Modeling the Structure and Interactions of Intrinsically Disordered Peptides with Multiple Replica, Metadynamics-Based Sampling Methods and Force-Field Combinations

Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates

Phase separation is a fundamental process that enables cellular organization by forming biomolecular condensates. These assemblies regulate diverse functions by creating distinct environments, influencing reaction kinetics, and facilitating processes such as genome organization, signal transduction, and RNA metabolism. Recent studies highlight the complexity of condensate properties, shaped by intrinsic molecular features and external factors such as temperature and pH. Molecular simulations serve as an effective approach to establishing a comprehensive framework for analyzing these influences, offering high-resolution insights into condensate stability, dynamics, and material properties. This review evaluates recent advancements in biomolecular condensate simulations, with a particular focus on coarse-grained 1-bead-per-amino-acid (1BPA) protein models, and emphasizes OpenABC, a tool designed to simplify and streamline condensate simulations. OpenABC supports the implementation of various coarse-grained force fields, enabling their performance evaluation. Our benchmarking identifies inconsistencies in phase behavior predictions across force fields, even though these models accurately capture single-chain statistics. This finding underscores the need for enhanced force field accuracy, achievable through enriched training data sets, many-body potentials, and advanced optimization techniques. Such refinements could significantly improve the predictive capacity of coarse-grained models, bridging molecular details with emergent condensate behaviors.

Molecular crowding and amyloidogenic self-assembly: Emergent perspectives from modern computations

In recent decades, the conventional protein folding paradigm has been challenged by intriguing properties of disordered peptide sequences that do not adopt stably folded conformations. Such intrinsically disordered proteins and protein regions (IDPs and IDRs) are poised uniquely in biology due to their propensity for self-aggregation, amyloidogenesis, and correlations with a cluster of debilitating diseases. Complexities underlying their structural and functional manifestations are enhanced in the presence of molecular crowding via non-specific protein-protein and protein-solvent contacts. Enabled by technological advances, physics-based algorithms, and data science, modern computer simulations provide unprecedented insights into the structure, function, dynamics, and thermodynamics of complex macromolecular systems. These characteristics are frequently correlated and manifest into unique observables. This chapter presents an overview of how such methodologies can lend insights and drive investigations into the molecular trifecta of crowding, protein self-aggregation, and amyloidogenesis. It begins with a general overview of disordered proteins in relation to biological function and of a suite of relevant experimental methods. Specific examples are showcased in the biological context. This is followed by a description of the computational approaches that supplant experimental efforts, with an elaboration on enhanced molecular simulation methods. The chapter concludes by alluding to expanded possibilities in disease amelioration.

Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Survey of the Aβ-peptide structural diversity: molecular dynamics approaches

The review deals with the application of Molecular Dynamics (MD) to the structure modeling of beta-amyloids (Aβ), currently classified as intrinsically disordered proteins (IDPs). In this review, we strive to relate the main advances in this area but specifically focus on the approaches and methodology. All relevant papers on the Aβ modeling are cited in the Tables in Supplementary Data, including a concise description of the applied approaches, sorted according to the types of the studied systems: modeling of the monomeric Aβ and Aβ aggregates. Similar sections focused according to the type of modeled object are present in the review. In the final part of the review, novel methods of general IDP modeling not confined to Aβ are described.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-024-01253-y.

Graphitic nanoflakes modulate the structure and binding of human amylin

Human amylin is an inherently disordered protein whose ability to form amyloid fibrils is linked to the onset of type II diabetes. Graphitic nanomaterials have potential in managing amyloid diseases as they can disrupt protein aggregation processes in biological settings, but optimising these materials to prevent fibrillation is challenging. Here, we employ bias-exchange molecular dynamics simulations to systematically study the structure and adsorption preferences of amylin on graphitic nanoflakes that vary in their physical dimensions and surface functionalisation. Our findings reveal that nanoflake size and surface oxidation both influence the structure and adsorption preferences of amylin. The purely hydrophobic substrate of pristine graphene (PG) nanoflakes encourages non-specific protein adsorption, leading to unrestricted lateral mobility once amylin adheres to the surface. Particularly on larger PG nanoflakes, this induces structural changes in amylin that may promote fibril formation, such as the loss of native helical content and an increase in β-sheet character. In contrast, oxidised graphene nanoflakes form hydrogen bonds between surface oxygen sites and amylin, and as such restricting protein mobility. Reduced graphene oxide (rGO) flakes, featuring lower amounts of surface oxidation, are amphiphilic and exhibit substantial regions of bare carbon which promote protein binding and reduced conformational flexibility, leading to conservation of the native structure of amylin. In comparison, graphene oxide (GO) nanoflakes, which are predominantly hydrophilic and have a high degree of surface oxidation, facilitate considerable protein structural variability, resulting in substantial contact area between the protein and GO, and subsequent protein unfolding. Our results indicate that tailoring the size, oxygen concentration and surface patterning of graphitic nanoflakes can lead to specific and robust protein binding, ultimately influencing the likelihood of fibril formation. These atomistic insights provide key design considerations for the development of graphitic nanoflakes that can modulate protein aggregation by sequestering protein monomers in the biological environment and inhibit conformational changes linked to amyloid fibril formation.

Digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational space of intrinsically disordered peptides

We propose digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational ensembles of peptides, especially intrinsically disordered peptides (IDPs). The DNCS algorithm relies on generating history-dependent samples of dihedral variables using bitwise XOR operations and binary angle measurements (BAM). The algorithm was initially studied using met-enkephalin, a highly elusive neuropeptide. The DNCS method predicted near-native structures and the energy landscape of met-enkephalin was observed to be in direct correlation with earlier studies on the neuropeptide. Clustering analysis revealed that there are only 24 low-lying conformations of the molecule. The DNCS method has then been tested for predicting optimal conformations of 42 oligopeptides of length varying from 3 to 8 residues. The closest-to-native structures of 86% of cases are near-native and 24% of them have a root mean square deviation of less than 1.00 Å with respect to their crystal structures. The results obtained reveal that the DNCS method performs well, that too in less computational time.

Atomic Scale Structure of Self-Assembled Lipidated Peptide Nanomaterials

β-Peptides have great potential as novel biomaterials and therapeutic agents, due to their unique ability to self-assemble into low dimensional nanostructures, and their resistance to enzymatic degradation in vivo. However, the self-assembly mechanisms of β-peptides, which possess increased flexibility due to the extra backbone methylene groups present within the constituent β-amino acids, are not well understood due to inherent difficulties of observing their bottom-up growth pathway experimentally. A computational approach is presented for the bottom-up modelling of the self-assembled lipidated β3-peptides, from monomers, to oligomers, to supramolecular low-dimensional nanostructures, in all-atom detail. The approach is applied to elucidate the self-assembly mechanisms of recently discovered, distinct structural morphologies of low dimensional nanomaterials, assembled from lipidated β3-peptide monomers. The resultant structures of the nanobelts and the twisted fibrils are stable throughout subsequent unrestrained all-atom molecular dynamics simulations, and these assemblies display good agreement with the structural features obtained from X-ray fiber diffraction and atomic force microscopy data. This is the first reported, fully-atomistic model of a lipidated β3-peptide-based nanomaterial, and the computational approach developed here, in combination with experimental fiber diffraction analysis and atomic force microscopy, will be useful in elucidating the atomic scale structure of self-assembled peptide-based and other supramolecular nanomaterials.

Accelerating Solvent Dynamics with Replica Exchange for Improved Free Energy Sampling

Molecular reactions in solution typically involve solvent exchange; for example, a surface must partly desolvate for a molecule to adsorb onto it. When these reactions are simulated, slow solvent dynamics can limit the sampling of configurations and reduce the accuracy of free energy estimates. Here, we combine Hamiltonian replica exchange (HREX) with well-tempered metadynamics (WTMD) to accelerate the sampling of solvent configurations orthogonal to the collective variable space. We compute the formation free energy of a carbonate vacancy in the calcite-water interface and find that the combination of WTMD with HREX significantly improves the sampling relative to WTMD without HREX.

Protein compactness and interaction valency define the architecture of a biomolecular condensate across scales

Non-membrane-bound biomolecular condensates have been proposed to represent an important mode of subcellular organization in diverse biological settings. However, the fundamental principles governing the spatial organization and dynamics of condensates at the atomistic level remain unclear. The Saccharomyces cerevisiae Lge1 protein is required for histone H2B ubiquitination and its N-terminal intrinsically disordered fragment (Lge11-80) undergoes robust phase separation. This study connects single- and multi-chain all-atom molecular dynamics simulations of Lge11-80 with the in vitro behavior of Lge11-80 condensates. Analysis of modeled protein-protein interactions elucidates the key determinants of Lge11-80 condensate formation and links configurational entropy, valency, and compactness of proteins inside the condensates. A newly derived analytical formalism, related to colloid fractal cluster formation, describes condensate architecture across length scales as a function of protein valency and compactness. In particular, the formalism provides an atomistically resolved model of Lge11-80 condensates on the scale of hundreds of nanometers starting from individual protein conformers captured in simulations. The simulation-derived fractal dimensions of condensates of Lge11-80 and its mutants agree with their in vitro morphologies. The presented framework enables a multiscale description of biomolecular condensates and embeds their study in a wider context of colloid self-organization.

Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling

The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.

Conformational ensemble of amyloid-forming semenogelin 1 peptide SEM1(68-107) by NMR spectroscopy and MD simulations

SEM1(68-107) is a peptide corresponding to the region of semenogelin 1 protein from 68 to 107 amino acid position. SEM1(68-107) is an abundant component of semen, which participates in HIV infection enhanced by amyloid fibrils forming. To understand the causes influencing amyloid fibril formation, it is necessary to determine the spatial structure of SEM1(68-107). It was shown that the determination of SEM1(68-107) structure is complicated by the non-informative NMR spectra due to the high intramolecular mobility of peptides. The complementary approach based on the geometric restrictions of individual peptide fragments and molecular modeling was used for the determination of the spatial structure of SEM1(68-107). The N- (SEM1(68-85)) and C-terminuses (SEM1(86-107)) of SEM1(68-107) were chosen as two individual peptide fragments. SEM1(68-85) and SEM1(86-107) structures were established with NMR and circular dichroism CD spectroscopies. These regions were used as geometric restraints for the SEM1(68-107) structure modeling. Even though most of the SEM1(68-107) peptide is unstructured, our detailed analysis revealed the following structured elements: N-terminus (70His-84Gln) forms an α-helix, (86Asp-94Thr) and (101Gly-103Ser) regions fold into 3₁₀-helixes. The absence of a SEM1(68-107) rigid conformation leads to instability of these secondary structure regions. The calculated SEM1(68-107) structure is in good agreement with experimental values of hydrodynamic radius and dihedral angles obtained by NMR spectroscopy. This testifies the adequacy of a combined approach based on the use of peptide fragment structures for the molecular modeling formation of full-size peptide spatial structure.

Salt-Induced Transitions in the Conformational Ensembles of Intrinsically Disordered Proteins

Salts modulate the behavior of intrinsically disordered proteins (IDPs) and influence the formation of membraneless organelles through liquid-liquid phase separation (LLPS). In low ionic strength solutions, IDP conformations are perturbed by the screening of electrostatic interactions, independent of the salt identity. In this regime, insight into the IDP behavior can be obtained using the theory for salt-induced transitions in charged polymers. However, salt-specific interactions with the charged and uncharged residues, known as the Hofmeister effect, influence IDP behavior in high ionic strength solutions. There is a lack of reliable theoretical models in high salt concentration regimes to predict the salt effect on IDPs. We propose a simulation methodology using a coarse-grained IDP model and experimentally measured water to salt solution transfer free energies of various chemical groups that allowed us to study the salt-specific transitions induced in the IDPs conformational ensemble. We probed the effect of three different monovalent salts on five IDPs belonging to various polymer classes based on charged residue content. We demonstrate that all of the IDPs of different polymer classes behave as self-avoiding walks (SAWs) at physiological salt concentration. In high salt concentrations, the transitions observed in the IDP conformational ensembles are dependent on the salt used and the IDP sequence and composition. Changing the anion with the cation fixed can result in the IDP transition from a SAW-like behavior to a collapsed globule. An important implication of these results is that a suitable salt can be identified to induce condensation of an IDP through LLPS.