Atomic-level characterization of protein-protein association

Dynamic Formation of the Protein-Lipid Pre-fusion Complex

Synaptic vesicles (SVs) fuse with the presynaptic membrane (PM) to release neuronal transmitters. The SV protein Synaptotagmin 1 (Syt1) serves as a Ca2+ sensor for evoked fusion. Syt1 is thought to trigger fusion by penetrating into PM upon Ca2+ binding, however the mechanistic detail of this process is still debated. Syt1 interacts with the SNARE complex, a coiled-coil four-helical bundle that enables the SV-PM attachment. The SNARE-associated protein Complexin (Cpx) promotes the Ca2+-dependent fusion, possibly interacting with Syt1. We employed all-atom molecular dynamics (MD) to investigate the formation of the Syt1-SNARE-Cpx complex interacting with the lipid bilayers of PM and SV. Our simulations demonstrated that the PM-Syt1-SNARE-Cpx complex can transition to a "dead-end" state, wherein Syt1 attaches tightly to PM but does not immerse into it, as opposed to a pre-fusion state, which has the tips of the Ca2+-bound C2 domains of Syt1 inserted into PM. Our simulations unraveled the sequence of Syt1 conformational transitions, including the simultaneous Syt1 docking to the SNARE-Cpx bundle and PM, followed by the Ca2+ chelation and the penetration of the tips of Syt1 domains into PM, leading to the pre-fusion state of the protein-lipid complex. Importantly, we found that the direct Syt1-Cpx interactions are required to promote these transitions. Thus, we developed the all-atom dynamic model of the conformational transitions that lead to the formation of the pre-fusion PM-Syt1-SNARE-Cpx complex. Our simulations also revealed an alternative "dead-end" state of the protein-lipid complex that can be formed if this pathway is disrupted.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

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.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Protein-based bioactive coatings: from nanoarchitectonics to applications

Protein-based bioactive coatings have emerged as a versatile and promising strategy for enhancing the performance and biocompatibility of diverse biomedical materials and devices. Through surface modification, these coatings confer novel biofunctional attributes, rendering the material highly bioactive. Their widespread adoption across various domains in recent years underscores their importance. This review systematically elucidates the behavior of protein-based bioactive coatings in organisms and expounds on their underlying mechanisms. Furthermore, it highlights notable advancements in artificial synthesis methodologies and their functional applications in vitro. A focal point is the delineation of assembly strategies employed in crafting protein-based bioactive coatings, which provides a guide for their expansion and sustained implementation. Finally, the current trends, challenges, and future directions of protein-based bioactive coatings are discussed.

Engineering immunogens that select for specific mutations in HIV broadly neutralizing antibodies

Vaccine development targeting rapidly evolving pathogens such as HIV-1 requires induction of broadly neutralizing antibodies (bnAbs) with conserved paratopes and mutations, and, in some cases, the same Ig-heavy chains. The current trial-and-error search for immunogen modifications that improve selection for specific bnAb mutations is imprecise. To precisely engineer bnAb boosting immunogens, we used molecular dynamics simulations to examine encounter states that form when antibodies collide with the HIV-1 Envelope (Env). By mapping how bnAbs use encounter states to find their bound states, we identified Env mutations that were predicted to select for specific antibody mutations in two HIV-1 bnAb B cell lineages. The Env mutations encoded antibody affinity gains and selected for desired antibody mutations in vivo. These results demonstrate proof-of-concept that Env immunogens can be designed to directly select for specific antibody mutations at residue-level precision by vaccination, thus demonstrating the feasibility of sequential bnAb-inducing HIV-1 vaccine design.

Eukaryotic Kv channel Shaker inactivates through selectivity filter dilation rather than collapse

Eukaryotic voltage-gated K+ channels have been extensively studied, but the structural bases for some of their most salient functional features remain to be established. C-type inactivation, for example, is an auto-inhibitory mechanism that confers temporal resolution to their signal-firing activity. In a recent breakthrough, studies of a mutant of Shaker that is prone to inactivate indicated that this process entails a dilation of the selectivity filter, the narrowest part of the ion conduction pathway. Here, we report an atomic-resolution cryo-electron microscopy structure that demonstrates that the wild-type channel can also adopt this dilated state. All-atom simulations corroborate this conformation is congruent with the electrophysiological characteristics of the C-type inactivated state, namely, residual K+ conductance and altered ion specificity, and help rationalize why inactivation is accelerated or impeded by certain mutations. In summary, this study establishes the molecular basis for an important self-regulatory mechanism in eukaryotic K+ channels, laying a solid foundation for further studies.

Functional dynamics and allosteric modulation of TRPA1

The cation channel TRPA1 is a potentially important drug target, and characterization of TRPA1 functional dynamics might help guide structure-based drug design. Here, we present results from long-timescale molecular dynamics simulations of TRPA1 with an allosteric activator, allyl isothiocyanate (AITC), in which we observed spontaneous transitions from a closed, non-conducting channel conformation into an open, conducting conformation. Based on these transitions, we propose a gating mechanism in which movement of a regulatory TRP-like domain allosterically translates into pore opening in a manner reminiscent of pore opening in voltage-gated ion channels. In subsequent experiments, we found that mutations that disrupt packing of the S4-S5 linker-TRP-like domain and the S5 and S6 helices also affected channel activity. In simulations, we also observed A-967079, a known allosteric inhibitor, binding between helices S5 and S6, suggesting that A-967079 may suppress activity by stabilizing a non-conducting pore conformation-a finding consistent with our proposed gating mechanism.

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.

Investigating the novel-binding site of RPA2 on Menin and predicting the effect of point mutation of Menin through protein-protein interactions

Protein-protein interactions (PPIs) play a critical role in all biological processes. Menin is tumor suppressor protein, mutated in multiple endocrine neoplasia type 1 syndrome and has been shown to interact with multiple transcription factors including (RPA2) subunit of replication protein A (RPA). RPA2, heterotrimeric protein required for DNA repair, recombination and replication. However, it's still remains unclear the specific amino acid residues that have been involved in Menin-RPA2 interaction. Thus, accurately predicting the specific amino acid involved in interaction and effects of MEN1 mutations on biological systems is of great interests. The experimental approaches for identifying amino acids in menin-RPA2 interactions are expensive, time-consuming, and challenging. This study leverages computational tools, free energy decomposition and configurational entropy scheme to annotate the menin-RPA2 interaction and effect on menin point mutation, thereby proposing a viable model of menin-RPA2 interaction. The menin-RPA2 interaction pattern was calculated on the basis of different 3D structures of menin and RPA2 complexes, constructed using homology modeling and docking strategy, generating three best-fit models: Model 8 (- 74.89 kJ/mol), Model 28 (- 92.04 kJ/mol) and Model 9 (- 100.4 kJ/mol). The molecular dynamic (MD) was performed for 200 ns and binding free energies and energy decomposition analysis were calculated using Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) in GROMACS. From binding free energy change, model 8 of Menin-RPA2 exhibited most negative binding energy of - 205.624 kJ/mol, followed by model 28 of Menin-RPA2 with - 177.382 kJ/mol. After S606F point mutation in Menin, increase of BFE (ΔGbind) by - 34.09 kJ/mol in Model 8 of mutant Menin-RPA2 occurs. Interestingly, we found a significant reduction of BFE (ΔGbind) and configurational entropy by - 97.54 kJ/mol and - 2618 kJ/mol in mutant model 28 as compared the o wild type. Collectively, this is the first study to highlight the configurational entropy of protein-protein interactions thereby strengthening the prediction of two significant important interaction sites in menin for the binding of RPA2. These predicted sites could be vulnerable for structural alternation in terms of binding free energy and configurational entropy after missense mutation in menin.

Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations are used to study molecular dissociation events by applying a harmonic force to the molecules and pulling them at a constant velocity. Instead of constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation time at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein-ligand systems, producing dissociation time at various forces. We extrapolated these values to the dissociation rate without a constant force using Bell's model or the Dudko-Hummer-Szabo model. We demonstrate that the CF-SMD simulations with the models predicted the dissociation time in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

Desensitization dynamics of the AMPA receptor

To perform their physiological functions, amino methyl propionic acid receptors (AMPARs) cycle through active, resting, and desensitized states, and dysfunction in AMPAR activity is associated with various neurological disorders. Transitions among AMPAR functional states, however, are largely uncharacterized at atomic resolution and are difficult to examine experimentally. Here, we report long-timescale molecular dynamics simulations of dimerized AMPAR ligand-binding domains (LBDs), whose conformational changes are tightly coupled to changes in AMPAR functional states, in which we observed LBD dimer activation and deactivation upon ligand binding and unbinding at atomic resolution. Importantly, we observed the ligand-bound LBD dimer transition from the active conformation to several other conformations, which may correspond with distinct desensitized conformations. We also identified a linker region whose structural rearrangements heavily affected the transitions to and among these putative desensitized conformations, and confirmed, using electrophysiology experiments, the importance of the linker region in these functional transitions.

Predicting Biomolecular Binding Kinetics: A Review

Biomolecular binding kinetics including the association (k_on) and dissociation (k_off) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.

Quantifying the Molecular Polarization Response of Liquid Water Interfaces at Heterogeneously Charged Surfaces

The hydration shells of proteins mediate interactions, such as small molecule binding, that are vital to their biological function or in some cases their dysfunction. However, even when the structure of a protein is known, the properties of its hydration environment cannot be easily predicted due to the complex interplay between protein surface heterogeneity and the collective structure of water's hydrogen bonding network. This manuscript presents a theoretical study of the influence of surface charge heterogeneity on the polarization response of the liquid water interface. We focus our attention on classical point charge models of water, where the polarization response is limited to molecular reorientation. We introduce a new computational method for analyzing simulation data that is capable of quantifying water's collective polarization response and determining the effective surface charge distribution of hydrated surfaces over atomistic length scales. To illustrate the utility of this method, we present the results of molecular dynamics simulations of liquid water in contact with a heterogeneous model surface and the CheY protein.

An insight into SARS-CoV-2 membrane protein interaction with spike, envelope, and nucleocapsid proteins

Intraviral protein-protein interactions are crucial for replication, pathogenicity, and viral assembly. Among these, virus assembly is a critical step as it regulates the arrangements of viral structural proteins and helps in the encapsulation of genomic material. SARS-CoV-2 structural proteins play an essential role in the self-rearrangement, RNA encapsulation, and mature virus particle formation. In SARS-CoV, the membrane protein interacts with the envelope and spike protein in Endoplasmic Reticulum Golgi Intermediate Complex (ERGIC) to form an assembly in the lipid bilayer, followed by membrane-ribonucleoprotein (nucleocapsid) interaction. In this study, we tried to understand the interaction of membrane protein's interaction with envelope, spike, and nucleocapsid proteins using protein-protein docking. Further, simulation studies were performed up to 100 ns to examine the stability of protein-protein complexes of Membrane-Envelope, Membrane-Spike, and Membrane-Nucleocapsid proteins. Prime MM-GBSA showed high binding energy calculations for the simulated structures than the docked complex. The interactions identified in our study will be of great importance, as it provides valuable insight into the protein-protein complex, which could be the potential drug targets for future studies.Communicated by Ramaswamy H. Sarma.

State-specific morphological deformations of the lipid bilayer explain mechanosensitive gating of MscS ion channels

The force-from-lipids hypothesis of cellular mechanosensation posits that membrane channels open and close in response to changes in the physical state of the lipid bilayer, induced for example by lateral tension. Here, we investigate the molecular basis for this transduction mechanism by studying the mechanosensitive ion channel MscS from Escherichia coli and its eukaryotic homolog MSL1 from Arabidopsis thaliana. First, we use single-particle cryo-electron microscopy to determine the structure of a novel open conformation of wild-type MscS, stabilized in a thinned lipid nanodisc. Compared with the closed state, the structure shows a reconfiguration of helices TM1, TM2, and TM3a, and widening of the central pore. Based on these structures, we examined how the morphology of the membrane is altered upon gating, using molecular dynamics simulations. The simulations reveal that closed-state MscS causes drastic protrusions in the inner leaflet of the lipid bilayer, both in the absence and presence of lateral tension, and for different lipid compositions. These deformations arise to provide adequate solvation to hydrophobic crevices under the TM1-TM2 hairpin, and clearly reflect a high-energy conformation for the membrane, particularly under tension. Strikingly, these protrusions are largely eradicated upon channel opening. An analogous computational study of open and closed MSL1 recapitulates these findings. The gating equilibrium of MscS channels thus appears to be dictated by opposing conformational preferences, namely those of the lipid membrane and of the protein structure. We propose a membrane deformation model of mechanosensation, which posits that tension shifts the gating equilibrium towards the conductive state not because it alters the mode in which channel and lipids interact, but because it increases the energetic cost of the morphological perturbations in the membrane required by the closed state.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

Large interfacial relocation in RBD-ACE2 complex may explain fast-spreading property of Omicron

The Omicron variant of SARS-CoV-2 emerged in South African in late 2021. This variant has a large number of mutations, and regarded as fastest-spreading Covid variant. The spike RBD region of SARS-CoV-2 and its interaction with human ACE2 play fundamental role in viral infection and transmission. To explore the reason of fast-spreading properties of Omicron variant, we have modeled the interactions of Omicron RBD and human ACE2 using docking and molecular dynamics simulations. Results show that RBD-ACE2 binding site may drastically relocate with an enlarged interface. The predicted interface has large negative binding energies and shows stable conformation in molecular dynamics simulations. It was found that the interfacial area in Omicron RBD-ACE2 complex is increased up to 40% in comparison to wild-type Sars-Cov-2. Moreover, the number of hydrogen bonds significantly increased up to 80%. The key interacting residues become also very different in Omicron variant. The new binding interface can significantly accommodate R403, as a key RBD residue, near ACE2 surface which leads to two new strong salt bridges. The exploration of the new binding interface can help to understand the reasons of high transmission rate of Omicron.

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Advances in computational modeling have led to an increasing focus on larger biomolecular systems, up to the level of a cell. Protein interactions are a central component of cellular processes. Techniques for modeling protein interactions have been divided between two fields: protein docking (predicting the static structures of protein complexes) and molecular simulation (modeling the dynamics of protein association, for relatively short simulation times at atomic resolution). Our study combined the two approaches to reach very long simulation times. The study makes the model more adequate to the real cells, to explore cellular processes at atomic resolution to better understand molecular mechanisms of life, and to use this knowledge to improve our ability to treat diseases.

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Association of two polyelectrolytes in salt solutions

The association of polyelectrolytes (PEs) in solution affects a wealth of structural and dynamic behaviors, and is also fundamentally important for an understanding of protein association and aggregation. Here, we theoretically study the association of two PE chains by addressing the stability and morphology of the non-spherical associates. Our theory predicts that an elongated pearl-necklace (PN) associate can be stable at high salt concentrations due to the screened electrostatic repulsion. This contradicts the implication of scaling theory. In addition, there is no one-to-one correspondence between the morphology of the associate and its constituting unimers, which is demonstrated by the existence of different association modes.

Dynamic stability of salt stable cowpea chlorotic mottle virus capsid protein dimers and pentamers of dimers

Intermediates of the self-assembly process of the salt stable cowpea chlorotic mottle virus (ss-CCMV) capsid can be modelled atomistically on realistic computational timescales either by studying oligomers in equilibrium or by focusing on their dissociation instead of their association. Our previous studies showed that among the three possible dimer interfaces in the icosahedral capsid, two are thermodynamically relevant for capsid formation. The aim of the current study is to evaluate the relative structural stabilities of the three different ss-CCMV dimers and to find and understand the conditions that lead to their dissociation. Long timescale molecular dynamics simulations at 300 K of the various dimers and of the pentamer of dimers underscore the importance of large contact surfaces on stabilizing the capsid subunits within an oligomer. Simulations in implicit solvent show that at higher temperature (350 K), the N-terminal tails of the protein units act as tethers, delaying dissociation for all but the most stable interface. The pentamer of dimers is also found to be stable on long timescales at 300 K, with an inherent flexibility of the outer protein chains.

Investigation of the conformation of human prion protein in ethanol solution using molecular dynamics simulations

When the conformation of protein is changed from its natural state to a misfolded state, some diseases will happen like prion disease. Prion diseases are a set of deadly neurodegenerative diseases caused by prion protein misfolding and aggregation. Monohydric alcohols have a strong influence on the structure of protein. However, whether monohydric alcohols inhibit amyloid fibrosis remains uncertain. Here, to elucidate the effect of ethanol on the structural stability of human prion protein, molecular dynamics simulations were employed to analyze the conformational changes and dynamics characteristics of human prion proteins at different temperatures. The results show that the extension of β-sheet occurs more easily and the α-helix is more easily disrupted at high temperatures. We found that ethanol can destroy the hydrophobic interactions and make the hydrogen bonds stable, which protects the secondary structure of the protein, especially at 500 K.Communicated by Ramaswamy H. Sarma.

Hazardous Shortcuts in Standard Binding Free Energy Calculations

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.

Characterizing Receptor Flexibility to Predict Mutations That Lead to Human Adaptation of Influenza Hemagglutinin

A key step in the emergence of human pandemic influenza strains has been a switch in binding preference of the viral glycoprotein hemagglutinin (HA) from avian to human sialic acid (SA) receptors. The conformation of the bound SA varies substantially with HA sequence, and crystallographic evidence suggests that the bound SA is flexible, making it difficult to predict which mutations are responsible for changing HA-binding preference. We performed molecular dynamics (MD) simulations of SA analogues binding to various HAs and observed a dynamic equilibrium among structurally diverse receptor conformations, including conformations that have not been experimentally observed. Using one such novel conformation, we predicted—and experimentally confirmed—a set of mutations that substantially increased an HA’s affinity for a human SA analogue. This prediction could not have been inferred from the existing crystal structures, suggesting that MD-generated HA–SA conformational ensembles could help researchers predict human-adaptive mutations, aiding surveillance of emerging pandemic threats.

Computational Assessment of Protein-Protein Binding Specificity within a Family of Synaptic Surface Receptors

Atomic-level information is essential to explain the formation of specific protein complexes in terms of structure and dynamics. The set of Dpr and DIP proteins, which play a key role in the neuromorphogenesis in the nervous system of Drosophila melanogaster, offer a rich paradigm to learn about protein-protein recognition. Many members of the DIP subfamily cross-react with several members of the Dpr family and vice versa. While there exists a total of 231 possible Dpr-DIP heterodimer complexes from the 21 Dpr and 11 DIP proteins, only 57 "cognate" pairs have been detected by surface plasmon resonance (SPR) experiments, suggesting that the remaining 174 pairs have low or unreliable binding affinity. Our goal is to assess the performance of computational approaches to characterize the global set of interactions between Dpr and DIP proteins and identify the specificity of binding between each DIP with their corresponding Dpr binding partners. In addition, we aim to characterize how mutations influence the specificity of the binding interaction. In this work, a wide range of knowledge-based and physics-based approaches are utilized, including mutual information, linear discriminant analysis, homology modeling, molecular dynamics simulations, Poisson-Boltzmann continuum electrostatics calculations, and alchemical free energy perturbation to decipher the origin of binding specificity of the Dpr-DIP complexes examined. Ultimately, the results show that those two broad strategies are complementary, with different strengths and limitations. Biological inter-relations are more clearly revealed through knowledge-based approaches combining evolutionary and structural features, the molecular determinants controlling binding specificity can be predicted accurately with physics-based approaches based on atomic models.

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Induced fit with replica exchange improves protein complex structure prediction

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.

Elucidating the mechanisms underlying protein conformational switching using NMR spectroscopy

How proteins switch between various ligand-free and ligand-bound structures has been a key biophysical question ever since the postulation of the Monod-Wyman-Changeux and Koshland-Nemethy-Filmer models over six decades ago. The ability of NMR spectroscopy to provide structural and kinetic information on biomolecular conformational exchange places it in a unique position as an analytical tool to interrogate the mechanisms of biological processes such as protein folding and biomolecular complex formation. In addition, recent methodological developments in the areas of saturation transfer and relaxation dispersion have expanded the scope of NMR for probing the mechanics of transitions in systems where one or more states constituting the exchange process are sparsely populated and 'invisible' in NMR spectra. In this review, we highlight some of the strategies available from NMR spectroscopy for examining the nature of multi-site conformational exchange, using five case studies that have employed NMR, either in isolation, or in conjunction with other biophysical tools.

Integrated Design of a Membrane-Lytic Peptide-Based Intravenous Nanotherapeutic Suppresses Triple-Negative Breast Cancer

Membrane-lytic peptides offer broad synthetic flexibilities and design potential to the arsenal of anticancer therapeutics, which can be limited by cytotoxicity to noncancerous cells and induction of drug resistance via stress-induced mutagenesis. Despite continued research efforts on membrane-perforating peptides for antimicrobial applications, success in anticancer peptide therapeutics remains elusive given the muted distinction between cancerous and normal cell membranes and the challenge of peptide degradation and neutralization upon intravenous delivery. Using triple-negative breast cancer as a model, the authors report the development of a new class of anticancer peptides. Through function-conserving mutations, the authors achieved cancer cell selective membrane perforation, with leads exhibiting a 200-fold selectivity over non-cancerogenic cells and superior cytotoxicity over doxorubicin against breast cancer tumorspheres. Upon continuous exposure to the anticancer peptides at growth-arresting concentrations, cancer cells do not exhibit resistance phenotype, frequently observed under chemotherapeutic treatment. The authors further demonstrate efficient encapsulation of the anticancer peptides in 20 nm polymeric nanocarriers, which possess high tolerability and lead to effective tumor growth inhibition in a mouse model of MDA-MB-231 triple-negative breast cancer. This work demonstrates a multidisciplinary approach for enabling translationally relevant membrane-lytic peptides in oncology, opening up a vast chemical repertoire to the arms race against cancer.

Optimizing the Calculation of Free Energy Differences in Nonequilibrium Work SQM/MM Switching Simulations

A key step during indirect alchemical free energy simulations using quantum mechanical/molecular mechanical (QM/MM) hybrid potential energy functions is the calculation of the free energy difference ΔAlow→high between the low level (e.g., pure MM) and the high level of theory (QM/MM). A reliable approach uses nonequilibrium work (NEW) switching simulations in combination with Jarzynski's equation; however, it is computationally expensive. In this study, we investigate whether it is more efficient to use more shorter switches or fewer but longer switches. We compare results obtained with various protocols to reference free energy differences calculated with Crooks' equation. The central finding is that fewer longer switches give better converged results. As few as 200 sufficiently long switches lead to ΔAlow→high values in good agreement with the reference results. This optimized protocol reduces the computational cost by a factor of 40 compared to earlier work. We also describe two tools/ways of analyzing the raw data to detect sources of poor convergence. Specifically, we find it helpful to analyze the raw data (work values from the NEW switching simulations) in a quasi-time series-like manner. Principal component analysis helps to detect cases where one or more conformational degrees of freedom are different at the low and high level of theory.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

Structure of the Shaker Kv channel and mechanism of slow C-type inactivation

Voltage-activated potassium (Kv) channels open upon membrane depolarization and proceed to spontaneously inactivate. Inactivation controls neuronal firing rates and serves as a form of short-term memory and is implicated in various human neurological disorders. Here, we use high-resolution cryo-electron microscopy and computer simulations to determine one of the molecular mechanisms underlying this physiologically crucial process. Structures of the activated Shaker Kv channel and of its W434F mutant in lipid bilayers demonstrate that C-type inactivation entails the dilation of the ion selectivity filter and the repositioning of neighboring residues known to be functionally critical. Microsecond-scale molecular dynamics trajectories confirm that these changes inhibit rapid ion permeation through the channel. This long-sought breakthrough establishes how eukaryotic K+ channels self-regulate their functional state through the plasticity of their selectivity filters.

Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.

How does a small molecule bind at a cryptic binding site?

Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.

Small-molecule drugs typically function by binding to and modulating the biological activity of their protein targets. Drug-binding sites resemble pockets or grooves on the surface of the target protein, and are generally present even when the drug is not bound. In the case of “cryptic” binding sites, however, the pocket or groove only takes shape during the drug-binding process, prior to which the geometric features of a typical binding site are absent. Cryptic sites commonly occur at protein-protein interfaces, for example, so targeting such sites could facilitate the design of drugs capable of modulating specific protein-protein interactions—an approach with great therapeutic potential. In practice, targeting cryptic sites is typically difficult, in part because much less is known about how small molecules bind to cryptic sites than to conventional sites. In the work reported here, we used molecular dynamics simulations to study the process of a drug binding at a cryptic binding site, and showed that simulations are capable of predicting the location and geometry of a drug binding. The improved understanding of how small molecules bind at cryptic sites afforded by approaches like the one presented here could aid the rational design of small molecules that target such sites.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Bivalent recognition of fatty acyl-CoA by a human integral membrane palmitoyltransferase

Protein palmitoylation is one of the most highly abundant protein modifications, through which long-chain fatty acids get attached to cysteines by a thioester linkage. It plays critically important roles in growth signaling, the organization of synaptic receptors, and the regulation of ion channel function. Yet the molecular mechanism of the DHHC family of integral membrane enzymes that catalyze this modification remains poorly understood. Here, we present the structure of a precatalytic complex of human DHHC20 with palmitoyl CoA. Together with the accompanying functional data, the structure shows how a bivalent recognition of palmitoyl CoA by the DHHC enzyme, simultaneously at both the fatty acyl group and the CoA headgroup, is essential for catalytic chemistry to proceed.

S-acylation, also known as palmitoylation, is the most abundant form of protein lipidation in humans. This reversible posttranslational modification, which targets thousands of proteins, is catalyzed by 23 members of the DHHC family of integral membrane enzymes. DHHC enzymes use fatty acyl-CoA as the ubiquitous fatty acyl donor and become autoacylated at a catalytic cysteine; this intermediate subsequently transfers the fatty acyl group to a cysteine in the target protein. Protein S-acylation intersects with almost all areas of human physiology, and several DHHC enzymes are considered as possible therapeutic targets against diseases such as cancer. These efforts would greatly benefit from a detailed understanding of the molecular basis for this crucial enzymatic reaction. Here, we combine X-ray crystallography with all-atom molecular dynamics simulations to elucidate the structure of the precatalytic complex of human DHHC20 in complex with palmitoyl CoA. The resulting structure reveals that the fatty acyl chain inserts into a hydrophobic pocket within the transmembrane spanning region of the protein, whereas the CoA headgroup is recognized by the cytosolic domain through polar and ionic interactions. Biochemical experiments corroborate the predictions from our structural model. We show, using both computational and experimental analyses, that palmitoyl CoA acts as a bivalent ligand where the interaction of the DHHC enzyme with both the fatty acyl chain and the CoA headgroup is important for catalytic chemistry to proceed. This bivalency explains how, in the presence of high concentrations of free CoA under physiological conditions, DHHC enzymes can efficiently use palmitoyl CoA as a substrate for autoacylation.

Modeling biomolecular kinetics with large-scale simulation

The molecular details of biomolecular kinetics present a challenging estimation problem because the identities of relevant intermediates and the rates of exchange between them must be determined. These can be derived from prior knowledge, but in recent years, great advances have been made in the development and application of methods to systematically determine states and rates using biomolecular simulation. Doing this for biological systems of reasonable complexity requires substantial computational power, and contemporary methods leverage distributed computing or leadership-class computing resources to accomplish this. The result has been substantial insight into pressing contemporary problems, including structural activation of pandemic viruses. Here, we highlight recent developments in both methodology and exciting applications.

Accurate parameterization of the kinetic energy functional for calculations using exact-exchange

Electronic structure calculations based on Kohn-Sham density functional theory (KSDFT) that incorporate exact-exchange or hybrid functionals are associated with a large computational expense, a consequence of the inherent cubic scaling bottleneck and large associated prefactor, which limits the length and time scales that can be accessed. Although orbital-free density functional theory (OFDFT) calculations scale linearly with system size and are associated with a significantly smaller prefactor, they are limited by the absence of accurate density-dependent kinetic energy functionals. Therefore, the development of accurate density-dependent kinetic energy functionals is important for OFDFT calculations of large realistic systems. To this end, we propose a method to train kinetic energy functional models at the exact-exchange level of theory by using a dictionary of physically relevant terms that have been proposed in the literature in conjunction with linear or nonlinear regression methods to obtain the fitting coefficients. For our dictionary, we use a gradient expansion of the kinetic energy nonlocal models proposed in the literature and their nonlinear combinations, such as a model that incorporates spatial correlations between higher order derivatives of electron density at two points. The predictive capabilities of these models are assessed by using a variety of model one-dimensional (1D) systems that exhibit diverse bonding characteristics, such as a chain of eight hydrogens, LiF, LiH, C₄H₂, C₄N₂, and C₃O₂. We show that by using the data from model 1D KSDFT calculations performed using the exact-exchange functional for only a few neutral structures, it is possible to generate models with high accuracy for charged systems and electron and kinetic energy densities during self-consistent field iterations. In addition, we show that it is possible to learn both the orbital dependent terms, i.e., the kinetic energy and the exact-exchange energy, and models that incorporate additional nonlinearities in spatial correlations, such as a quadratic model, are needed to capture subtle features of the kinetic energy density that are present in exact-exchange-based KSDFT calculations.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

Progress in Simulation Studies of Insulin Structure and Function

Insulin is a peptide hormone known for chiefly regulating glucose level in blood among several other metabolic processes. Insulin remains the most effective drug for treating diabetes mellitus. Insulin is synthesized in the pancreatic β-cells where it exists in a compact hexameric architecture although its biologically active form is monomeric. Insulin exhibits a sequence of conformational variations during the transition from the hexamer state to its biologically-active monomer state. The structural transitions and the mechanism of action of insulin have been investigated using several experimental and computational methods. This review primarily highlights the contributions of molecular dynamics (MD) simulations in elucidating the atomic-level details of conformational dynamics in insulin, where the structure of the hormone has been probed as a monomer, dimer, and hexamer. The effect of solvent, pH, temperature, and pressure have been probed at the microscopic scale. Given the focus of this review on the structure of the hormone, simulation studies involving interactions between the hormone and its receptor are only briefly highlighted, and studies on other related peptides (e.g., insulin-like growth factors) are not discussed. However, the review highlights conformational dynamics underlying the activities of reported insulin analogs and mimetics. The future prospects for computational methods in developing promising synthetic insulin analogs are also briefly highlighted.

Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes

Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61:160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5):1106-1116, 2018; Dror et al., Annu Rev Biophys 41:429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry. Experimentally validated MD simulations open a new route towards peptide design starting from sequence and structure and leading to desirable functions.

Initiating Coarse-Grained MD Simulations for Membrane-Bound Proteins

Molecular dynamics (MD) simulations have become a widely used tool in the scientific community for understanding molecular scale phenomena that are challenging to address with wet-lab techniques. Coarse-grained simulations, in which multiple atoms are combined into single beads, allow for larger systems and longer time scales to be explored than atomistic techniques. Here, we describe the procedures and equipment required to set up coarse-grained simulations of membrane-bound proteins in a lipid bilayer that can mimic many membrane environments.

Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation

Protein aggregation can lead to well-defined structures that are functional, but is also the cause of the death of neuron cells in many neurodegenerative diseases. The complexity of the molecular events involved in the aggregation kinetics of amyloid proteins and the transient and heterogeneous characters of all oligomers prevent high-resolution structural experiments. As a result, computer simulations have been used to determine the atomic structures of amyloid proteins at different association stages as well as to understand fibril dissociation. In this chapter, we first review the current computer simulation methods used for aggregation with some atomistic and coarse-grained results aimed at better characterizing the early formed oligomers and amyloid fibril formation. Then we present the applications of non-equilibrium molecular dynamics simulations to comprehend the dissociation of protein assemblies.