this sense, and based on the apparent inconsistency between structural and functional studies, we propose to apply biophysical approaches based on elastic network models (ENM) to study the intrinsic dynamics of the theoretically most probable hydrolysis mechanism. The proposed models ENM suggest that the ClpP region is decisive for the stabilization of the ClpXP complex, contributing to the flexibility of the residues adjacent to the pore, favoring the increase in pore size and, therefore, with the energy of interaction of its residues with a larger portion of the substrate. It is predicted that the complex may undergo a stable configurational change once assembled and that the deformability of the system once assembled is oriented, to increase the rigidity of the domains of each region (ClpP and ClpX) and to gain flexibility of the pore. Our predictions could suggest under the conditions of this study the mechanism of the interaction of the system, of which the substrate passes through the unfolding of the pore in parallel with a folding of the bottleneck. The variations in the distance calculated by molecular dynamics could allow the passage of a substrate with a size equivalent to ∼3 residues. The theoretical behavior of the pore and the stability and energy of binding to the substrate based on ENM models suggest that in this system, there are thermodynamic, structural, and configurational conditions that allow a possible translocation mechanism that is not strictly sequential.

Molecular mechanisms of integrin αvβ8 activation regulated by graphene, boron nitride and black phosphorus nanosheets

Integrin αvβ8 is a heterodimeric transmembrane protein on macrophages. Nanosheets can activate the integrin and elicit immune responses, exhibiting adverse immunotoxicity. Understanding the mechanism of integrin activation regulated by nanosheets is crucial for safe and effective use of nanosheets in biomedical applications. Herein, we performed all-atom molecular dynamics simulations to clarify the interactions between integrin αvβ8 in the cell membrane and three types of nanosheets, graphene (GRA), hexagonal boron nitride (BN), and black phosphorus (BP). We observed that BP could adsorb the intracellular end of αv monomer and thus break the inner membrane clasp, an important hydrophobic cluster for maintaining the inactive state of integrin. The association between αv and β8 subunit is weakened, promoting the integrin activation. By contrast, GRA and BN exert little influence on the association state of the integrin. Interestingly, the puckered structure of BP affects the integrin activation, where BP with the armchair direction perpendicular to the membrane plane cannot unpack the integrin. Moreover, the perturbation effect of nanosheets on the membrane was also evaluated. BP shows a milder effect on membrane structures and lipid properties than GRA and BN. This work unravels the molecular basis on the activation of integrin mediated by three nanosheets, and suggests the toxicity and therapeutic effect of well-established nanomaterials in the immune system.

Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models

Toll-like receptors (TLRs), classified as pattern recognition receptors, have a primordial role in the activation of the innate immunity. In particular, TLR4 binds to lipopolysaccharides (LPS), a membrane constituent of Gram-negative bacteria, and, together with Myeloid Differentiation factor 2 (MD-2) protein, forms a heterodimeric complex which leads to the activation of the innate immune system response. Identification of TLRs has sparked great interest in the therapeutic manipulation of the innate immune system. In particular, TLR4 antagonists may be useful for the treatment of septic shock, certain autoimmune diseases, noninfectious inflammatory disorders, and neuropathic pain, and TLR4 agonists are under development as vaccine adjuvants in antitumoral treatments. Therefore, TLR4 has risen as a promising therapeutic target, and its modulation constitutes a highly relevant and active research area. Deep structural understanding of TLR4 signaling may help in the design and discovery of TLR4-modulating molecules with desirable therapeutic properties.Computational studies of the different independent domains composing the TLR4 were undertaken, to understand the differential domain organization of TLR4 in aqueous and membrane environments, including Liquid-disordered (Ld) and Liquid-ordered (Lo) membrane models, to account for the TLR4 recruitment in lipid rafts over activation. We modeled, by means of all-atom Molecular Dynamics (MD) simulations, the structural assembly of plausible full-length TLR4 models embedded into a realistic plasma membrane, accounting for the active (agonist) state of the TLR4, thus providing an analysis at both atomic/molecular and thermodynamic levels of the TLR4 assembly and biological activity. Our results unveil relevant molecular aspects involved in the mechanism of receptor activation, and adaptor recruitment in the innate immune pathways, and will promote the discovery of new TLR4 modulators and probes.

Protex-A Python utility for proton exchange in molecular dynamics simulations

Protex is an open-source program that enables proton exchanges of solvent molecules during molecular dynamics simulations. While conventional molecular dynamics simulations do not allow for bond breaking or formation, protex offers an easy-to-use interface to augment these simulations and define multiple proton sites for (de-)protonation using a single topology approach with two different λ-states. Protex was successfully applied to a protic ionic liquid system, where each molecule is prone to (de-)protonation. Transport properties were calculated and compared to experimental values and simulations without proton exchange.

The Role of Transmembrane Proteins in Plant Growth, Development, and Stress Responses

Transmembrane proteins participate in various physiological activities in plants, including signal transduction, substance transport, and energy conversion. Although more than 20% of gene products are predicted to be transmembrane proteins in the genome era, due to the complexity of transmembrane domains they are difficult to reliably identify in the predicted protein, and they may have different overall three-dimensional structures. Therefore, it is challenging to study their biological function. In this review, we describe the typical structures of transmembrane proteins and their roles in plant growth, development, and stress responses. We propose a model illustrating the roles of transmembrane proteins during plant growth and response to various stresses, which will provide important references for crop breeding.

Protein drift-diffusion dynamics and phase separation in curved cell membranes and dendritic spines: Hybrid discrete-continuum methods

We develop methods for investigating protein drift-diffusion dynamics in heterogeneous cell membranes and the roles played by geometry, diffusion, chemical kinetics, and phase separation. Our hybrid stochastic numerical methods combine discrete particle descriptions with continuum-level models for tracking the individual protein drift-diffusion dynamics when coupled to continuum fields. We show how our approaches can be used to investigate phenomena motivated by protein kinetics within dendritic spines. The spine geometry is hypothesized to play an important biological role regulating synaptic strength, protein kinetics, and self-assembly of clusters. We perform simulation studies for model spine geometries varying the neck size to investigate how phase-separation and protein organization is influenced by different shapes. We also show how our methods can be used to study the roles of geometry in reaction-diffusion systems including Turing instabilities. Our methods provide general approaches for investigating protein kinetics and drift-diffusion dynamics within curved membrane structures.

COGRIMEN: Coarse-Grained Method for Modeling of Membrane Proteins in Implicit Environments

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.

Refining details of the structural and electronic properties of the CuB site in pMMO enzyme through sequential molecular dynamics/CPKS-EPR calculations

This work investigated the structural and electronic properties of the copper mononuclear site of the PmoB part of the pMMO enzyme at the molecular level. We propose that the Cu_B catalytic site in the soluble portion of pMMO at room temperature and under physiological conditions is a mononuclear copper complex in a distorted octahedral arrangement with the residues His³³, His¹³⁷, and His¹³⁹ on the equatorial base and two water molecules on the axial axis. Our view was based on the molecular dynamics results and DFT calculations of the electronic paramagnetic resonance parameters and comparisons with experimental EPR data. This new proposed model for the Cu_B site brings additional support concerning the recent experimental evidence, which pointed out that a saturated coordination sphere of the copper ion in the Cu_B center is an essential factor that makes it less efficient than the Cu_C site in the methane oxidation. Therefore, according to the Cu_B site model proposed here, an additional step involving a displacement of at least one water molecule of the copper coordination sphere by the O₂ molecule prior to its activation must be necessary. This scenario is less likely to occur in the Cu_C center once this one is buried in the alpha-helices, which are part of the pMMO structure bound to the membrane wall, and consequently located in a less solvent-exposed region. In addition, we also present a simple and efficient sequential S-MD/CPKS protocol to compute EPR parameters that can, in principle, be expanded for the study of other copper-containing proteins.

Translocating Peptides of Biomedical Interest Obtained from the Spike (S) Glycoprotein of the SARS-CoV-2

At the beginning of 2020, the pandemic caused by the SARS-CoV-2 virus led to the fast sequencing of its genome to facilitate molecular engineering strategies to control the pathogen’s spread. The spike (S) glycoprotein has been identified as the leading therapeutic agent due to its role in localizing the ACE2 receptor in the host’s pulmonary cell membrane, binding, and eventually infecting the cells. Due to the difficulty of delivering bioactive molecules to the intracellular space, we hypothesized that the S protein could serve as a source of membrane translocating peptides. AHB-1, AHB-2, and AHB-3 peptides were identified and analyzed on a membrane model of DPPC (dipalmitoylphosphatidylcholine) using molecular dynamics (MD) simulations. An umbrella sampling approach was used to quantify the energy barrier necessary to cross the boundary (13.2 to 34.9 kcal/mol), and a flat-bottom pulling helped to gain a deeper understanding of the membrane’s permeation dynamics. Our studies revealed that the novel peptide AHB-1 exhibited comparable penetration potential of already known potent cell-penetrating peptides (CPPs) such as TP2, Buforin II, and Frenatin 2.3s. Results were confirmed by in vitro analysis of the peptides conjugated to chitosan nanoparticles, demonstrating its ability to reach the cytosol and escape endosomes, while maintaining high biocompatibility levels according to standardized assays.

Conformational Energy Benchmark for Longer n-Alkane Chains

We present the first benchmark set focusing on the conformational energies of highly flexible, long n-alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g., complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced r²SCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and r²SCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wave function methods, where MP2/CBS surprisingly performs worse compared to the simple D4 dispersion corrected Hartree-Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parametrization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods.

Dawn of a New Era for Membrane Protein Design

A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificial neural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein design community. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a major limitation of the field for decades. Here, we review the work done to date on the membrane protein design and set out established and emerging tools that can be used to most effectively exploit this new access to structures.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

Model architectures for bacterial membranes

The complex composition of bacterial membranes has a significant impact on the understanding of pathogen function and their development towards antibiotic resistance. In addition to the inherent complexity and biosafety risks of studying biological pathogen membranes, the continual rise of antibiotic resistance and its significant economical and clinical consequences has motivated the development of numerous in vitro model membrane systems with tuneable compositions, geometries, and sizes. Approaches discussed in this review include liposomes, solid-supported bilayers, and computational simulations which have been used to explore various processes including drug-membrane interactions, lipid-protein interactions, host–pathogen interactions, and structure-induced bacterial pathogenesis. The advantages, limitations, and applicable analytical tools of all architectures are summarised with a perspective for future research efforts in architectural improvement and elucidation of resistance development strategies and membrane-targeting antibiotic mechanisms.

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.

Lipid21: Complex Lipid Membrane Simulations with AMBER

We extend the modular AMBER lipid force field to include anionic lipids, polyunsaturated fatty acid (PUFA) lipids, and sphingomyelin, allowing the simulation of realistic cell membrane lipid compositions, including raft-like domains. Head group torsion parameters are revised, resulting in improved agreement with NMR order parameters, and hydrocarbon chain parameters are updated, providing a better match with phase transition temperature. Extensive validation runs (0.9 μs per lipid type) show good agreement with experimental measurements. Furthermore, the simulation of raft-like bilayers demonstrates the perturbing effect of increasing PUFA concentrations on cholesterol molecules. The force field derivation is consistent with the AMBER philosophy, meaning it can be easily mixed with protein, small molecule, nucleic acid, and carbohydrate force fields.

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.

Dynamic Coupling of Tyrosine 185 with the Bacteriorhodopsin Photocycle, as Revealed by Chemical Shifts, Assisted AF-QM/MM Calculations and Molecular Dynamic Simulations

Aromatic residues are highly conserved in microbial photoreceptors and play crucial roles in the dynamic regulation of receptor functions. However, little is known about the dynamic mechanism of the functional role of those highly conserved aromatic residues during the receptor photocycle. Tyrosine 185 (Y185) is one of the highly conserved aromatic residues within the retinal binding pocket of bacteriorhodopsin (bR). In this study, we explored the molecular mechanism of its dynamic coupling with the bR photocycle by automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) calculations and molecular dynamic (MD) simulations based on chemical shifts obtained by 2D solid-state NMR correlation experiments. We observed that Y185 plays a significant role in regulating the retinal cis–trans thermal equilibrium, stabilizing the pentagonal H-bond network, participating in the orientation switch of Schiff Base (SB) nitrogen, and opening the F42 gate by interacting with the retinal and several key residues along the proton translocation channel. Our findings provide a detailed molecular mechanism of the dynamic couplings of Y185 and the bR photocycle from a structural perspective. The method used in this paper may be applied to the study of other microbial photoreceptors.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

pK a Calculations in Membrane Proteins from Molecular Dynamics Simulations

The conformational changes of membrane proteins are crucial to their function and usually lead to fluctuations in the electrostatic environment of the protein surface. A very effective way to quantify these changes is by calculating the pK _a values of the protein's titratable residues, which can be regarded as electrostatic probes. To achieve this, we need to take advantage of the fast and reliable pK _a calculators developed for globular proteins and adapt them to include the explicit effects of membranes. Here, we provide a detailed linear response approximation protocol that uses our own software (PypKa) to calculate reliable pK _a values from short MD simulations of membrane proteins.

Normal mode analysis of membrane protein dynamics using the vibrational subsystem analysis

The vibrational subsystem analysis is a useful approach that allows for evaluating the spectrum of modes of a given system by integrating out the degrees of freedom accessible to the environment. The approach could be utilized for exploring the collective dynamics of a membrane protein (system) coupled to the lipid bilayer (environment). However, the application to membrane proteins is limited due to high computational costs of modeling a sufficiently large membrane environment unbiased by end effects, which drastically increases the size of the investigated system. We derived a recursive formula for calculating the reduced Hessian of a membrane protein embedded in a lipid bilayer by decomposing the membrane into concentric cylindrical domains with the protein located at the center. The approach allows for the design of a time- and memory-efficient algorithm and a mathematical understanding of the convergence of the reduced Hessian with respect to increasing membrane sizes. The application to the archaeal aspartate transporter Glt_Ph illustrates its utility and efficiency in capturing the transporter's elevator-like movement during its transition between outward-facing and inward-facing states.

Effects on Steroid 5-Alpha Reductase Gene Expression of Thai Rice Bran Extracts and Molecular Dynamics Study on SRD5A2

Simple Summary

Dihydrotestosterone (DHT), the most potent androgen hormone, is an important aetiologic factor of androgenetic alopecia (AGA), or hair loss. Steroid 5-alpha reductases (SRD5As) increase DHT production in the scalp hair follicles, resulting in hair thinning and hair loss. Even though synthetic SRD5A inhibitors (finasteride and dutasteride) are effective in treating AGA, they cause adverse effects. This has led to an increased interest in alternative treatments from natural sources. The value of Thai rice bran has increased because several of its components may have use in AGA treatment. This study aimed to compare the suppression of the expression of SRD5A genes (type 1–3) exerted by several Thai rice bran extracts and investigate the interactional mechanism of their components towards SRD5A type 2. Tubtim Chumphae rice bran (TRB) had the highest sum of overall bioactive compounds. Among all extracts, the expression of SRD5A genes was suppressed by TRB as well as finasteride. In silico simulation showed that α-tocopherol had the greatest interaction with SRD5A type 2. Our findings identified α-tocopherol as the key bioactive in TRB; it could be developed as an anti-hair loss product.

Abstract

Steroid 5-alpha reductases (SRD5As) are responsible for the conversion of testosterone to dihydrotestosterone, a potent androgen, which is the aetiologic factor of androgenetic alopecia. This study aimed to compare the SRD5A gene expression suppression activity exerted by Thai rice bran extracts and their components and investigate the interactional mechanism between bioactive compounds and SRD5A2 using molecular dynamics (MD) simulation. Bran of Oryza sativa cv. Tubtim Chumphae (TRB), Yamuechaebia Morchor (YRB), Riceberry (RRB), and Malinil Surin (MRB), all rice milling by-products, was solvent-extracted. The ethanolic extract of TRB had the highest sum of overall bioactive compounds (γ-oryzanol; α-, β-, and γ-tocopherol; phenolics; and flavonoids). Among all extracts, TRB greatly downregulated the expression of SRD5A1, SRD5A2, and SRD5A3; there were no significant differences between TRB and finasteride regarding SRD5A suppression. The linear relationship and principal component analysis supported that the α-tocopherol content was correlated with the SRD5A suppression exerted by TRB. Furthermore, MD simulation demonstrated that α-tocopherol had the highest binding affinity towards SRD5A2 by interacting with residues Phe118 and Trp201. Our findings indicate that α-tocopherol effectively downregulates the expression of SRD5A genes and inhibits SRD5A2 activity, actions that are comparable to standard finasteride. TRB, a source of α-tocopherol, could be developed as an anti-hair loss product.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Decoding the Molecular Effects of Atovaquone Linked Resistant Mutations on Plasmodium falciparum Cytb-ISP Complex in the Phospholipid Bilayer Membrane

Atovaquone (ATQ) is a drug used to prevent and treat malaria that functions by targeting the Plasmodium falciparum cytochrome b (PfCytb) protein. PfCytb catalyzes the transmembrane electron transfer (ET) pathway which maintains the mitochondrial membrane potential. The ubiquinol substrate binding site of the protein has heme bL, heme bH and iron-sulphur [2FE-2S] cluster cofactors that act as redox centers to aid in ET. Recent studies investigating ATQ resistance mechanisms have shown that point mutations of PfCytb confer resistance. Thus, understanding the resistance mechanisms at the molecular level via computational approaches incorporating phospholipid bilayer would help in the design of new efficacious drugs that are also capable of bypassing parasite resistance. With this knowledge gap, this article seeks to explore the effect of three drug resistant mutations Y268C, Y268N and Y268S on the PfCytb structure and function in the presence and absence of ATQ. To draw reliable conclusions, 350 ns all-atom membrane (POPC:POPE phospholipid bilayer) molecular dynamics (MD) simulations with derived metal parameters for the holo and ATQ-bound -proteins were performed. Thereafter, simulation outputs were analyzed using dynamic residue network (DRN) analysis. Across the triplicate MD runs, hydrophobic interactions, reported to be crucial in protein function were assessed. In both, the presence and absence of ATQ and a loss of key active site residue interactions were observed as a result of mutations. These active site residues included: Met 133, Trp136, Val140, Thr142, Ile258, Val259, Pro260 and Phe264. These changes to residue interactions are likely to destabilize the overall intra-protein residue communication network where the proteins' function could be implicated. Protein dynamics of the ATQ-bound mutant complexes showed that they assumed a different pose to the wild-type, resulting in diminished residue interactions in the mutant proteins. In summary, this study presents insights on the possible effect of the mutations on ATQ drug activity causing resistance and describes accurate MD simulations in the presence of the lipid bilayer prior to conducting inhibitory drug discovery for the PfCytb-iron sulphur protein (Cytb-ISP) complex.

Dynamics and Environmental Characteristics of Spin Labels in a KvAP Voltage Sensor by Molecular Dynamics Simulations

The nitroxide spin label is the most widely used probe for electron paramagnetic resonance (EPR) spectroscopy studies of the structure and function of biomolecules. However, the role of surrounding environments in determining the dynamics of nitroxide spin labels in biological complex systems remains to be clarified. This study aims to characterize the dynamics and environmental structure of spin labels in the voltage-sensing domain (VSD) of a KvAP potassium channel by means of molecular dynamics (MD) studies. MD simulations for unlabeled and 132 spin-labeled KvAP-VSD models (spin labels introduced at positions 20-151) were carried out in a phospholipid bilayer to evaluate conformational dynamics of nitroxide spin-label side chains in the VSD. Structural flexibility, conformational freedom, and orientation of the spin-label side chains were investigated in relation to their dynamics in different microenvironments. The analysis of MD data showed that the attached spin-label probe did not severely perturb the protein dynamics. The conformational freedoms of the nitroxide side chain vary with the physical structure of the surrounding environments. The two terminal dihedral angles of the nitroxide side chain tend to cluster and adopt several preferred rotameric states. From the nearest-neighbor analysis, the spin label can be exposed to either a homogeneous or heterogeneous environment with various exposure scenarios. The dynamical movement of KvAP-VSD is high at a water-exposed site, moderate in the membrane, and low in the protein core. Understanding the structure and dynamics behaviors of spin labels helps to manage the experimental uncertainty and avoid misleading interpretation in relation to the protein structure.

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.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Inactivation-mimicking block of the epithelial calcium channel TRPV6

Epithelial calcium channel TRPV6 plays vital roles in calcium homeostasis, and its dysregulation is implicated in multifactorial diseases, including cancers. Here, we study the molecular mechanism of selective nanomolar-affinity TRPV6 inhibition by (4-phenylcyclohexyl)piperazine derivatives (PCHPDs). We use x-ray crystallography and cryo-electron microscopy to solve the inhibitor-bound structures of TRPV6 and identify two types of inhibitor binding sites in the transmembrane region: (i) modulatory sites between the S1-S4 and pore domains normally occupied by lipids and (ii) the main site in the ion channel pore. Our structural data combined with mutagenesis, functional and computational approaches suggest that PCHPDs plug the open pore of TRPV6 and convert the channel into a nonconducting state, mimicking the action of calmodulin, which causes inactivation of TRPV6 channels under physiological conditions. This mechanism of inhibition explains the high selectivity and potency of PCHPDs and opens up unexplored avenues for the design of future-generation biomimetic drugs.

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

The dynamic structure of proteins is essential for their functions and may include large conformational transitions which can be studied by molecular dynamics (MD) simulations. However, details of these transitions are difficult to automatically track. To facilitate their analysis, we developed two scores of correlation between sidechain dihedral angles. The CIRCULAR and OMES scores are computed from, respectively, dihedral angle values and rotamer distributions. As a case study, we applied our methods to an activation-like transition of the chemokine receptor CXCR4, observed during accelerated MD simulations. The principal component analysis of the correlation matrices was consistent with the networking structure of the top ranking pairs. Both scores identify a set of residues whose “collaborative” sidechain rotamerization immediately preceded or accompanied the conformational transition of CXCR4. Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways.

A Computational and Modeling Study of the Reaction Mechanism of Staphylococcus aureus Monoglycosyltransferase Reveals New Insights on the GT51 Family of Enzymes

Bacterial glycosyltransferases of the GT51 family are key enzymes in bacterial cell wall synthesis. Inhibiting cell wall synthesis is a very effective approach for development of antibiotics, as this can lead to either bacteriostatic or bactericidal effects. Even though the existence of this family has been known for over 50 years, only one potent inhibitor exists, which is an analog of the lipid IV product and derived from a natural product. Drug development focused on bacterial transglycosylase has been hampered due to little being know about its structure and reaction mechanism. In this study, Staphylococcus aureus monoglycosyltransferase was investigated at an atomistic level using computational methods. Classical molecular dynamics simulations were used to reveal information about the large-scale dynamics of the enzyme-substrate complex and the importance of magnesium in structure and function of the protein, while mixed mode quantum mechanics/molecular mechanics calculations unveiled a novel hypothesis for the reaction mechanism. From these results, we present a new model for the binding mode of lipid II and the reaction mechanism of the GT51 glycosyltransferases. A metal-bound hydroxide catalyzed reaction mechanism yields an estimated free energy barrier of 16.1 ± 1.0 kcal/mol, which is in line with experimental values. The importance of divalent cations is also further discussed. These findings could significantly aid targeted drug design, particularly the efficient development of transition state analogues as potential inhibitors for the GT51 glycosyltransferases.

In Silico Study of Membrane Lipid Composition Regulating Conformation and Hydration of Influenza Virus B M2 Channel

The proton conduction of transmembrane influenza virus B M2 (BM2) proton channel is possibly mediated by the membrane environment, but the detailed molecular mechanism is challenging to determine. In this work, how membrane lipid composition regulates the conformation and hydration of BM2 channel is elucidated in silico. The appearance of several important hydrogen-bond networks has been discovered, as the addition of negatively charged lipid palmitoyloleoyl phosphatidylglycerol (POPG) and cholesterol reduces membrane fluidity and augments membrane rigidity. A more rigid membrane environment is beneficial to expand the channel, allow more water to enter the channel, promote channel hydration, and then even affect the proton conduction facilitated by the hydrated channel. Thus, membrane environment could be identified as an important influence factor of conformation and hydration of BM2. These findings can provide a unique perspective for understanding the mechanism of membrane lipid composition regulating conformation and hydration of BM2 and have important significance to the further study of anti-influenza virus B drugs.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R²) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

Molecular and Coarse-Grained Modeling to Characterize and Optimize Dendrimer-Based Nanocarriers for Short Interfering RNA Delivery

Dendrimer nanocarriers are unique hyper-branched polymers with biomolecule-like properties, representing a promising prospect as a nucleic acid delivery system. The design of effective dendrimer-based gene carriers requires considering several parameters, such as carrier morphology, size, molecular weight, surface chemistry, and flexibility/rigidity. In detail, the rational design of the dendrimer surface chemistry has been ascertained to play a crucial role on the efficiency of interaction with nucleic acids. Within this framework, advances in the field of organic chemistry have allowed us to design dendrimers with even small difference in the chemical structure of their surface terminals. In this study, we have selected two different cationic phosphorus dendrimers of generation 3 functionalized, respectively, with pyrrolidinium (DP) and morpholinium (DM) surface groups, which have demonstrated promising potential for short interfering RNA (siRNA) delivery. Despite DP and DM differing only for one atom in their chemical structure, in vitro and in vivo experiments have highlighted several differences between them in terms of siRNA complexation properties. In this context, we have employed coarse-grained molecular dynamics simulation techniques to shed light on the supramolecular characteristics of dendrimer-siRNA complexation, the so-called dendriplex formations. Our data provide important information on self-assembly dynamics driven by surface chemistry and competition mechanisms.