Water dispersion interactions strongly influence simulated structural properties of disordered protein states

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules

RNA molecules play a vital role in biological processes within the cell, with significant implications for science and medicine. Notably, the biological functions exerted by specific RNA molecules are often linked to the RNA conformational ensemble. However, the experimental characterization of such three-dimensional RNA structures is challenged by the structural heterogeneity of RNA and by its multiple dynamic interactions with binding partners such as small molecules, proteins, and metal ions. Consequently, our current understanding of the structure-function relationship of RNA molecules is still limited. In this context, we highlight molecular dynamics (MD) simulations as a powerful tool to complement experimental efforts on RNAs. Despite the recognized limitations of current force fields for RNA MD simulations, examining the dynamics of selected RNAs has provided valuable functional insights into their structures.

Comprehensive Assessment of Force-Field Performance in Molecular Dynamics Simulations of DNA/RNA Hybrid Duplexes

Mixed double helices formed by RNA and DNA strands, commonly referred to as hybrid duplexes or hybrids, are essential in biological processes like transcription and reverse transcription. They are also important for their applications in CRISPR gene editing and nanotechnology. Yet, despite their significance, the hybrid duplexes have been seldom modeled by atomistic molecular dynamics methodology, and there is no benchmark study systematically assessing the force-field performance. Here, we present an extensive benchmark study of polypurine tract (PPT) and Dickerson-Drew dodecamer hybrid duplexes using contemporary and commonly utilized pairwise additive and polarizable nucleic acid force fields. Our findings indicate that none of the available force-field choices accurately reproduces all the characteristic structural details of the hybrid duplexes. The AMBER force fields are unable to populate the C3'-endo (north) pucker of the DNA strand and underestimate inclination. The CHARMM force field accurately describes the C3'-endo pucker and inclination but shows base pair instability. The polarizable force fields struggle with accurately reproducing the helical parameters. Some force-field combinations even demonstrate a discernible conflict between the RNA and DNA parameters. In this work, we offer a candid assessment of the force-field performance for mixed DNA/RNA duplexes. We provide guidance on selecting utilizable force-field combinations and also highlight potential pitfalls and best practices for obtaining optimal performance.

Deeper Insight of the Conformational Ensemble of Intrinsically Disordered Proteins

It is generally known that, unlike structured proteins, intrinsically disordered proteins, IDPs, exhibit various structures and conformers, the so-called conformational ensemble, CoE. This study aims to better understand the conformers that make up the IDP ensemble by decomposing the CoE into groups separated by their radius of gyration, Rg. A common approach to studying CoE for IDPs is to use low-resolution techniques, such as small-angle scattering, and combine those with computer simulations on different length scales. Herein, the well-studied antimicrobial saliva protein histatin 5 was utilized as a model peptide for an IDP; the average intensity curves were obtained from small-angle X-ray scattering; and compared with fully atomistic, explicit water, molecular dynamics simulations; then, the intensity curve was decomposed with respect to the different Rg values; and their secondary structure propensities were investigated. We foresee that this approach can provide important information on the CoE and the individual conformers within; in that case, it will serve as an additional tool for understanding the IDP structure-function relationship on a more detailed level.

Charge Relaying within a Phospho-Motif Rescue Binding Competency of a Disordered Transcription Factor

Structural disorder in proteins is central to cellular signaling, where conformational plasticity equips molecules to promiscuously interact with different partners. By engaging with multiple binding partners via the rearrangement of its three helices, the nuclear coactivator binding domain (NCBD) of the CBP/p300 transcription factor is a paradigmatic example of promiscuity. Recently, molecular simulations and experiments revealed that, through the establishment of long-range electrostatic interactions, intended as salt-bridges formed between the post-translationally inserted phosphate and positively charged residues in helix H3 of NCBD, phosphorylation triggers NCBD compaction, lowering its affinity for binding partners. By means of extensive molecular simulations, we here investigated the effect of short-range electrostatics on the conformational ensemble of NCBD, by monitoring the interactions between a phosphorylated serine and conserved positively charged residues within the NCBD phospho-motif. We found that empowering proximal electrostatic interactions, as opposed to long-range electrostatics, can reshape the NCBD ensemble rescuing the binding competency of phosphorylated NCBD. Given the conservation of positive charges in phospho-motifs, proximal electrostatic interactions might dampen the effects of phosphorylation and act as a relay to regulate phosphorylated intrinsically disordered proteins, ultimately tuning the binding affinity for different cellular partners.

Searching for proton transfer channels in respiratory complex I

We have explored a strategy to identify potential proton transfer channels using computational analysis of a protein structure based on Voronoi partitioning and applied it for the analysis of proton transfer pathways in redox-driven proton-pumping respiratory complex I. The analysis results in a network of connected voids/channels, which represent the dual structure of the protein; we then hydrated the identified channels using our water placement program Dowser++. Many theoretical water molecules found in the channels perfectly match the observed experimental water molecules in the structure; some other predicted water molecules have not been resolved in the experiments. The channels are of varying cross sections. Some channels are big enough to accommodate water molecules that are suitable to conduct protons; others are too narrow to hold water but require only minor conformational changes to accommodate proton transfer. We provide a preliminary analysis of the proton conductivity of the network channels, classifying the proton transfer channels as open, closed, and partially open, and discuss possible conformational changes that can modulate, i.e., open and close, the channels.

Novel Insights into the Catalytic Mechanism of Collagenolysis by Zn(II)-Dependent Matrix Metalloproteinase-1

Collagen hydrolysis, catalyzed by Zn(II)-dependent matrix metalloproteinases (MMPs), is a critical physiological process. Despite previous computational investigations into the catalytic mechanisms of MMP-mediated collagenolysis, a significant knowledge gap in understanding remains regarding the influence of conformational sampling and entropic contributions at physiological temperature on enzymatic collagenolysis. In our comprehensive multilevel computational study, employing quantum mechanics/molecular mechanics (QM/MM) metadynamics (MetD) simulations, we aimed to bridge this gap and provide valuable insights into the catalytic mechanism of MMP-1. Specifically, we compared the full enzyme-substrate complex in solution, clusters in solution, and gas-phase to elucidate insights into MMP-1-catalyzed collagenolysis. Our findings reveal significant differences in the catalytic mechanism when considering thermal effects and the dynamic evolution of the system, contrasting with conventional static potential energy surface QM/MM reaction path studies. Notably, we observed a significant stabilization of the critical tetrahedral intermediate, attributed to contributions from conformational flexibility and entropy. Moreover, we found that protonation of the scissile bond nitrogen occurs via proton transfer from a Zn(II)-coordinated hydroxide rather than from a solvent water molecule. Following C-N bond cleavage, the C-terminus remains coordinated to the catalytic Zn(II), while the N-terminus forms a hydrogen bond with a solvent water molecule. Subsequently, the release of the C-terminus is facilitated by the coordination of a water molecule. Our study underscores the pivotal role of protein conformational dynamics at physiological temperature in stabilizing the transition state of the rate-limiting step and key intermediates, compared to the corresponding reaction in solution. These fundamental insights into the mechanism of collagen degradation provide valuable guidance for the development of MMP-1-specific inhibitors.

Accuracy limit of non-polarizable four-point water models: TIP4P/2005 vs OPC. Should water models reproduce the experimental dielectric constant?

The last generation of four center non-polarizable models of water can be divided into two groups: those reproducing the dielectric constant of water, as OPC, and those significantly underestimating its value, as TIP4P/2005. To evaluate the global performance of OPC and TIP4P/2005, we shall follow the test proposed by Vega and Abascal in 2011 evaluating about 40 properties to fairly address this comparison. The liquid-vapor and liquid-solid equilibria are computed, as well as the heat capacities, isothermal compressibilities, surface tensions, densities of different ice polymorphs, the density maximum, equations of state at high pressures, and transport properties. General aspects of the phase diagram are considered by comparing the ratios of different temperatures (namely, the temperature of maximum density, the melting temperature of hexagonal ice, and the critical temperature). The final scores are 7.2 for TIP4P/2005 and 6.3 for OPC. The results of this work strongly suggest that we have reached the limit of what can be achieved with non-polarizable models of water and that the attempt to reproduce the experimental dielectric constant deteriorates the global performance of the water force field. The reason is that the dielectric constant depends on two surfaces (potential energy and dipole moment surfaces), whereas in the absence of an electric field, all properties can be determined simply from just one surface (the potential energy surface). The consequences of the choice of the water model in the modeling of electrolytes in water are also discussed.

Transient Non-local Interactions Dominate the Dynamics of Measles Virus NTAIL

The RNA genome of measles virus is encapsidated by the nucleoprotein within a helical nucleocapsid that serves as template for both transcription and replication. The intrinsically disordered domain of the nucleoprotein (NTAIL), partly protruding outward from the nucleocapsid, is essential for binding the polymerase complex responsible for viral transcription and replication. As for many IDPs, binding of NTAIL occurs through a short molecular recognition element (MoRE) that folds upon binding, with the majority of NTAIL remaining disordered. Though NTAIL regions far from the MoRE influence the binding affinity, interactions between them and the MoRE have not been investigated in depth. Using an integrated approach, relying on photo-induced electron transfer (PET) experiments between tryptophan and cysteine pairs placed at different positions in the protein under varying salt and pH conditions, combined with simulations and analytical models, we identified transient interactions between two disordered regions distant in sequence, which dominate NTAIL dynamics, and regulate the conformational preferences of both the MoRE and the entire NTAIL domain. Co-evolutionary analysis corroborates our findings, and suggests an important functional role for the same intramolecular interactions. We propose mechanisms by which these non-local interactions may regulate binding to the phosphoprotein, polymerase recruitment, and ultimately viral transcription and replication. Our findings may be extended to other IDPs, where non-local intra-protein interactions affect the conformational preferences of intermolecular binding sites.

Nanosecond chain dynamics of single-stranded nucleic acids

The conformational dynamics of single-stranded nucleic acids are fundamental for nucleic acid folding and function. However, their elementary chain dynamics have been difficult to resolve experimentally. Here we employ a combination of single-molecule Förster resonance energy transfer, nanosecond fluorescence correlation spectroscopy, and nanophotonic enhancement to determine the conformational ensembles and rapid chain dynamics of short single-stranded nucleic acids in solution. To interpret the experimental results in terms of end-to-end distance dynamics, we utilize the hierarchical chain growth approach, simple polymer models, and refinement with Bayesian inference to generate structural ensembles that closely align with the experimental data. The resulting chain reconfiguration times are exceedingly rapid, in the 10-ns range. Solvent viscosity-dependent measurements indicate that these dynamics of single-stranded nucleic acids exhibit negligible internal friction and are thus dominated by solvent friction. Our results provide a detailed view of the conformational distributions and rapid dynamics of single-stranded nucleic acids.

Impact of Nonnative Interactions on the Binding Kinetics of Intrinsically Disordered p53 with MDM2: Insights from All-Atom Simulation and Markov State Model Analysis

Intrinsically disordered proteins (IDPs) lack a well-defined tertiary structure but are essential players in various biological processes. Their ability to undergo a disorder-to-order transition upon binding to their partners, known as the folding-upon-binding process, is crucial for their function. One classical example is the intrinsically disordered transactivation domain (TAD) of the tumor suppressor protein p53, which quickly forms a structured α-helix after binding to its partner MDM2, with clinical significance for cancer treatment. However, the contribution of nonnative interactions between the IDP and its partner to the rapid binding kinetics, as well as their interplay with native interactions, is not well understood at the atomic level. Here, we used molecular dynamics simulation and Markov state model (MSM) analysis to study the folding-upon-binding mechanism between p53-TAD and MDM2. Our results suggest that the system progresses from the nascent encounter complex to the well-structured encounter complex and finally reaches the native complex, following an induced-fit mechanism. We found that nonnative hydrophobic and hydrogen bond interactions, combined with native interactions, effectively stabilize the nascent and well-structured encounter complexes. Among the nonnative interactions, Leu25p53-Leu54MDM2 and Leu25p53-Phe55MDM2 are particularly noteworthy, as their interaction strength is close to the optimum. Evidently, strengthening or weakening these interactions could both adversely affect the binding kinetics. Overall, our findings suggest that nonnative interactions are evolutionarily optimized to accelerate the binding kinetics of IDPs in conjunction with native interactions.

O-glycans Expand Lubricin and Attenuate Its Viscosity and Shear Thinning

Lubricin, an intrinsically disordered glycoprotein, plays a pivotal role in facilitating smooth movement and ensuring the enduring functionality of synovial joints. The central domain of this protein serves as a source of this excellent lubrication and is characterized by its highly glycosylated, negatively charged, and disordered structure. However, the influence of O-glycans on the viscosity of lubricin remains unclear. In this study, we employ molecular dynamics simulations in the absence and presence of shear, along with continuum simulations, to elucidate the intricate interplay between O-glycans and lubricin and the impact of O-glycans on lubricin's conformational properties and viscosity. We found the presence of O-glycans to induce a more extended conformation in fragments of the disordered region of lubricin. These O-glycans contribute to a reduction in solution viscosity but at the same time weaken shear thinning at high shear rates, compared to nonglycosylated systems with the same density. This effect is attributed to the steric and electrostatic repulsion between the fragments, which prevents their conglomeration and structuring. Our computational study yields a mechanistic mechanism underlying previous experimental observations of lubricin and paves the way to a more rational understanding of its function in the synovial fluid.

Atomistic insights into the reentrant phase-transitions in polyuracil and polylysine mixtures

The phase separation of protein and RNA mixtures underpins the assembly and regulation of numerous membraneless organelles in cells. The ubiquity of protein-RNA condensates in cellular regulatory processes is in part due to their sensitivity to RNA concentration, which affects their physical properties and stability. Recent experiments with poly-cationic peptide-RNA mixtures have revealed closed-loop phase diagrams featuring lower and upper critical solution temperatures. These diagrams indicate reentrant phase transitions shaped by biomolecular interactions and entropic forces such as solvent and ion reorganization. We employed atomistic simulations to study mixtures with various RNA-polylysine stoichiometries and temperatures to elucidate the microscopic driving forces behind reentrant phase transitions in protein-RNA mixtures. Our findings reveal an intricate interplay between hydration, ion condensation, and specific RNA-polylysine hydrogen bonding, resulting in distinct stoichiometry-dependent phase equilibria governing stabilities and structures of the condensate phase. Our simulations show that reentrant transitions are accompanied by desolvation around the phosphate groups of RNA, with increased contacts between phosphate and lysine side chains. In RNA-rich systems at lower temperatures, RNA molecules can form an extensive pi-stacking and hydrogen bond network, leading to percolation. In protein-rich systems, no such percolation-induced transitions are observed. Furthermore, we assessed the performance of three prominent water force fields-Optimal Point Charge (OPC), TIP4P-2005, and TIP4P-D-in capturing reentrant phase transitions. OPC provided a superior balance of interactions, enabling effective capture of reentrant transitions and accurate characterization of changes in solvent reorganization. This study offers atomistic insights into the nature of reentrant phase transitions using simple model peptide and nucleotide mixtures. We believe that our results are broadly applicable to larger classes of peptide-RNA mixtures exhibiting reentrant phase transitions.

Intrinsic Conformational Dynamics of Glycine and Alanine in Polarizable Molecular Dynamics Force Fields: Comparison to Spectroscopic Data

Molecular dynamics (MD) is a great tool for elucidating conformational dynamics of proteins and peptides in water at the atomistic level that often surpasses the level of detail available experimentally. Structure predictions, however, are limited by the accuracy of the underlying MD force field. This limitation is particularly stark in the case of intrinsically disordered peptides and proteins, which are characterized by solvent-accessible and disordered peptide regions and domains. Recent studies show that most additive MD force fields, including CHARMM36m, do not reproduce the intrinsic conformational distributions of guest amino acid residues x in cationic GxG peptides in water in line with experimental data. Positing that a lack of polarizability in additive MD force fields may be the culprit for the reported discrepancies, we here examine the conformational dynamics of guest glycine and alanine residues in cationic GxG peptides in water using two polarizable MD force fields, CHARMM Drude and AMOEBA. Our results indicate that while AMOEBA captures the experimental data better than CHARMM Drude, neither of the two polarizable force fields offers an improvement of the Ramachandran distributions of glycine and alanine residues in cationic GGG and GAG peptides, respectively, over CHARMM36m.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 μs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign

Enzymes exhibit diverse conformations, as represented in the free energy landscape (FEL). Such conformational diversity provides enzymes with the ability to evolve towards novel functions. The challenge lies in identifying mutations that enhance specific conformational changes, especially if located in distal sites from the active site cavity. The shortest path map (SPM) method, which we developed to address this challenge, constructs a graph based on the distances and correlated motions of residues observed in nanosecond timescale molecular dynamics (MD) simulations. We recently introduced a template based AlphaFold2 (tAF2) approach coupled with 10 nanosecond MD simulations to quickly estimate the conformational landscape of enzymes and assess how the FEL is shifted after mutation. In this study, we evaluate the potential of SPM when coupled with tAF2-MD in estimating conformational heterogeneity and identifying key conformationally-relevant positions. The selected model system is the beta subunit of tryptophan synthase (TrpB). We compare how the SPM pathways differ when integrating tAF2 with different MD simulation lengths from as short as 10 ns until 50 ns and considering two distinct Amber forcefield and water models (ff14SB/TIP3P versus ff19SB/OPC). The new methodology can more effectively capture the distal mutations found in laboratory evolution, thus showcasing the efficacy of tAF2-MD-SPM in rapidly estimating enzyme dynamics and identifying the key conformationally relevant hotspots for computational enzyme engineering.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-β (Aβ42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the β-content of Aβ42; however, it increases the tendency of the monomeric Aβ42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aβ42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aβ42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.

Elucidating the Membrane Binding Process of a Disordered Protein: Dynamic Interplay of Anionic Lipids and the Polybasic Region

Intrinsically disordered regions of proteins are responsible for many biological processes such as in the case of liver kinase B1 (LKB1)-a serine/threonine kinase relevant for cell proliferation and cell polarity. LKB1 becomes fully activated upon recruitment to the plasma membrane by binding of its disordered C-terminal polybasic motif consisting of eight lysines/arginines to phospholipids. Here, we present extensive molecular dynamics (MD) simulations of the polybasic motif interacting with a model membrane composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1-palmitoyl-2-oleyl phosphatidic acid (PA) and cell culture experiments. Protein-membrane binding effects are due to the electrostatic interactions between the polybasic amino acids and PAs. For significant binding, the first three lysines turn out to be dispensable, which was also recapitulated in cell culture using transfected GFP-LKB1 variants. LKB1-membrane binding results in nonmonotonous changes in the structure of the protein as well as the membrane, in particular, accumulation of PAs and reduced thickness at the protein-membrane contact area. The protein-lipid binding turns out to be highly dynamic due to an interplay of PA-PA repulsion and protein-PA attraction. The thermodynamics of this interplay is captured by a statistical fluctuation model, which allows the estimation of both energies. Quantification of the significance of each polar amino acid in the polybasic provides detailed insights into the molecular mechanism of protein-membrane binding of LKB1. These results can likely be transferred to other proteins, which interact by intrinsically disordered polybasic regions with anionic membranes.

Cyclization Scaffolding for Improved Vaccine Immunogen Stability: Application to Tau Protein in Alzheimer's Disease

Effective scaffolding of immunogens is crucial for generating conformationally selective antibodies through active immunization, particularly in the treatment of protein misfolding diseases such as Alzheimer's and Parkinson's disease. Previous computational work has revealed that a disorder-prone region of the tau protein, when in a stacked form, is predicted to structurally resemble a small, soluble protofibril, having conformational properties similar to those of experimental in vitro tau oligomers. Such an oligomeric structural mimic has the potential to serve as a vaccine immunogen design for Alzheimer's disease. In this study, we developed a cyclization scaffolding method in Rosetta, in which multiple cyclic peptides are stacked into a protofibril. Cyclization results in significant stabilization of protofibril-like structures by constraining the conformational space. Applying this method to the disorder-prone region of the tau fibril, we evaluated the metastability of the cyclized tau immunogen using molecular dynamics simulations, and we identified sequences of two cyclic constructs having high metastability in the protofibril. We then assessed their thermodynamic stability by computing the free energy required to separate a distal chain from the rest of the stacked structure. Our computational results, based on molecular dynamics simulations and free energy calculations, demonstrate that two cyclized constructs, cyclo-(VKSEKLDFKDRVQSKIFyN) and cyclo-(VKSEKLDFKDRVQSKIYvG) (lowercase letters indicate d-form amino acids), possess significantly increased thermodynamic stability in the protofibril over an uncyclized linear construct VKSEKLDFKDRVQSKI. The cyclization scaffolding approach proposed here holds promise as a means to effectively design immunogens for protein misfolding diseases, particularly those involving liposome-conjugated peptide constructs.

Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.

Hierarchical Assembly of Single-Stranded RNA

Single-stranded RNA (ssRNA) plays a major role in the flow of genetic information-most notably, in the form of messenger RNA (mRNA)-and in the regulation of biological processes. The highly dynamic nature of chains of unpaired nucleobases challenges structural characterizations of ssRNA by experiments or molecular dynamics (MD) simulations alike. Here, we use hierarchical chain growth (HCG) to construct ensembles of ssRNA chains. HCG assembles the structures of protein and nucleic acid chains from fragment libraries created by MD simulations. Applied to homo- and heteropolymeric ssRNAs of different lengths, we find that HCG produces structural ensembles that overall are in good agreement with diverse experiments, including nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and single-molecule Förster resonance energy transfer (FRET). The agreement can be further improved by ensemble refinement using Bayesian inference of ensembles (BioEn). HCG can also be used to assemble RNA structures that combine base-paired and base-unpaired regions, as illustrated for the 5' untranslated region (UTR) of SARS-CoV-2 RNA.

Simulations of Functional Motions of Super Large Biomolecules with a Mixed-Resolution Model

Many large protein machines function through an interplay between large-scale movements and intricate conformational changes. Understanding functional motions of these proteins through simulations becomes challenging for both all-atom and coarse-grained (CG) modeling techniques because neither approach alone can readily capture the full details of these motions. In this study, we develop a multiscale model by employing the popular MARTINI CG model to represent a heterogeneous environment and structurally stable proteins and using the united-atom (UA) model PACE to describe proteins undergoing subtle conformational changes. PACE was previously developed to be compatible with the MARTINI solvent and membrane. Here, we couple the protein descriptions of the two models by directly mixing UA and CG interaction parameters to greatly simplify parameter determination. Through extensive validations with diverse protein systems in solution or membrane, we demonstrate that only additional parameter rescaling is needed to enable the resulting model to recover the stability of native structures of proteins under mixed representation. Moreover, we identify the optimal scaling factors that can be applied to various protein systems, rendering the model potentially transferable. To further demonstrate its applicability for realistic systems, we apply the model to a mechanosensitive ion channel Piezo1 that has peripheral arms for sensing membrane tension and a central pore for ion conductance. The model can reproduce the coupling between Piezo1's large-scale arm movement and subtle pore opening in response to membrane stress while consuming much less computational costs than all-atom models. Therefore, our model shows promise for studying functional motions of large protein machines.

Temperature-dependent elasticity of DNA, RNA, and hybrid double helices

Nucleic acid double helices in their DNA, RNA, and DNA-RNA hybrid form play a fundamental role in biology and are main building blocks of artificial nanostructures, but how their properties depend on temperature remains poorly understood. Here, we report thermal dependence of dynamic bending persistence length, twist rigidity, stretch modulus, and twist-stretch coupling for DNA, RNA, and hybrid duplexes between 7°C and 47°C. The results are based on all-atom molecular dynamics simulations using different force field parameterizations. We first demonstrate that unrestrained molecular dynamics can reproduce experimentally known mechanical properties of the duplexes at room temperature. Beyond experimentally known features, we also infer the twist rigidity and twist-stretch coupling of the hybrid duplex. As for the temperature dependence, we found that increasing temperature softens all the duplexes with respect to bending, twisting, and stretching. The relative decrease of the stretch moduli is 0.003-0.004/°C, similar for all the duplex variants despite their very different stretching stiffness, whereas RNA twist stiffness decreases by 0.003/°C, and smaller values are found for the other elastic moduli. The twist-stretch couplings are nearly unaffected by temperature. The stretching, bending, and twisting stiffness all include an important entropic component. Relation of our results to the two-state model of DNA flexibility is discussed. Our work provides temperature-dependent elasticity of nucleic acid duplexes at the microsecond scale relevant for initial stages of protein binding.

The molecular basis for cellular function of intrinsically disordered protein regions

Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.

Domain coupling in activation of a family C GPCR

The G protein-coupled metabotropic glutamate receptors form homodimers and heterodimers with highly diverse responses to glutamate and varying physiological function. The molecular basis for this diversity remains poorly delineated. We employ molecular dynamics, single-molecule spectroscopy, and hydrogen-deuterium exchange to dissect the pathway of activation triggered by glutamate. We find that activation entails multiple loosely coupled steps and identify a novel pre-active intermediate whose transition to the active state forms dimer interactions that set signaling efficacy. Such subunit interactions generate functional diversity that differs across homodimers and heterodimers. The agonist-bound receptor is remarkably dynamic, with low occupancy of G protein-coupling conformations, providing considerable headroom for modulation of the landscape by allosteric ligands. Sites of sequence diversity within the dimerization interface and diverse coupling between activation rearrangements may contribute to precise decoding of glutamate signals and transients over broad spatial and temporal scales.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning-based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long timescale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We observe that folding-upon-binding predominantly proceeds through a multi-step induced fit mechanism with several intermediates and do not find evidence for the existence of canonical conformational selection pathways. We observe four kinetically separated native-like bound states that interconvert on timescales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

Energy, water, and protein folding: A molecular dynamics-based quantitative inventory of molecular interactions and forces that make proteins stable

Protein folding energetics can be determined experimentally on a case-by-case basis but it is not understood in sufficient detail to provide deep control in protein design. The fundamentals of protein stability have been outlined by calorimetry, protein engineering, and biophysical modeling, but these approaches still face great difficulty in elucidating the specific contributions of the intervening molecules and physical interactions. Recently, we have shown that the enthalpy and heat capacity changes associated to the protein folding reaction can be calculated within experimental error using molecular dynamics simulations of native protein structures and their corresponding unfolded ensembles. Analyzing in depth molecular dynamics simulations of four model proteins (CI2, barnase, SNase, and apoflavodoxin), we dissect here the energy contributions to ΔH (a key component of protein stability) made by the molecular players (polypeptide and solvent molecules) and physical interactions (electrostatic, van der Waals, and bonded) involved. Although the proteins analyzed differ in length, isoelectric point and fold class, their folding energetics is governed by the same quantitative pattern. Relative to the unfolded ensemble, the native conformations are enthalpically stabilized by comparable contributions from protein-protein and solvent-solvent interactions, and almost equally destabilized by interactions between protein and solvent molecules. The native protein surface seems to interact better with water than the unfolded one, but this is outweighed by the unfolded surface being larger. From the perspective of physical interactions, the native conformations are stabilized by van de Waals and Coulomb interactions and destabilized by conformational strain arising from bonded interactions. Also common to the four proteins, the sign of the heat capacity change is set by interactions between protein and solvent molecules or, from the alternative perspective, by Coulomb interactions.

The intrinsically disordered transcriptional activation domain of CIITA is functionally tuneable by single substitutions: An exception or a new paradigm?

During protein evolution, some amino acid substitutions modulate protein function ("tuneability"). In most proteins, the tuneable range is wide and can be sampled by a set of protein variants that each contains multiple amino acid substitutions. In other proteins, the full tuneable range can be accessed by a set of variants that each contains a single substitution. Indeed, in some globular proteins, the full tuneable range can be accessed by the set of site-saturating substitutions at an individual "rheostat" position. However, in proteins with intrinsically disordered regions (IDRs), most functional studies-which would also detect tuneability-used multiple substitutions or small deletions. In disordered transcriptional activation domains (ADs), studies with multiple substitutions led to the "acidic exposure" model, which does not anticipate the existence of rheostat positions. In the few studies that did assess effects of single substitutions on AD function, results were mixed: the ADs of two full-length transcription factors did not show tuneability, whereas a fragment of a third AD was tuneable by single substitutions. In this study, we tested tuneability in the AD of full-length human class II transactivator (CIITA). Sequence analyses and experiments showed that CIITA's AD is an IDR. Functional assays of singly-substituted AD variants showed that CIITA's function was highly tuneable, with outcomes not predicted by the acidic exposure model. Four tested positions showed rheostat behavior for transcriptional activation. Thus, tuneability of different IDRs can vary widely. Future studies are needed to illuminate the biophysical features that govern whether an IDR is tuneable by single substitutions.

Temperature-Dependent Twist of Double-Stranded RNA Probed by Magnetic Tweezer Experiments and Molecular Dynamics Simulations

RNA plays critical roles in the transmission and regulation of genetic information and is increasingly used in biomedical and biotechnological applications. Functional RNAs contain extended double-stranded regions, and the structure of double-stranded RNA (dsRNA) has been revealed at high resolution. However, the dependence of the properties of the RNA double helix on environmental effects, notably temperature, is still poorly understood. Here, we use single-molecule magnetic tweezer measurements to determine the dependence of the dsRNA twist on temperature. We find that dsRNA unwinds with increasing temperature, even more than DNA, with ΔTwRNA = -14.4 ± 0.7°/(°C·kbp), compared to ΔTwDNA = -11.0 ± 1.2°/(°C·kbp). All-atom molecular dynamics (MD) simulations using a range of nucleic acid force fields, ion parameters, and water models correctly predict that dsRNA unwinds with rising temperature but significantly underestimate the magnitude of the effect. These MD data, together with additional MD simulations involving DNA and DNA-RNA hybrid duplexes, reveal a linear correlation between the twist temperature decrease and the helical rise, in line with DNA but at variance with RNA experimental data. We speculate that this discrepancy might be caused by some unknown bias in the RNA force fields tested or by as yet undiscovered transient alternative structures in the RNA duplex. Our results provide a baseline to model more complex RNA assemblies and to test and develop new parametrizations for RNA simulations. They may also inspire physical models of the temperature-dependent dsRNA structure.

Improved Prediction of Molecular Response to Pulling by Combining Force Tempering with Replica Exchange Methods

Small mechanical forces play important functional roles in many crucial cellular processes, including in the dynamic behavior of the cytoskeleton and in the regulation of osmotic pressure through membrane-bound proteins. Molecular simulations offer the promise of being able to design the behavior of proteins that sense and respond to these forces. However, it is difficult to predict and identify the effect of the relevant piconewton (pN) scale forces due to their small magnitude. Previously, we introduced the Infinite Switch Simulated Tempering in Force (FISST) method, which allows one to estimate the effect of a range of applied forces from a single molecular dynamics simulation, and also demonstrated that FISST additionally accelerates sampling of a molecule's conformational landscape. For some problems, we find that this acceleration is not sufficient to capture all relevant conformational fluctuations, and hence, here we demonstrate that FISST can be combined with either temperature replica exchange or solute tempering approaches to produce a hybrid method that enables more robust prediction of the effect of small forces on molecular systems.

Adenosine Triphosphate Mediates Phase Separation of Disordered Basic Proteins by Bridging Intermolecular Interaction Networks

Adenosine triphosphate (ATP) is an abundant molecule with crucial cellular roles as the energy currency and a building block of nucleic acids and for protein phosphorylation. Here we show that ATP mediates the phase separation of basic intrinsically disordered proteins (bIDPs). In the resulting condensates, ATP is highly concentrated (apparent partition coefficients up to 7700) and serves as bridges between bIDP chains. These liquid-like droplets have some of the lowest interfacial tension (∼25 pN/μm) but high zero-shear viscosities (1-15 Pa s) due to the bridged protein networks, and yet their fusion has some of the highest speeds (∼1 μm/ms). The rapid fusion manifests extreme shear thinning, where the apparent viscosity is lower than zero-shear viscosity by over 100-fold, made possible by fast reformation of the ATP bridges. At still higher concentrations, ATP does not dissolve bIDP droplets but results in aggregates and fibrils.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Excited States of apo-Guanidine-III Riboswitch Contribute to Guanidinium Binding through Both Conformational and Induced-Fit Mechanisms

Riboswitches are mRNA segments that regulate gene expression through conformational changes driven by their cognate ligand binding. The ykkC motif forms a riboswitch class that selectively senses a guanidinium ion (Gdm+) and regulates the downstream expression of proteins which aid in the efflux of excess Gdm+ from the cells. The aptamer domain (AD) of the guanidine-III riboswitch forms an H-type pseudoknot with a triple helical domain that binds a Gdm+. We studied the binding of Gdm+ to the AD of the guanidine (ykkC)-III riboswitch using computer simulations to probe the specificity of the riboswitch to Gdm+ binding. We show that Gdm+ binding is a fast process occurring on the nanosecond time scale, with minimal conformational changes to the AD. Using machine learning and Markov-state models, we identified the excited conformational states of the AD, which have a high Gdm+ binding propensity, making the Gdm+ binding landscape complex exhibiting both conformational selection and induced-fit mechanisms. The proposed apo-AD excited states and their role in the ligand-sensing mechanism are amenable to experimental verification. Further, targeting these excited-state conformations in discovering new antibiotics can be explored.

Using NMR diffusion data to validate MD models of disordered proteins: Test case of N-terminal tail of histone H4

MD simulations can provide uniquely detailed models of intrinsically disordered proteins (IDPs). However, these models need careful experimental validation. The coefficient of translational diffusion Dtr, measurable by pulsed field gradient NMR, offers a potentially useful piece of experimental information related to the compactness of the IDP's conformational ensemble. Here, we investigate, both experimentally and via the MD modeling, the translational diffusion of a 25-residue N-terminal fragment from histone H4 (N-H4). We found that the predicted values of Dtr, as obtained from mean-square displacement of the peptide in the MD simulations, are largely determined by the viscosity of the MD water (which has been reinvestigated as a part of our study). Beyond that, our analysis of the diffusion data indicates that MD simulations of N-H4 in the TIP4P-Ew water give rise to an overly compact conformational ensemble for this peptide. In contrast, TIP4P-D and OPC simulations produce the ensembles that are consistent with the experimental Dtr result. These observations are supported by the analyses of the 15N spin relaxation rates. We also tested a number of empirical methods to predict Dtr based on IDP's coordinates extracted from the MD snapshots. In particular, we show that the popular approach involving the program HYDROPRO can produce misleading results. This happens because HYDROPRO is not intended to predict the diffusion properties of highly flexible biopolymers such as IDPs. Likewise, recent empirical schemes that exploit the relationship between the small-angle x-ray scattering-informed conformational ensembles of IDPs and the respective experimental Dtr values also prove to be problematic. In this sense, the first-principle calculations of Dtr from the MD simulations, such as demonstrated in this work, should provide a useful benchmark for future efforts in this area.

Characterization of Posttranslationally Modified PHF-1 Tau Peptides Using Gaussian Accelerated Molecular Dynamics Simulation

The microtubule-associated protein, Tau, is an intrinsically disordered protein that plays a crucial role in neurodegenerative diseases like Alzheimer's disease. The posttranslational modifications across the Tau protein domains are involved in regulating Tau protein's function and disease onset. Of the various posttranslational modifications at Ser, Thr, and Tyr sites, O-GlcNAcylation and phosphorylation are the most critical ones, playing a vital role in Tau aggregation and tauopathies. To understand the function, it is essential to characterize the structural changes associated with Tau modification. Previous experimental studies have focused on high-resolution nuclear magnetic resonance techniques to structurally characterize the effect of phosphorylation, O-GlcNAcylation, and combination of both PTMs on Tau conformation in small peptides centered on the PHF-1 epitope from amino acid 392 to 411. The structural characterization using atomistic molecular dynamics simulation of such disordered peptides requires long simulation time, proper sampling method, and utilization of appropriate force fields for accurate determination of conformational ensembles, resembling the experimental data. This chapter details the protocol for the structural characterization of modified Tau peptides using the CHARMM36m force field and enhanced sampling methods like Gaussian accelerated molecular dynamics (GaMD) simulation. We have focused on a detailed explanation of the GaMD method and analyses of molecular dynamics trajectories to explain the relationship between two modifications, phospho- and glyco-, at C-terminus of Tau protein and its stable conformation over the longer simulation timeframes. The analyses involve energetics reweighting, clustering of simulation trajectories, and characterization of secondary structure using circular dichroism data from the simulation. The reader can utilize this protocol to investigate the structures of complex proteins, especially the disordered ones.

Prediction of interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins from dihedral angles

Intrinsically disordered proteins (IDPs) are proteins that do not form uniquely defined three-dimensional (3-D) structures. Experimental research on IDPs is difficult since they go against the traditional protein structure-function paradigm. Although there are several predictors of disorder based on amino acid sequences, but very limited based on the 3-D structures of proteins. Dihedral angles have a significant role in predicting protein structure because they establish a protein's backbone, which, coupled with its side chain, establishes its overall shape. Here, we have carried out atomistic Molecular Dynamics (MD) simulations on four different proteins: one ordered protein (Monellin), two partially disordered proteins (p53-TAD and Amyloid beta (Aβ1-42) peptide), and one completely disordered protein (Histatin 5). The MD simulation trajectories for the corresponding four proteins were used to conduct dihedral angle (ϕ and ѱ) analysis. Then, the average dihedral angles for each of the residues were calculated and plotted against the residue index. We noticed steep rises or falls in the average ϕ value at certain locations in the plot. These sudden shifts in the average ϕ value reflect the interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins. Using this method, the probable conformer of a protein with a higher degree of disorder can be found among the ensembles of structures sampled during the MD simulations. The results of our study offer new understandings on precisely identifying regions of various degrees of disorder in intrinsically disordered proteins.Communicated by Ramaswamy H. Sarma.

Improved Protein Model in SPICA Force Field

The previous version of the SPICA coarse-grained (CG) force field (FF) protein model focused primarily on membrane proteins and successfully reproduced the dimerization free energies of several transmembrane helices and the stable structures of various membrane protein assemblies. However, that model had limited accuracy when applied to other proteins, such as intrinsically disordered proteins (IDPs) and peripheral proteins, because the dimensions of the IDPs in an aqueous solution were too compact, and protein binding on the lipid membrane surface was overstabilized. To improve the accuracy of the SPICA FF model for the simulation of such systems, in this study, we introduce protein secondary structure-dependent nonbonded interaction parameters to the backbone segments and reoptimize almost all nonbonded parameters for amino acids. The improved FF proposed here successfully reproduces the radii of gyration of various IDPs, the binding sensitivity of several peripheral membrane proteins, and the dimerization free energies of several transmembrane helices. The new model also shows improved agreement with experiments on the free energy of peptide association in water. In addition, an extensive library of nonbonded interactions between proteins and lipids, including various glycerophospholipids, sphingolipids, and cholesterol, allows the study of specific interactions between lipids and peripheral and transmembrane proteins. Hence, the new SPICA FF (version 2) proposed herein is applicable with high accuracy for simulating a wide range of protein systems.

Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data

Biological macromolecules in solution are surrounded by a hydration shell, whose structure differs from the structure of bulk solvent. While the importance of the hydration shell for numerous biological functions is widely acknowledged, it remains unknown how the hydration shell is regulated by macromolecular shape and surface composition, mainly because a quantitative probe of the hydration shell structure has been missing. We show that small-angle scattering in solution using X-rays (SAXS) or neutrons (SANS) provide a protein-specific probe of the protein hydration shell that enables quantitative comparison with molecular simulations. Using explicit-solvent SAXS/SANS predictions, we derived the effect of the hydration shell on the radii of gyration Rg of five proteins using 18 combinations of protein force field and water model. By comparing computed Rg values from SAXS relative to SANS in D2O with consensus SAXS/SANS data from a recent worldwide community effort, we found that several but not all force fields yield a hydration shell contrast in remarkable agreement with experiments. The hydration shell contrast captured by Rg values depends strongly on protein charge and geometric shape, thus providing a protein-specific footprint of protein-water interactions and a novel observable for scrutinizing atomistic hydration shell models against experimental data.

An extended Tudor domain within Vreteno interconnects Gtsf1L and Ago3 for piRNA biogenesis in Bombyx mori

Piwi-interacting RNAs (piRNAs) direct PIWI proteins to transposons to silence them, thereby preserving genome integrity and fertility. The piRNA population can be expanded in the ping-pong amplification loop. Within this process, piRNA-associated PIWI proteins (piRISC) enter a membraneless organelle called nuage to cleave their target RNA, which is stimulated by Gtsf proteins. The resulting cleavage product gets loaded into an empty PIWI protein to form a new piRISC complex. However, for piRNA amplification to occur, the new RNA substrates, Gtsf-piRISC, and empty PIWI proteins have to be in physical proximity. In this study, we show that in silkworm cells, the Gtsf1 homolog BmGtsf1L binds to piRNA-loaded BmAgo3 and localizes to granules positive for BmAgo3 and BmVreteno. Biochemical assays further revealed that conserved residues within the unstructured tail of BmGtsf1L directly interact with BmVreteno. Using a combination of AlphaFold modeling, atomistic molecular dynamics simulations, and in vitro assays, we identified a novel binding interface on the BmVreteno-eTudor domain, which is required for BmGtsf1L binding. Our study reveals that a single eTudor domain within BmVreteno provides two binding interfaces and thereby interconnects piRNA-loaded BmAgo3 and BmGtsf1L.

Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations

Small-angle X-ray and neutron scattering (SAXS/SANS) provide valuable insights into the structure and dynamics of biomolecules in solution, complementing a wide range of structural techniques, including molecular dynamics simulations. As contrast-based methods, they are sensitive not only to structural properties but also to solvent-solute interactions. Their use in molecular dynamics simulations requires a forward model that should be as fast and accurate as possible. In this work, we demonstrate the feasibility of calculating SAXS and SANS intensities using a coarse-grained representation consisting of one bead per amino acid and three beads per nucleic acid, with form factors that can be corrected on the fly to account for solvation effects at no additional computational cost. By coupling this forward model with molecular dynamics simulations restrained with SAS data, it is possible to determine conformational ensembles or refine the structure and dynamics of proteins and nucleic acids in agreement with the experimental results. To assess the robustness of this approach, we applied it to gelsolin, for which we acquired SAXS data on its closed state, and to a UP1-microRNA complex, for which we used previously collected measurements. Our hybrid-resolution small-angle scattering (hySAS) implementation, being distributed in PLUMED, can be used with atomistic and coarse-grained simulations using diverse restraining strategies.

Conformational and Interaction Landscape of Histone H4 Tails in Nucleosomes Probed by Paramagnetic NMR Spectroscopy

The fundamental repeat unit of chromatin, the nucleosome, consists of approximately 147 base pairs of double-stranded DNA and a histone protein octamer containing two copies each of histones H2A, H2B, H3, and H4. Each histone possesses a dynamically disordered N-terminal tail domain, and it is well-established that the tails of histones H3 and H4 play key roles in chromatin compaction and regulation. Here we investigate the conformational ensemble and interactions of the H4 tail in nucleosomes by means of solution NMR measurements of paramagnetic relaxation enhancements (PREs) in recombinant samples reconstituted with 15N-enriched H4 and nitroxide spin-label tagged H3. The experimental PREs, which report on the proximities of individual H4 tail residues to the different H3 spin-label sites, are interpreted by using microsecond time-scale molecular dynamics simulations of the nucleosome core particle. Collectively, these data enable improved localization of histone H4 tails in nucleosomes and support the notion that H4 tails engage in a fuzzy complex interaction with nucleosomal DNA.

The stability and dynamics of the Aβ40/Aβ42 interlaced mixed fibrils

The accumulation of fibrillar amyloid-β (Aβ) aggregates in the brain, predominantly comprising 40- and 42-residue amyloid-β (Aβ40 and Aβ42), is a major pathological hallmark of Alzheimer's disease (AD). Aβ40 and Aβ42 naturally coexist in the brain under normal physiological conditions, and their interplay is generally considered to be a critical factor in the progression of AD. In addition to forming homogeneous oligomers and fibrils, Aβ40 and Aβ42 are also reported to co-assemble into hetero-oligomers and interlaced mixed fibrils, as evidenced by solid-state nuclear magnetic resonance spectroscopy (NMR), high molecular weight mass spectrometry and cross-seeding experiments. However, the exact molecular mechanisms underlying these processes remain unclear. In this study, we have used a recently resolved structurally uniform 1:1 mixture of Aβ40/Aβ42 interlaced mixed fibril as a prototype to gain insights into the molecular-level interactions between Aβ40 and Aβ42. We employed fully atomistic molecular dynamics simulation and compared the results with a homogeneous U-shaped Aβ40 fibrillar model. Our simulations using two different force fields provide conclusive evidence that the Aβ40/Aβ42 interlaced mixed fibril is energetically more favorable than the homogeneous Aβ40 fibrillar model. Furthermore, we also show that the increase in stability observed in the mixed model stems primarily from the packing interfaces and the stacking interfaces between C-termini. Our simulation results provide valuable mechanistic insights that are not readily accessible in experiment and could have significant implications for both the pathogenesis of AD and the development of current therapeutic strategies.Communicated by Ramaswamy H. Sarma.

Accurately Predicting Protein pKa Values Using Nonequilibrium Alchemy

The stability, solubility, and function of a protein depend on both its net charge and the protonation states of its individual residues. pKa is a measure of the tendency for a given residue to (de)protonate at a specific pH. Although pKa values can be resolved experimentally, theory and computation provide a compelling alternative. To this end, we assess the applicability of a nonequilibrium (NEQ) alchemical free energy method to the problem of pKa prediction. On a data set of 144 residues that span 13 proteins, we report an average unsigned error of 0.77 ± 0.09, 0.69 ± 0.09, and 0.52 ± 0.04 pK for aspartate, glutamate, and lysine, respectively. This is comparable to current state-of-the-art predictors and the accuracy recently reached using free energy perturbation methods (e.g., FEP+). Moreover, we demonstrate that our open-source, pmx-based approach can accurately resolve the pKa values of coupled residues and observe a substantial performance disparity associated with the lysine partial charges in Amber14SB/Amber99SB*-ILDN, for which an underused fix already exists.

The role of flow in the self-assembly of dragline spider silk proteins

Hydrodynamic flow in the spider duct induces conformational changes in dragline spider silk proteins (spidroins) and drives their assembly, but the underlying physical mechanisms are still elusive. Here we address this challenging multiscale problem with a complementary strategy of atomistic and coarse-grained molecular dynamics simulations with uniform flow. The conformational changes at the molecular level were analyzed for single-tethered spider silk peptides. Uniform flow leads to coiled-to-stretch transitions and pushes alanine residues into β sheet and poly-proline II conformations. Coarse-grained simulations of the assembly process of multiple semi-flexible block copolymers using multi-particle collision dynamics reveal that the spidroins aggregate faster but into low-order assemblies when they are less extended. At medium-to-large peptide extensions (50%-80%), assembly slows down and becomes reversible with frequent association and dissociation events, whereas spidroin alignment increases and alanine repeats form ordered regions. Our work highlights the role of flow in guiding silk self-assembly into tough fibers by enhancing alignment and kinetic reversibility, a mechanism likely relevant also for other proteins whose function depends on hydrodynamic flow.

Mechanism of Fluoride Ion Encapsulation by Magnesium Ions in a Bacterial Riboswitch

Riboswitches sense various ions in bacteria and activate gene expression to synthesize proteins that help maintain ion homeostasis. The crystal structure of the aptamer domain (AD) of the fluoride riboswitch shows that the F- ion is encapsulated by three Mg2+ ions bound to the ligand-binding domain (LBD) located at the core of the AD. The assembly mechanism of this intricate structure is unknown. To this end, we performed computer simulations using coarse-grained and all-atom RNA models to bridge multiple time scales involved in riboswitch folding and ion binding. We show that F- encapsulation by the Mg2+ ions bound to the riboswitch involves multiple sequential steps. Broadly, two Mg2+ ions initially interact with the phosphate groups of the LBD using water-mediated outer-shell coordination and transition to a direct inner-shell interaction through dehydration to strengthen their interaction with the LBD. We propose that the efficient binding mode of the third Mg2+ and F- is that they form a water-mediated ion pair and bind to the LBD simultaneously to minimize the electrostatic repulsion between three Mg2+ bound to the LBD. The tertiary stacking interactions among the LBD nucleobases alone are insufficient to stabilize the alignment of the phosphate groups to facilitate Mg2+ binding. We show that the stability of the whole assembly is an intricate balance of the interactions among the five phosphate groups, three Mg2+, and the encapsulated F- ion aided by the Mg2+ solvated water. These insights are helpful in the rational design of RNA-based ion sensors and fast-switching logic gates.

GaMD simulations as an alternative in the TFE-water mixture description

CONTEXT

2,2,2-Trifluoroethanol has been widely used to study the structure and dynamic properties of intrinsically disordered proteins. Experimentally, it is known that TFE-water mixtures stabilize secondary structures of IDPs, and therefore, it allows the studying of conformational ensembles of these proteins. In the last decades, molecular dynamic simulations have helped study the IDPs' conformational ensemble. Unfortunately, conventional MD requires very long simulation times to describe the properties of IDPs. Therefore, a variety of accelerated sampling techniques have been developed and employed. The TFE-water mixture arrangement description through MD has faced substantial difficulties since emulating the TFE nanocrowding at certain TFE:H[Formula: see text]O ratios (around 15-40% of TFE). In this work, we determine the most suitable conditions that reproduce experimentally reported properties of TFE-water mixtures. We compared the employment of conventional MD and GaMD simulations and various water parameters. Our results show that the combination of parameters that better reproduce the experimental information is the combination of the TIP4PD water model and GaMD simulations. Therefore, these conditions help accurately describe the structural ensemble of IDPs in TFE-water mixtures.

METHODS

Conventional MD and GaMD simulations were performed under AMBER 18 software. The TFE and water molecules were described using GAFF2 and a variety of water models, such as TIP3P, TIP4P2005, TIP4PD, and TIP5P, respectively. The systems were simulated a 100 ns at 298 K.

Cryo-EM structure of human PAPP-A2 and mechanism of substrate recognition

Pregnancy-Associated Plasma Protein A isoforms, PAPP-A and PAPP-A2, are metalloproteases that cleave insulin-like growth factor binding proteins (IGFBPs) to modulate insulin-like growth factor signaling. The structures of homodimeric PAPP-A in complex with IGFBP5 anchor peptide, and inhibitor proteins STC2 and proMBP have been recently reported. Here, we present the single-particle cryo-EM structure of the monomeric, N-terminal LG, MP, and the M1 domains (with the exception of LNR1/2) of human PAPP-A2 to 3.13 Å resolution. Our structure together with functional studies provides insight into a previously reported patient mutation that inactivates PAPP-A2 in a distal region of the protein. Using a combinational approach, we suggest that PAPP-A2 recognizes IGFBP5 in a similar manner as PAPP-A and show that PAPP-A2 cleaves IGFBP5 less efficiently due to differences in the M2 domain. Overall, our studies characterize the cleavage mechanism of IGFBP5 by PAPP-A2 and shed light onto key differences with its paralog PAPP-A.

EphrinA5 regulates cell motility by modulating Snhg15/DNA triplex-dependent targeting of DNMT1 to the Ncam1 promoter

Cell-cell communication is mediated by membrane receptors and their ligands, such as the Eph/ephrin system, orchestrating cell migration during development and in diverse cancer types. Epigenetic mechanisms are key for integrating external "signals", e.g., from neighboring cells, into the transcriptome in health and disease. Previously, we reported ephrinA5 to trigger transcriptional changes of lncRNAs and protein-coding genes in cerebellar granule cells, a cell model for medulloblastoma. LncRNAs represent important adaptors for epigenetic writers through which they regulate gene expression. Here, we investigate a lncRNA-mediated targeting of DNMT1 to specific gene loci by the combined power of in silico modeling of RNA/DNA interactions and wet lab approaches, in the context of the clinically relevant use case of ephrinA5-dependent regulation of cellular motility of cerebellar granule cells. We provide evidence that Snhg15, a cancer-related lncRNA, recruits DNMT1 to the Ncam1 promoter through RNA/DNA triplex structure formation and the interaction with DNMT1. This mediates DNA methylation-dependent silencing of Ncam1, being abolished by ephrinA5 stimulation-triggered reduction of Snhg15 expression. Hence, we here propose a triple helix recognition mechanism, underlying cell motility regulation via lncRNA-targeted DNA methylation in a clinically relevant context.