The Coarse-Grained OPEP Force Field for Non-Amyloid and Amyloid Proteins

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

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.

Toward Consistent Physics-Based Modeling of Local Backbone Structures and Chirality Change of Proteins in Coarse-Grained Approaches

A reliable representation of local interactions is critical for the accuracy of modeling protein structure and dynamics at both the all-atom and coarse-grained levels. The development of local (mainly torsional) potentials was focused on careful parametrization of the predetermined (usually Fourier) formulas rather than on their physics-based derivation. In this Perspective we discuss the state-of-the-art methods for modeling local interactions, including the scale-consistent theory developed in our laboratory, which implies that the coarse-grained torsional potentials inseparably depend on the virtual-bond angles adjacent to a given dihedral and that multitorsional terms should be considered. We extend the treatment to split the residue-based torsional potentials into the site-based regular and improper torsional potentials. These considerations are illustrated with the revised torsional potentials and improper-torsional potentials involving the l-alanine residue and the improper-torsional potential corresponding to serine-residue enantiomerization. Applications of the new approach in coarse-grained modeling and revising all-atom force fields are discussed.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.

Transferable and Polarizable Coarse Grained Model for Proteins─ProMPT

The application of classical molecular dynamics (MD) simulations at atomic resolution (fine-grained level, FG), to most biomolecular processes, remains limited because of the associated computational complexity of representing all the atoms. This problem is magnified in the presence of protein-based biomolecular systems that have a very large conformational space, and MD simulations with fine-grained resolution have slow dynamics to explore this space. Current transferable coarse grained (CG) force fields in literature are either limited to only peptides with the environment encoded in an implicit form or cannot capture transitions into secondary/tertiary peptide structures from a primary sequence of amino acids. In this work, we present a transferable CG force field with an explicit representation of the environment for accurate simulations with proteins. The force field consists of a set of pseudoatoms representing different chemical groups that can be joined/associated together to create different biomolecular systems. This preserves the transferability of the force field to multiple environments and simulation conditions. We have added electronic polarization that can respond to environmental heterogeneity/fluctuations and couple it to protein's structural transitions. The nonbonded interactions are parametrized with physics-based features such as solvation and partitioning free energies determined by thermodynamic calculations and matched with experiments and/or atomistic simulations. The bonded potentials are inferred from corresponding distributions in nonredundant protein structure databases. We present validations of the CG model with simulations of well-studied aqueous protein systems with specific protein fold types─Trp-cage, Trpzip4, villin, WW-domain, and β-α-β. We also explore the applications of the force field to study aqueous aggregation of Aβ 16-22 peptides.

Virtual Quasi-2D Intermediates as Building Blocks for Plausible Structural Models of Amyloid Fibrils from Proteins with Complex Topologies: A Case Study of Insulin

Conformational transitions of globular proteins into amyloid fibrils are complex multistage processes exceedingly challenging to simulate using molecular dynamics (MD). Slow monomer diffusion rates and rugged free energy landscapes disfavor swift self-assembly of orderly amyloid architectures within timescales accessible to all-atom MD. Here, we conduct a multiscale MD study of the amyloidogenic self-assembly of insulin: a small protein with a complex topology defined by two polypeptide chains interlinked by three disulfide bonds. To avoid kinetic traps, unconventional preplanarized insulin conformations are used as amyloid building blocks. These starting conformers generated through uniaxial compression of the native monomer in various spatial directions represent 6 distinct (out of 16 conceivable) two-dimensional (2D) topological classes varying in N-/C-terminal segments of insulin's A- and B-chains being placed inside or outside of the central loop constituted by the middle sections of both chains and Cys7A-Cys7B/Cys19B-Cys20A disulfide bonds. Simulations of the fibrillar self-assembly are initiated through a biased in-register alignment of two, three, or four layers of flat conformers belonging to a single topological class. The various starting topologies are conserved throughout the self-assembly process resulting in polymorphic amyloid fibrils varying in structural features such as helical twist, presence of cavities, and overall stability. Some of the protofilament structures obtained in this work are highly compatible with the earlier biophysical studies on insulin amyloid and high-resolution studies on insulin-derived amyloidogenic peptide models postulating the presence of steric zippers. Our approach provides in silico means to study amyloidogenic tendencies and viable amyloid architectures of larger disulfide-constrained proteins with complex topologies.

Paired Simulations and Experimental Investigations into the Calcium-Dependent Conformation of Albumin

Serum albumin is the most abundant protein in blood plasma, and it is involved in multiple biological processes. Serum albumin has recently been adapted for improving biomaterial integration with bone tissue, and studies have shown the importance of this protein in bone repair and regeneration. However, the mechanism of action is not yet clear. In stark contrast, other studies have demonstrated that albumin blocks cell adhesion to surfaces, which is seen as a limitation to its bone healing role. These apparent contradictions suggest that the conformation of albumin facilitates its bioactivity, leading to enhanced bone repair. Serum albumin is known to play a major role in maintaining the calcium ion concentration in blood plasma. Due to the prevalence of calcium at bone repair and regeneration sites, it has been hypothesized that calcium binding to serum albumin triggers a conformational change, leading to bioactivity. In the current study, molecular modeling approaches including molecular docking, atomic molecular dynamics (MD) simulation, and coarse-grained MD simulation were used to test this hypothesis by investigating the conformational changes induced in bovine serum albumin by interaction with calcium ions. The computational results were qualitatively validated with experimental Fourier-transform infrared spectroscopy analysis. We find that free calcium ions in solution transiently bind with the three major loops in albumin, triggering a conformational change where N-terminal and C-terminal domains separate from each other in a partial unfolding process. The separation distance between these domains was found to correlate with the calcium ion concentration. The experimental data support the simulation results showing that albumin has enhanced conformational heterogeneity upon exposure to intermediate levels of calcium, without any significant secondary structure changes.

Computational Models for the Study of Protein Aggregation

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation

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

Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations

Amyloid fibril formation is an intrinsic property of short peptides, non-disease proteins, and proteins associated with neurodegenerative diseases. Aggregates of the Aβ and tau proteins, the α-synuclein protein, and the prion protein are observed in the brain of Alzheimer's, Parkinson's, and prion disease patients, respectively. Due to the transient short-range and long-range interactions of all species and their high aggregation propensities, the conformational ensemble of these devastating proteins, the exception being for the monomeric prion protein, remains elusive by standard structural biology methods in bulk solution and in lipid membranes. To overcome these limitations, an increasing number of simulations using different sampling methods and protein models have been performed. In this chapter, we first review our main contributions to the field of amyloid protein simulations aimed at understanding the early aggregation steps of short linear amyloid peptides, the conformational ensemble of the Aβ40/42 dimers in bulk solution, and the stability of Aβ aggregates in lipid membrane models. Then we focus on our studies on the interactions of amyloid peptides/inhibitors to prevent aggregation, and long amyloid sequences, including new results on a monomeric tau construct.

Accurate Description of Protein-Protein Recognition and Protein Aggregation with the Implicit-Solvent-Based PACSAB Protein Model

We used the PACSAB protein model, based on the implicit solvation approach, to simulate protein–protein recognition and study the effect of helical structure on the association of aggregating peptides. After optimization, the PACSAB force field was able to reproduce correctly both the correct binding interface in ubiquitin dimerization and the conformational ensemble of the disordered protein activator for hormone and retinoid receptor (ACTR). The PACSAB model allowed us to predict the native binding of ACTR with its binding partner, reproducing the refolding upon binding mechanism of the disordered protein.

Exposure of Von Willebrand Factor Cleavage Site in A1A2A3-Fragment under Extreme Hydrodynamic Shear

Von Willebrand Factor (vWf) is a giant multimeric extracellular blood plasma involved in hemostasis. In this work we present multi-scale simulations of its three-domains fragment A1A2A3. These three domains are essential for the functional regulation of vWf. Namely the A2 domain hosts the site where the protease ADAMTS13 cleavages the multimeric vWf allowing for its length control that prevents thrombotic conditions. The exposure of the cleavage site follows the elongation/unfolding of the domain that is caused by an increased shear stress in blood. By deploying Lattice Boltzmann molecular dynamics simulations based on the OPEP coarse-grained model for proteins, we investigated at molecular level the unfolding of the A2 domain under the action of a perturbing shear flow. We described the structural steps of this unfolding that mainly concerns the β-strand structures of the domain, and we compared the process occurring under shear with that produced by the action of a directional pulling force, a typical condition of single molecule experiments. We observe, that under the action of shear flow, the competition among the elongational and rotational components of the fluid field leads to a complex behaviour of the domain, where elongated structures can be followed by partially collapsed melted globule structures with a very different degree of exposure of the cleavage site. Our simulations pose the base for the development of a multi-scale in-silico description of vWf dynamics and functionality in physiological conditions, including high resolution details for molecular relevant events, e.g., the binding to platelets and collagen during coagulation or thrombosis.

Effects of applied surface-tension on membrane-assisted Aβ aggregation

Accumulation of protein-based (Aβ) aggregates on cellular membranes with varying structural properties is commonly recognized as the key step in Alzheimer's pathogenesis. But experimental and computational challenges have made this biophysical characterization difficult. In particular, studies connecting biological membrane organization and Aβ aggregation are limited. While experiments have suggested that an increased membrane curvature results in faster Aβ peptide aggregation in the context of Alzheimer's disease, a mechanistic explanation for this relation is missing. In this work, we are leveraging molecular simulations with a physics-based coarse grained model to address and understand the relationships between curved cellular membranes and aggregation of a model template peptide Aβ 16-22. In agreement with experimental results, our simulations also suggest a positive correlation between increased peptide aggregation and membrane curvature. More curved membranes have higher lipid packing defects that engage peptide hydrophobic groups and promote faster diffusion leading to peptide fibrillar structures. In addition, we curated the effects of peptide aggregation on the membrane's structure and organization. Interfacial peptide aggregation results in heterogeneous headgroup-peptide interactions and an induced crowding effect at the lipid headgroup region, leading to a more ordered headgroup region and disordered lipid-tails at the membrane core. This work presents a mechanistic and morphological overview of the relationships between the biomembrane local structure and organization, and Aβ peptide aggregation.

Extending the Martini Coarse-Grained Force Field to N-Glycans

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the force field parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini force field parameters for N-glycans, calibrated against experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional elastic networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol-water partition coefficients were also calculated for various N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. We also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-protein interactions can reproduce free energies obtained from experimental studies. These parameters should be of use in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.

Aggregation of disease-related peptides

Protein misfolding and aggregation of amyloid proteins is the fundamental cause of more than 20 diseases. Molecular mechanisms of the self-assembly and the formation of the toxic aggregates are still elusive. Computer simulations have been intensively used to study the aggregation of amyloid peptides of various amino acid lengths related to neurodegenerative diseases. We review atomistic and coarse-grained simulations of short amyloid peptides aimed at determining their transient oligomeric structures and the early and late aggregation steps.

Modelling lipid systems in fluid with Lattice Boltzmann Molecular Dynamics simulations and hydrodynamics

In this work we present the coupling between Dry Martini, an efficient implicit solvent coarse-grained model for lipids, and the Lattice Boltzmann Molecular Dynamics (LBMD) simulation technique in order to include naturally hydrodynamic interactions in implicit solvent simulations of lipid systems. After validating the implementation of the model, we explored several systems where the action of a perturbing fluid plays an important role. Namely, we investigated the role of an external shear flow on the dynamics of a vesicle, the dynamics of substrate release under shear, and inquired the dynamics of proteins and substrates confined inside the core of a vesicle. Our methodology enables future exploration of a large variety of biological entities and processes involving lipid systems at the mesoscopic scale where hydrodynamics plays an essential role, e.g. by modulating the migration of proteins in the proximity of membranes, the dynamics of vesicle-based drug delivery systems, or, more generally, the behaviour of proteins in cellular compartments.

Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.

OPUS-SSF: A side-chain-inclusive scoring function for ranking protein structural models

We introduce a side-chain-inclusive scoring function, named OPUS-SSF, for ranking protein structural models. The method builds a scoring function based on the native distributions of the coordinate components of certain anchoring points in a local molecular system for peptide segments of 5, 7, 9, and 11 residues in length. Differing from our previous OPUS-CSF [Xu et al., Protein Sci. 2018; 27: 286-292], which exclusively uses main chain information, OPUS-SSF employs anchoring points on side chains so that the effect of side chains is taken into account. The performance of OPUS-SSF was tested on 15 decoy sets containing totally 603 proteins, and 571 of them had their native structures recognized from their decoys. Similar to OPUS-CSF, OPUS-SSF does not employ the Boltzmann formula in constructing scoring functions. The results indicate that OPUS-SSF has achieved a significant improvement on decoy recognition and it should be a very useful tool for protein structural prediction and modeling.

Multiconfigurational Coarse-Grained Molecular Dynamics

Standard low resolution coarse-grained modeling techniques have difficulty capturing multiple configurations of protein systems. Here, we present a method for creating accurate coarse-grained (CG) models with multiple configurations using a linear combination of functions or “states”. Individual CG models are created to capture the individual states, and the approximate coupling between the two states is determined from an all-atom potential of mean force. We show that the resulting multiconfiguration coarse-graining (MCCG) method accurately captures the transition state as well as the free energy between the two states. We have tested this method on the folding of dodecaalanine, as well as the amphipathic helix of endophilin.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), ³J-couplings (³Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

The SIRAH 2.0 Force Field: Altius, Fortius, Citius

A new version of the coarse-grained (CG) SIRAH force field for proteins has been developed. Modifications to bonded and non-bonded interactions on the existing molecular topologies significantly ameliorate the structural description and flexibility of a non-redundant set of proteins. The SIRAH 2.0 force field has also been ported to the popular simulation package AMBER, which along with the former implementation in GROMACS expands significantly the potential range of users and performance of this CG force field on CPU/GPU codes. As a non-trivial example of its application, we undertook the structural and dynamical analysis of the most abundant and conserved calcium-binding protein, calmodulin (CaM). CaM is composed of two calcium-binding motifs called EF-hands, which in the presence of calcium specifically recognize a cognate peptide by embracing it. CG simulations of CaM bound to four calcium ions in the presence or absence of a binding peptide (holo and apo forms, respectively) resulted in good and stable ion coordination. The simulation of the holo form starting from an experimental structure sampled near-native conformations, retrieving quasi-atomistic precision. Removing the binding peptide enabled the EF-hands to perform large reciprocal movements, comparable to those observed in NMR structures. On the other hand, the isolated peptide starting from the helical conformation experienced spontaneous unfolding, in agreement with previous experimental data. However, repositioning the peptide in the neighborhood of one EF-hand not only prevented the peptide from unfolding but also drove CaM to a fully bound conformation, with both EF-hands embracing the cognate peptide, resembling the experimental holo structure. Therefore, SIRAH 2.0 shows the capacity to handle a number of structurally and dynamically challenging situations, including metal ion coordination, unbiased conformational sampling, and specific protein-peptide recognition.

Pathways of amyloid-beta absorption and aggregation in a membranous environment

Aggregation of misfolded oligomeric amyloid-beta (Aβ) peptides on lipid membranes has been identified as a primary event in Alzheimer's pathogenesis. However, the structural and dynamical features of this membrane assisted Aβ aggregation have not been well characterized. The microscopic characterization of dynamic molecular-level interactions in peptide aggregation pathways has been challenging both computationally and experimentally. In this work, we explore differential patterns of membrane-induced Aβ 16-22 (K-L-V-F-F-A-E) aggregation from the microscopic perspective of molecular interactions. Physics-based coarse-grained molecular dynamics (CG-MD) simulations were employed to investigate the effect of lipid headgroup charge - zwitterionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine: POPC) and anionic (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-l-serine: POPS) - on Aβ 16-22 peptide aggregation. Our analyses present an extensive overview of multiple pathways for peptide absorption and biomechanical forces governing peptide folding and aggregation. In agreement with experimental observations, anionic POPS molecules promote extended configurations in Aβ peptides that contribute towards faster emergence of ordered β-sheet-rich peptide assemblies compared to POPC, suggesting faster fibrillation. In addition, lower cumulative rates of peptide aggregation in POPS due to higher peptide-lipid interactions and slower lipid diffusion result in multiple distinct ordered peptide aggregates that can serve as nucleation seeds for subsequent Aβ aggregation. This study provides an in-silico assessment of experimentally observed aggregation patterns, presents new morphological insights and highlights the importance of lipid headgroup chemistry in modulating the peptide absorption and aggregation process.

Three Weaknesses for Three Perturbations: Comparing Protein Unfolding Under Shear, Force, and Thermal Stresses

The perturbation of a protein conformation by a physiological fluid flow is crucial in various biological processes including blood clotting and bacterial adhesion to human tissues. Investigating such mechanisms by computer simulations is thus of great interest, but it requires development of ad hoc strategies to mimic the complex hydrodynamic interactions acting on the protein from the surrounding flow. In this study, we apply the Lattice Boltzmann Molecular Dynamics (LBMD) technique built on the implicit solvent coarse-grained model for protein Optimized Potential for Efficient peptide structure Prediction (OPEP) and a mesoscopic representation of the fluid solvent, to simulate the unfolding of a small globular cold-shock protein in shear flow and to compare it to the unfolding mechanisms caused either by mechanical or thermal perturbations. We show that each perturbation probes a specific weakness of the protein and causes the disruption of the native fold along different unfolding pathways. Notably, the shear flow and the thermal unfolding exhibit very similar pathways, while because of the directionality of the perturbation, the unfolding under force is quite different. For force and thermal disruption of the native state, the coarse-grained simulations are compared to all-atom simulations in explicit solvent, showing an excellent agreement in the explored unfolding mechanisms. These findings encourage the use of LBMD based on the OPEP model to investigate how a flow can affect the function of larger proteins, for example, in catch-bond systems.

Seeding and cross-seeding fibrillation of N-terminal prion protein peptides PrP(120-144)

Prion diseases are infectious neurodegenerative diseases that are capable of cross-species transmission, thus arousing public health concerns. Seed-templating propagation of prion protein is believed to underlie prion cross-species transmission pathology. Understanding the molecular fundamentals of prion propagation is key to unravelling the pathology of prion diseases. In this study, we use coarse-grained molecular dynamics to investigate the seeding and cross-seeding aggregation of three prion protein fragments PrP(120-144) originating from human (Hu), bank vole (BV), and Syrian hamster (SHa). We find that the seed accelerates the aggregation of the monomer peptides by eliminating the lag phase. The monomer aggregation kinetics are mainly determined by the structure of the seed. The stronger the hydrophobic residues on the seed associate with each other, the higher the probability that the seed recruits monomer peptides to its surface/interface. For cross-seeding aggregation, we show that Hu has a strong tendency to adopt the conformation of the BV seed and vice versa; the Hu and BV monomers have a weak tendency to adopt the conformation of the SHa seed. These two findings are consistent with Apostol et al.'s experimental findings on PrP(138-143) and partially consistent with Jones et al.'s finding on PrP(23-144). We also identify several conformational mismatches when SHa cross-seeds BV and Hu peptides, indicating the existence of a cross-seeding barrier between SHa and the other two sequences. This study sheds light on the molecular mechanism of seed-templating aggregation of prion protein fragments underlying the sequence-dependent transmission barrier in prion diseases.

Using molecular simulation to explore the nanoscale dynamics of the plant kinome

Eukaryotic protein kinases (PKs) are a large family of proteins critical for cellular response to external signals, acting as molecular switches. PKs propagate biochemical signals by catalyzing phosphorylation of other proteins, including other PKs, which can undergo conformational changes upon phosphorylation and catalyze further phosphorylations. Although PKs have been studied thoroughly across the domains of life, the structures of these proteins are sparsely understood in numerous groups of organisms, including plants. In addition to efforts towards determining crystal structures of PKs, research on human PKs has incorporated molecular dynamics (MD) simulations to study the conformational dynamics underlying the switching of PK function. This approach of experimental structural biology coupled with computational biophysics has led to improved understanding of how PKs become catalytically active and why mutations cause pathological PK behavior, at spatial and temporal resolutions inaccessible to current experimental methods alone. In this review, we argue for the value of applying MD simulation to plant PKs. We review the basics of MD simulation methodology, the successes achieved through MD simulation in animal PKs, and current work on plant PKs using MD simulation. We conclude with a discussion of the future of MD simulations and plant PKs, arguing for the importance of molecular simulation in the future of plant PK research.

Molecular Mechanism of Protein Unfolding under Shear: A Lattice Boltzmann Molecular Dynamics Study

Proteins are marginally stable soft-matter entities that can be disrupted using a variety of perturbative stresses, including thermal, chemical, or mechanical ones. Fluid under extreme flow conditions is a possible route to probe the weakness of biomolecules and collect information on the molecular cohesive interactions that secure their stability. Moreover, in many cases, physiological flow triggers the functional response of specialized proteins as occurring in blood coagulation or cell adhesion. We deploy the Lattice Boltzmann molecular dynamics technique based on the coarse-grained model for protein OPEP to study the mechanism of protein unfolding under Couette flow. Our simulations provide a clear view of how structural elements of the proteins are affected by shear, and for the simple study case, the β-hairpin, we exploited the analogy to pulling experiments to quantify the mechanical forces acting on the protein under shear.

HyRes: a coarse-grained model for multi-scale enhanced sampling of disordered protein conformations

Efficient coarse-grained (CG) models can be coupled with atomistic force fields to accelerate the sampling of atomistic energy landscapes in the multi-scale enhanced sampling (MSES) framework. This approach may be particularly suitable for generating atomistic conformational ensembles of intrinsically disordered proteins (IDPs). While MSES is relatively robust to inherent CG artifacts, achieving optimal sampling efficiency requires CG modeling to generate the local and long-range fluctuations that are largely consistent with those at the atomistic level. Here, we describe a new hybrid resolution CG model (HyRes) for MSES simulations of disordered protein states, which is specifically designed to provide semi-quantitative secondary structure propensities together with a qualitative description of long-range nonspecific interactions. The HyRes model contains an atomistic description of the backbone with intermediate resolution side chains. The secondary structure propensities are tuned by adjusting the backbone hydrogen-bonding strength and the ϕ/ψ torsion profile. The sizes and covalent geometries of the side chains are parameterized to reproduce distributions derived from atomistic simulations. Lennard-Jones parameters for sidechain beads are assigned to reproduce statistical potentials derived from the protein structural database, and then globally parameterized with nonspecific electrostatic interactions to reproduce the free energy profiles of pair wise interactions and the key conformational properties of model peptides. Application of HyRes to MSES simulations of small IDPs suggests that it is capable of driving faster structural transitions at the atomistic level and increasing the convergence rate compared to the Cα-only Gō-like models previously utilized. With further optimization, we believe that the new CG model could greatly improve the efficiency of MSES simulations of the larger and more complex IDPs frequently involved in cellular signalling and regulation.

OPUS-CSF: A C-atom-based scoring function for ranking protein structural models

We report a C‐atom‐based scoring function, named OPUS‐CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testing OPUS‐CSF on decoy recognition, it maximally recognized 257 native structures out of 278 targets in 11 commonly used decoy sets, significantly outperforming other popular all‐atom empirical potentials. The average correlation coefficient with TM‐score was also comparable with those of other potentials. OPUS‐CSF is a highly coarse‐grained scoring function, which only requires input of partial mainchain information, and very fast. Thus, it is suitable for applications at early stage of structural building.

Shielding effect in protein folding

One of the most important interactions responsible for protein folding and stability are hydrogen bonds between peptide groups. There is a constant competition between the water molecules and peptide groups in a hydrogen bond formation. Also side-chains take part in this process by reducing hydration of peptide group (shielding effect) that promotes the protein folding. In this paper, a new approach to take into account a shielding effect is presented. A modification of the energy function is derived and incorporated into the UNited RESidue (UNRES) force field. Canonical Molecular Dynamics and Replica Exchange Molecular Dynamics with UNRES force field is applied to study the influence of this effect on protein structure, folding kinetics and free energy landscapes. The results of test calculations suggest that even small contribution of this effect into energy function changes force field behavior as well as speeds up the folding process significantly.

Multi-scale simulations of biological systems using the OPEP coarse-grained model

Biomolecules are complex machines that are optimized by evolution to properly fulfill or contribute to a variety of biochemical tasks in the cellular environment. Computer simulations based on quantum mechanics and atomistic force fields have been proven to be a powerful microscope for obtaining valuable insights into many biological, physical, and chemical processes. Many interesting phenomena involve, however, a time scale and a number of degrees of freedom, notably if crowding is considered, that cannot be explored at an atomistic resolution. To bridge the gap between reality and simulation, many different advanced computational techniques and coarse-grained (CG) models have been developed. Here, we report some applications of the CG OPEP protein model to amyloid fibril formation, the response of catch-bond proteins to two types of fluid flow, and interactive simulations to fold peptides with well-defined 3D structures or with intrinsic disorder.

Adaptive resolution simulations of biomolecular systems

In this review article, we discuss and analyze some recently developed hybrid atomistic-mesoscopic solvent models for multiscale biomolecular simulations. We focus on the biomolecular applications of the adaptive resolution scheme (AdResS), which allows solvent molecules to change their resolution back and forth between atomistic and coarse-grained representations according to their positions in the system. First, we discuss coupling of atomistic and coarse-grained models of salt solution using a 1-to-1 molecular mapping-i.e., one coarse-grained bead represents one water molecule-for development of a multiscale salt solution model. In order to make use of coarse-grained molecular models that are compatible with the MARTINI force field, one has to resort to a supramolecular mapping, in particular to a 4-to-1 mapping, where four water molecules are represented with one coarse-grained bead. To this end, bundled atomistic water models are employed, i.e., the relative movement of water molecules that are mapped to the same coarse-grained bead is restricted by employing harmonic springs. Supramolecular coupling has recently also been extended to polarizable coarse-grained water models with explicit charges. Since these coarse-grained models consist of several interaction sites, orientational degrees of freedom of the atomistic and coarse-grained representations are coupled via a harmonic energy penalty term. The latter aligns the dipole moments of both representations. The reviewed multiscale solvent models are ready to be used in biomolecular simulations, as illustrated in a few examples.

OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing

We report a new distance- and orientation-dependent, all-atom statistical potential derived from side-chain packing, named OPUS-DOSP, for protein structure modeling. The framework of OPUS-DOSP is based on OPUS-PSP, previously developed by us [JMB (2008), 376, 288-301], with refinement and new features. In particular, distance or orientation contribution is considered depending on the range of contact distance. A new auxiliary function in energy function is also introduced, in addition to the traditional Boltzmann term, in order to adjust the contributions of extreme cases. OPUS-DOSP was tested on 11 decoy sets commonly used for statistical potential benchmarking. Among 278 native structures, 239 and 249 native structures were recognized by OPUS-DOSP without and with the auxiliary function, respectively. The results show that OPUS-DOSP has an increased decoy recognition capability comparing with those of other relevant potentials to date.

What Can Human-Guided Simulations Bring to RNA Folding?

Inspired by the recent success of scientific-discovery games for predicting protein tertiary and RNA secondary structures, we have developed an open software for coarse-grained RNA folding simulations, guided by human intuition. To determine the extent to which interactive simulations can accurately predict 3D RNA structures of increasing complexity and lengths (four RNAs with 22-47 nucleotides), an interactive experiment was conducted with 141 participants who had very little knowledge of nucleic acids systems and computer simulations, and had received only a brief description of the important forces stabilizing RNA structures. Their structures and full trajectories have been analyzed statistically and compared to standard replica exchange molecular dynamics simulations. Our analyses show that participants gain easily chemical intelligence to fold simple and nontrivial topologies, with little computer time, and this result opens the door for the use of human-guided simulations to RNA folding. Our experiment shows that interactive simulations have better chances of success when the user widely explores the conformational space. Interestingly, providing on-the-fly feedback of the root mean square deviation with respect to the experimental structure did not improve the quality of the proposed models.

High-Resolution Structures of the Amyloid-β 1-42 Dimers from the Comparison of Four Atomistic Force Fields

The dimer of the amyloid-β peptide Aβ of 42 residues is the smallest toxic species in Alzheimer's disease, but its equilibrium structures are unknown. Here we determined the equilibrium ensembles generated by the four atomistic OPLS-AA, CHARMM22*, AMBER99sb-ildn, and AMBERsb14 force fields with the TIP3P water model. On the basis of 144 μs replica exchange molecular dynamics simulations (with 750 ns per replica), we find that the four force fields lead to random coil ensembles with calculated cross-collision sections, hydrodynamics properties, and small-angle X-ray scattering profiles independent of the force field. There are, however, marked differences in secondary structure, with the AMBERsb14 and CHARMM22* ensembles overestimating the CD-derived helix content, and the OPLS-AA and AMBER99sb-ildn secondary structure contents in agreement with CD data. Also the intramolecular beta-hairpin content spanning residues 17-21 and 30-36 varies between 1.5% and 13%. Overall, there are significant differences in tertiary and quaternary conformations among all force fields, and the key finding, irrespective of the force field, is that the dimer is stabilized by nonspecific interactions, explaining therefore its possible transient binding to multiple cellular partners and, in part, its toxicity.

Lattice model for amyloid peptides: OPEP force field parametrization and applications to the nucleus size of Alzheimer's peptides

Coarse-grained protein lattice models approximate atomistic details and keep the essential interactions. They are, therefore, suitable for capturing generic features of protein folding and amyloid formation at low computational cost. As our aim is to study the critical nucleus sizes of two experimentally well-characterized peptide fragments Aβ16-22 and Aβ37-42 of the full length Aβ1-42 Alzheimer's peptide, it is important that simulations with the lattice model reproduce all-atom simulations. In this study, we present a comprehensive force field parameterization based on the OPEP (Optimized Potential for Efficient protein structure Prediction) force field for an on-lattice protein model, which incorporates explicitly the formation of hydrogen bonds and directions of side-chains. Our bottom-up approach starts with the determination of the best lattice force parameters for the Aβ16-22 dimer by fitting its equilibrium parallel and anti-parallel β-sheet populations to all-atom simulation results. Surprisingly, the calibrated force field is transferable to the trimer of Aβ16-22 and the dimer and trimer of Aβ37-42. Encouraged by this finding, we characterized the free energy landscapes of the two decamers. The dominant structure of the Aβ16-22 decamer matches the microcrystal structure. Pushing the simulations for aggregates between 4-mer and 12-mer suggests a nucleus size for fibril formation of 10 chains. In contrast, the Aβ37-42 decamer is largely disordered with mixed by parallel and antiparallel chains, suggesting that the nucleus size is >10 peptides. Our refined force field coupled to this on-lattice model should provide useful insights into the critical nucleation number associated with neurodegenerative diseases.

Conformational Ensembles of the Wild-Type and S8C Aβ1-42 Dimers

We characterized the dimer of the amyloid-β wild-type (WT) peptide, Aβ, of 42 residues and its disulfide-bond-locked double mutant (S8C) by replica exchange molecular dynamics simulations. Aβ dimers are known to be the smallest toxic species in Alzheimer's disease, and the S8C mutant has been shown experimentally to form an exclusive homogeneous and neurotoxic dimer. Our 50 μs all-atom simulations reveal similar secondary structures and collision cross-sections but very different intramolecular and intermolecular conformations upon double S8C mutation. Both dimers are very dynamic with hundreds of free-energy minima that differ from the U-shape and S-shape conformations of the peptides in the fibrils. The only common structural feature, shared by both species with a probability of 4% in WT and 12% in S8C-S8C, is a three-stranded β-sheet spanning the 17-23, 29-36, and 39-41 residues, which does not exist in the Aβ40 WT dimers.

Discrete Molecular Dynamics Approach to the Study of Disordered and Aggregating Proteins

We present a refinement of the Coarse Grained PACSAB force field for Discrete Molecular Dynamics (DMD) simulations of proteins in aqueous conditions. As the original version, the refined method provides good representation of the structure and dynamics of folded proteins but provides much better representations of a variety of unfolded proteins, including some very large, impossible to analyze by atomistic simulation methods. The PACSAB/DMD method also reproduces accurately aggregation properties, providing good pictures of the structural ensembles of proteins showing a folded core and an intrinsically disordered region. The combination of accuracy and speed makes the method presented here a good alternative for the exploration of unstructured protein systems.

Hydrodynamic effects on β-amyloid (16-22) peptide aggregation

Computer simulations based on simplified representations are routinely used to explore the early steps of amyloid aggregation. However, when protein models with implicit solvent are employed, these simulations miss the effect of solvent induced correlations on the aggregation kinetics and lifetimes of metastable states. In this work, we apply the multi-scale Lattice Boltzmann Molecular Dynamics technique (LBMD) to investigate the initial aggregation phases of the amyloid Aβ16-22 peptide. LBMD includes naturally hydrodynamic interactions (HIs) via a kinetic on-lattice representation of the fluid kinetics. The peptides are represented by the flexible OPEP coarse-grained force field. First, we have tuned the essential parameters that control the coupling between the molecular and fluid evolutions in order to reproduce the experimental diffusivity of elementary species. The method is then deployed to investigate the effect of HIs on the aggregation of 100 and 1000 Aβ16-22 peptides. We show that HIs clearly impact the aggregation process and the fluctuations of the oligomer sizes by favouring the fusion and exchange dynamics of oligomers between aggregates. HIs also guide the growth of the leading largest cluster. For the 100 Aβ16-22 peptide system, the simulation of ∼300 ns allowed us to observe the transition from ellipsoidal assemblies to an elongated and slightly twisted aggregate involving almost the totality of the peptides. For the 1000 Aβ16-22 peptides, a system of unprecedented size at quasi-atomistic resolution, we were able to explore a branched disordered fibril-like structure that has never been described by other computer simulations, but has been observed experimentally.

Multiscale simulation of molecular processes in cellular environments

We describe the recent advances in studying biological systems via multiscale simulations. Our scheme is based on a coarse-grained representation of the macromolecules and a mesoscopic description of the solvent. The dual technique handles particles, the aqueous solvent and their mutual exchange of forces resulting in a stable and accurate methodology allowing biosystems of unprecedented size to be simulated.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Protein Aggregation and Molecular Crowding: Perspectives From Multiscale Simulations

Cells are extremely crowded environments, thus the use of diluted salted aqueous solutions containing a single protein is too simplistic to mimic the real situation. Macromolecular crowding might affect protein structure, folding, shape, conformational stability, binding of small molecules, enzymatic activity, interactions with cognate biomolecules, and pathological aggregation. The latter phenomenon typically leads to the formation of amyloid fibrils that are linked to several lethal neurodegenerative diseases, but that can also play a functional role in certain organisms. The majority of molecular simulations performed before the last few years were conducted in diluted solutions and were restricted both in the timescales and in the system dimensions by the available computational resources. In recent years, several computational solutions were developed to get close to physiological conditions. In this review we summarize the main computational techniques used to tackle the issue of protein aggregation both in a diluted and in a crowded environment.

Assembly of Triblock Amphiphilic Peptides into One-Dimensional Aggregates and Network Formation

Peptide assembly plays a key role in both neurological diseases and development of novel biomaterials with well-defined nanostructures. Synthetic model peptides provide a unique platform to explore the role of intermolecular interactions in the assembly process. A triblock peptide architecture designed by the Hartgerink group is a versatile system which relies on Coulomb interactions, hydrogen bonding, and hydrophobicity to guide these peptides' assembly at three different length scales: β-sheets, double-wall ribbon-like aggregates, and finally a highly porous network structure which can support gels with ≤1% by weight peptide concentration. In this study, by using molecular dynamics simulations of a structure based implicit solvent coarse grained model, we analyzed this hierarchical assembly process. Parametrization of our CG model is based on multiple-state points from atomistic simulations, which enables this model to represent the conformational adaptability of the triblock peptide molecule based on the surrounding medium. Our results indicate that emergence of the double-wall β-sheet packing mechanism, proposed in light of the experimental evidence, strongly depends on the subtle balance of the intermolecular forces. We demonstrate that, even though backbone hydrogen bonding dominates the early nucleation stages, depending on the strength of the hydrophobic and Coulomb forces, alternative structures such as zero-dimensional aggregates with two β-sheets oriented orthogonally (which we refer to as a cross-packed structure) and β-sheets with misoriented hydrophobic side chains are also feasible. We discuss the implications of these competing structures for the three different length scales of assembly by systematically investigating the influence of density, counterion valency, and hydrophobicity.

Electrostatics analysis of the mutational and pH effects of the N-terminal domain self-association of the major ampullate spidroin

Spider silk is a fascinating material combining mechanical properties such as maximum strength and high toughness comparable or better than man-made materials, with biocompatible degradability characteristics. Experimental measurements have shown that pH triggers the dimer formation of the N-terminal domain (NTD) of the major ampullate spidroin 1 (MaSp 1). A coarse-grained model accounting for electrostatics, van der Waals and pH-dependent charge-fluctuation interactions, by means of Monte Carlo simulations, gave us a more comprehensive view of the NTD dimerization process. A detailed analysis of the electrostatic properties and free energy derivatives for the NTD homoassociation was carried out at different pH values and salt concentrations for the protein wild type and for several mutants. We observed an enhancement of dipole-dipole interactions at pH 6 due to the ionization of key amino acids, a process identified as the main driving force for dimerization. Analytical estimates based on the DVLO theory framework corroborate our findings. Molecular dynamics simulations using the OPEP coarse-grained force field for proteins show that the mutant E17Q is subject to larger structural fluctuations when compared to the wild type. Estimates of the association rate constants for this mutant were evaluated by the Debye-Smoluchowski theory and are in agreement with the experimental data when thermally relaxed structures are used instead of the crystallographic data. Our results can contribute to the design of new mutants with specific association properties.