Well-Balanced Force Field ff03CMAP for Folded and Disordered Proteins

Computational study of the mechanism of a polyurethane esterase A (PueA) from Pseudomonas chlororaphis

The effective management of plastic waste has become a global imperative, given our reliance on a linear model in which plastics are manufactured, used once, and then discarded. This has led to the pervasive accumulation of plastic debris in landfills and environmental contamination. Recognizing this issue, numerous initiatives are underway to address the environmental repercussions associated with plastic disposal. In this study, we investigate the possible molecular mechanism of polyurethane esterase A (PueA), which has been previously identified as responsible for the degradation of a polyester polyurethane (PU) sample in Pseudomonas chlororaphis, as an effort to develop enzymatic biodegradation solutions. After generating the unsolved 3D structure of the protein by AlphaFold2 from its known genome, the enzymatic hydrolysis of the same model PU compound previously used in experiments has been explored employing QM/MM molecular dynamics simulations. This required a preliminary analysis of the 3D structure of the apo-enzyme, identifying the putative active site, and the search for the optimal protein-substrate binding site. Finally, the resulting free energy landscape indicates that wild-type PueA can degrade PU chains, although with low-level activity. The reaction takes place by a characteristic four-step path of the serine hydrolases, involving an acylation followed by a diacylation step. Energetics and structural analysis of the evolution of the active site along the reaction suggests that PueA can be considered a promising protein scaffold for further development to achieve efficient biodegradation of PU.

Graphormer supervised de novo protein design method and function validation

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Comprehensive Comparison and Critical Assessment of RNA-Specific Force Fields

Molecular dynamics simulations play a pivotal role in elucidating the dynamic behaviors of RNA structures, offering a valuable complement to traditional methods such as nuclear magnetic resonance or X-ray. Despite this, the current precision of RNA force fields lags behind that of protein force fields. In this work, we systematically compared the performance of four RNA force fields (ff99bsc0χOL3, AMBERDES, ff99OL3_CMAP1, AMBERMaxEnt) across diverse RNA structures. Our findings highlight significant challenges in maintaining stability, particularly with regard to cross-strand and cross-loop hydrogen bonds. Furthermore, we observed the limitations in accurately describing the conformations of nonhelical structural motif, terminal nucleotides, and also base pairing and base stacking interactions by the tested RNA force fields. The identified deficiencies in existing RNA force fields provide valuable insights for subsequent force field development. Concurrently, these findings offer recommendations for selecting appropriate force fields in RNA simulations.

Color Tuning in Bovine Rhodopsin through Polarizable Embedding

Bovine rhodopsin is among the most studied proteins in the rhodopsin family. Its primary activation mechanism is the photoisomerization of 11-cis retinal, triggered by the absorption of a UV-visible photon. Different mutants of the same rhodopsin show different absorption wavelengths due to the influence of the specific amino acid residues forming the cavity in which the retinal chromophore is embedded, and rhodopsins activated at different wavelengths are, for example, exploited in the field of optogenetics. In this letter, we present a procedure for systematically investigating color tuning in models of bovine rhodopsin and a set of its mutants embedded in a membrane bilayer. Vertical excitation energy calculations were carried out with the polarizable embedding potential for describing the environment surrounding the chromophore. We show that polarizable embedding outperformed regular electrostatic embedding in determining both the vertical excitation energies and associated oscillator strengths of the systems studied.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.

Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma

BACKGROUND

Class 2 uveal melanomas are associated with the inactivation of the BRCA1 ((breast cancer type 1 susceptibility protein)-associated protein 1 (BAP1)) gene. Inactivation of BAP1 promotes the upregulation of vitamin K-dependent protein S (PROS1), which interacts with the tyrosine-protein kinase Mer (MERTK) receptor on M2 macrophages to induce an immunosuppressive environment.

METHODS

We simulated the interaction of PROS1 with MERTK with ColabFold. We evaluated PROS1 and MERTK for the presence of intrinsically disordered protein regions (IDPRs) and disorder-to-order (DOT) regions to understand their protein-protein interaction (PPI). We first evaluated the structure of each protein with AlphaFold. We then analyzed specific sequence-based features of the PROS1 and MERTK with a suite of bioinformatics tools.

RESULTS

With high-resolution, moderate confidence, we successfully modeled the interaction between PROS1 and MERTK (predicted local distance difference test score (pDLLT) = 70.68). Our structural analysis qualitatively demonstrated IDPRs (i.e., spaghetti-like entities) in PROS1 and MERK. PROS1 was 23.37 % disordered, and MERTK was 23.09 % disordered, classifying them as moderately disordered and flexible proteins. PROS1 was significantly enriched in cysteine, the most order-promoting residue (p-value <0.05). Our IUPred analysis demonstrated that there are two disorder-to-order transition (DOT) regions in PROS1. MERTK was significantly enriched in proline, the most disorder-promoting residue (p-value <0.05), but did not contain DOT regions. Our STRING analysis demonstrated that the PPI network between PROS1 and MERTK is more complex than their assumed one-to-one binding (p-value <2.0 × 10-6).

CONCLUSION

Our findings present a novel prediction for the interaction between PROS1 and MERTK. Our findings show that PROS1 and MERTK contain elements of intrinsic disorder. PROS1 has two DOT regions that are attractive immunotherapy targets. We recommend that IDPRs and DOT regions found in PROS1 and MERTK should be considered when developing immunotherapies targeting this PPI.

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Intrinsically disordered proteins (IDPs) play a critical role in many biological processes. Due to the inherent structural flexibility of IDPs, experimental methods present significant challenges for sampling their conformational information at the atomic level. Therefore, molecular dynamics (MD) simulations have emerged as the primary tools for modeling IDPs whose accuracy depend on force field and water model. To enhance the accuracy of physical modeling of IDPs, several force fields have been developed. However, current water models lack precision and underestimate the interaction between water molecules and proteins. Here, we used Monte-Carlo re-weighting method to re-parameterize a three-point water model based on OPC3 for IDPs (named OPC3-B). We benchmarked the performance of OPC3-B compared with nine different water models for 10 IDPs and three ordered proteins. The results indicate that the performance of OPC3-B is better than other water models for both IDPs and ordered proteins. At the same time, OPC3-B possess the power of transferability with other force field to simulate IDPs. This newly developed water model can be used to insight into the research of sequence-disordered-function paradigm for IDPs.

Fuzzy Drug Targets: Disordered Proteins in the Drug-Discovery Realm

Intrinsically disordered proteins (IDPs) and regions (IDRs) form a large part of the eukaryotic proteome. Contrary to the structure-function paradigm, the disordered proteins perform a myriad of functions in vivo. Consequently, they are involved in various disease pathways and are plausible drug targets. Unlike folded proteins, that have a defined structure and well carved out drug-binding pockets that can guide lead molecule selection, the disordered proteins require alternative drug-development methodologies that are based on an acceptable picture of their conformational ensemble. In this review, we discuss various experimental and computational techniques that contribute toward understanding IDP "structure" and describe representative pursuances toward IDP-targeting drug development. We also discuss ideas on developing rational drug design protocols targeting IDPs.

Simple and Effective Conformational Sampling Strategy for Intrinsically Disordered Proteins Using the UNRES Web Server

Intrinsically disordered proteins (IDPs) contain more charged amino acids than folded proteins, resulting in a lack of hydrophobic core(s) and a tendency to adopt rapidly interconverting structures rather than well-defined structures. The structural heterogeneity of IDPs, encoded by the amino acid sequence, is closely related to their unique roles in biological pathways, which require them to interact with different binding partners. Recently, Robustelli and co-workers have demonstrated that a balanced all-atom force field can be used to sample heterogeneous structures of disordered proteins ( Proc. Natl. Acad. Sci. U.S.A. 2018, 115, E4758-E4766). However, such a solution requires extensive computational resources, such as Anton supercomputers. Here, we propose a simple and effective solution to sample the conformational space of IDPs using a publicly available web server, namely, the UNited-RESidue (UNRES) web server. Our proposed solution requires no investment in computational resources and no prior knowledge of UNRES. UNRES Replica Exchange Molecular Dynamics (REMD) simulations were carried out on a set of eight disordered proteins at temperatures spanning from 270 to 430 K. Utilizing the latest UNRES force field designed for structured proteins, with proper selections of temperatures, we were able to produce comparable results to all-atom force fields as reported in work done by Robustelli and co-workers. In addition, NMR observables and the radius of gyration calculated from UNRES ensembles were directly compared with the experimental data to further evaluate the accuracy of the UNRES model at all temperatures. Our results suggest that carrying out the UNRES simulations at optimal temperatures using the UNRES web server can be a good alternative to sample heterogeneous structures of IDPs.

Phosphorylation Modification Force Field FB18CMAP Improving Conformation Sampling of Phosphoproteins

Phosphorylation of proteins plays an important regulatory role at almost all levels of cellular organization. Molecular dynamics (MD) simulation is a promising tool to reveal the mechanism of how phosphorylation regulates many key biological processes at the atomistic level. MD simulation accuracy depends on force field precision, while the current force fields for phospho-amino acids have resulted in notable inconsistency with experimental data. Here, a new force field parameter (named FB18CMAP) is generated by fitting against quantum mechanics (QM) energy in aqueous solution with φ/ψ dihedral potential-energy surfaces optimized using CMAP parameters. MD simulations of phosphorylated dipeptides, intrinsically disordered proteins (IDPs), and ordered (folded) proteins show that FB18CMAP can mimic NMR observables and structural characteristics of phosphorylated dipeptides and proteins more accurately than the FB18 force field. These findings suggest that FB18CMAP performs well in both the simulation of ordered and disordered states of phosphorylated proteins.

Conformational landscapes of artificial peptides predicted by various force fields: are we ready to simulate β-amino acids?

With the introduction of artificial peptides as antimicrobial agents and organic catalysts, numerous efforts have been made to design foldamers with desirable structures and functions. Computational tools are a helpful proxy for revealing the dynamic structures at atomic resolution and understanding foldamer's complex structure-function relationships. However, the performance of conventional force fields in predicting the structures of artificial peptides has not been systematically evaluated. In this study, we critically assessed three popular force fields, AMBER ff14SB, CHARMM36m, and OPLS-AA/L, in predicting conformational propensities of a β-peptide foldamer at monomer and hexamer levels. Simulation results were compared to those obtained from quantum chemistry calculations and experimental data. We also utilised replica exchange molecular dynamics simulations to investigate the energy landscape of each force field and assess the similarities and differences between force fields. We compared different solvent systems in the AMBER ff14SB and CHARMM36m frameworks and confirmed the unanimous role of hydrogen bonds in shaping energy landscapes. We anticipate that our data will pave the way for further improvements to force fields and for understanding the role of solvents in peptide folding, crystallisation, and engineering.

Influence of force field choice on the conformational landscape of rat and human islet amyloid polypeptide

Human islet amyloid polypeptide (hIAPP) is a naturally occurring, intrinsically disordered protein (IDP) whose abnormal aggregation into toxic soluble oligomers and insoluble amyloid fibrils is a pathological feature in type-2 diabetes. Rat IAPP (rIAPP) differs from hIAPP by only six amino acids yet has a reduced tendency to aggregate or form fibrils. The structures of the monomeric forms of IAPP are difficult to characterize due to their intrinsically disordered nature. Molecular dynamics simulations can provide a detailed characterization of the monomeric forms of rIAPP and hIAPP in near-physiological conditions. In this work, the conformational landscapes of rIAPP and hIAPP as a function of secondary structure content were predicted using well-tempered bias exchange metadynamics simulations. Several combinations of commonly used biomolecular force fields and water models were tested. The predicted conformational preferences of both rIAPP and hIAPP are typical of IDPs, exhibiting dominant random coil structures but showing a low propensity for transient α-helical conformations. Predicted nuclear magnetic resonance Cα chemical shifts reveal different preferences with each force field towards certain conformations, with AMBERff99SBnmr2/TIP4Pd showing the best agreement with the experiment. Comparisons of secondary structure content demonstrate residue-specific differences between hIAPP and rIAPP that may reflect their different aggregation propensities.

Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model

Leukocyte adhesion deficiency-1 (LAD-1) disorder is a severe immunodeficiency syndrome caused by deficiency or mutation of β2 integrin. The phosphorylation on threonine 758 of β2 integrin acts as a molecular switch inhibiting the binding of filamin. However, the switch mechanism of site-specific phosphorylation at the atom level is still poorly understood. To resolve the regulation mechanism, all-atom molecular dynamics simulation and Markov state model were used to study the dynamic regulation pathway of phosphorylation. Wild type system possessed lower binding free energy and fewer number of states than the phosphorylated system. Both systems underwent local disorder-to-order conformation conversion when achieving steady states. To reach steady states, wild type adopted less number of transition paths/shortest path according to the transition path theory than the phosphorylated system. The underlying phosphorylated regulation pathway was from P1 to P0 and then P4 state, and the main driving force should be hydrogen bond and hydrophobic interaction disturbing the secondary structure of phosphorylated states. These studies will shed light on the pathogenesis of LAD-1 disease and lay a foundation for drug development.

Personal Precise Force Field for Intrinsically Disordered and Ordered Proteins Based on Deep Learning

Intrinsically disordered proteins (IDPs) are proteins without a fixed three-dimensional (3D) structure under physiological conditions and are associated with Parkinson's disease, Alzheimer's disease, cancer, cardiovascular disease, amyloidosis, diabetes, and other diseases. Experimental methods can hardly capture the ensemble of diverse conformations for IDPs. Molecular dynamics (MD) simulations can sample continuous conformations that might provide a valuable complement to experimental data. However, the accuracy of MD simulations depends on the quality of force field. In particular, the evolutionary conservation and coevolution of IDPs introduce that current force fields could not precisely reproduce the conformation of IDPs. In order to improve the performance of force field, deep learning and reweighting methods were used to automatically generate personal force field parameters for intrinsically disordered and ordered proteins. At first, the deep learning method predicted more accuracy φ/ψ dihedral of residue than the previous method. Then, reweighting optimized the personal force field parameters for each residue. Finally, typical representative systems such as IDPs, structure protein, and fast-folding protein were used to evaluate this force field. The results indicate that two personal force field parameters (named PPFF1 and PPFF1_af2) could better reproduce the experimental observables than ff03CMAP force field. In summary, this strategy will provide feasibility for the development of precise personal force fields.

Predicting molecular properties of α-synuclein using force fields for intrinsically disordered proteins

Independent force field validation is an essential practice to keep track of developments and for performing meaningful Molecular Dynamics simulations. In this work, atomistic force fields for intrinsically disordered proteins (IDP) are tested by simulating the archetypical IDP α-synuclein in solution for 2.5 μs. Four combinations of protein and water force fields were tested: ff19SB/OPC, ff19SB/TIP4P-D, ff03CMAP/TIP4P-D, and a99SB-disp/TIP4P-disp, with four independent repeat simulations for each combination. We compare our simulations to the results of a 73 μs simulation using the a99SB-disp/TIP4P-disp combination, provided by D. E. Shaw Research. From the trajectories, we predict a range of experimental observations of α-synuclein and compare them to literature data. This includes protein radius of gyration and hydration, intramolecular distances, NMR chemical shifts, and ³ J-couplings. Both ff19SB/TIP4P-D and a99SB-disp/TIP4P-disp produce extended conformational ensembles of α-synuclein that agree well with experimental radius of gyration and intramolecular distances while a99SB-disp/TIP4P-disp reproduces a balanced α-synuclein secondary structure content. It was found that ff19SB/OPC and ff03CMAP/TIP4P-D produce overly compact conformational ensembles and show discrepancies in the secondary structure content compared to the experimental data.

Performance evaluation of the balanced force field ff03CMAP for intrinsically disordered and ordered proteins

Intrinsically disordered proteins (IDPs) have been found to be closely associated with various human diseases. Because IDPs have no fixed tertiary structure under physiological conditions, current experimental methods, such as X-ray spectroscopy, NMR, and CryoEM, cannot capture all the dynamic conformations. Molecular dynamics simulation is an useful tool that is widely used to study the conformer distributions of IDPs and has become an important complementary tool for experimental methods. However, the accuracy of MD simulations directly depends on utilizing a precise force field. Recently a CMAP optimized force field based on the Amber ff03 force field (termed ff03CMAP herein) was developed for a balanced sampling of IDPs and folded proteins. In order to further evaluate the performance, more types of disordered and ordered proteins were used to test the ability for conformer sampling. The results showed that simulated chemical shifts, J-coupling, and R_g distribution with the ff03CMAP force field were in better agreement with NMR measurements and were more accurate than those with the ff03 force field. The sampling conformations by ff03CMAP were more diverse than those of ff03. At the same time, ff03CMAP could stabilize the conformers of the ordered proteins. These findings indicate that ff03CMAP can be widely used to sample diverse conformers for proteins, including the intrinsically disordered regions.

Polarizable Force Field of Intrinsically Disordered Proteins with CMAP and Reweighting Optimization

Intrinsically disordered proteins (IDPs) are highly structurally heterogeneous without a specific tertiary structure under physiology conditions and play key roles in the development of human diseases. Due to the characteristics of diverse conformations, as one of the important methods, molecular dynamics simulation can complement information for experimental methods. Because of the enrichment for charged amino acids for IDPs, polarizable force fields should be a good choice for the simulation of IDPs. However, current polarizable force fields are limited in sampling conformer features of IDPs. Therefore, a polarizable force field was released and named Drude2019IDP based on Drude2019 with reweighting and grid-based potential energy correction map optimization. In order to evaluate the performance of Drude2019IDP, 16 dipeptides, 18 short peptides, 3 representative IDPs, and 5 structural proteins were simulated. The results show that the NMR observables driven by Drude2019IDP are in better agreement with the experiment data than those by Drude2019 on short peptides and IDPs. Drude2019IDP can sample more diverse conformations than Drude2019. Furthermore, the performances of the two force fields are similar to the sample ordered proteins. These results confirm that the developed Drude2019IDP can improve the reproduction of conformers for intrinsically disordered proteins and can be used to gain insight into the paradigm of sequence-disorder for IDPs.

Balanced Force Field ff03CMAP Improving the Dynamics Conformation Sampling of Phosphorylation Site

Phosphorylation plays a key role in plant biology, such as the accumulation of plant cells to form the observed proteome. Statistical analysis found that many phosphorylation sites are located in disordered regions. However, current force fields are mainly trained for structural proteins, which might not have the capacity to perfectly capture the dynamic conformation of the phosphorylated proteins. Therefore, we evaluated the performance of ff03CMAP, a balanced force field between structural and disordered proteins, for the sampling of the phosphorylated proteins. The test results of 11 different phosphorylated systems, including dipeptides, disordered proteins, folded proteins, and their complex, indicate that the ff03CMAP force field can better sample the conformations of phosphorylation sites for disordered proteins and disordered regions than ff03. For the solvent model, the results strongly suggest that the ff03CMAP force field with the TIP4PD water model is the best combination for the conformer sampling. Additional tests of CHARMM36m and FB18 force fields on two phosphorylated systems suggest that the overall performance of ff03CMAP is similar to that of FB18 and better than that of CHARMM36m. These results can help other researchers to choose suitable force field and solvent models to investigate the dynamic properties of phosphorylation proteins.

Base-specific RNA force field improving the dynamics conformation of nucleotide

RNA plays a key role in numerous biological processes. Traditional experimental methods have difficulties capturing the structure and dynamic conformation of RNA. Thus, Molecular dynamic simulations (MDs) has become an essential complementary for RNA experiment. However, state-of-the-art RNA force fields have two major limitations of overestimation base stacking propensity and generation of a high ratio of intercalated conformations. Therefore, a two-step strategy was used to optimize the parameters of ff99bsc0χOL3 (named BSFF1) to improve these limitations, which as well adjusted the unbonded parameters of nucleobase heavy atoms and added ζ/α grid-based energy correction map energy term with reweighting. MD simulations of tetranucleotides indicate that BSFF1 can significantly decrease the ratio of intercalated conformations. Tests of single-strand RNA and kink-turn show that BSFF1 force field can reproduce more accurate conformers than ff99bsc0χOL3 force field. BSFF1 can also stabilize the conformers of duplex and riboswitch. The successful ab initio folding of tetraloop further supports the performance of BSFF1. These findings confirm that the newly developed force field BSFF1 can improve the conformer sampling of RNA.

Unexpected Dynamic Binding May Rescue the Binding Affinity of Rivaroxaban in a Mutant of Coagulation Factor X

A novel coagulation factor X (FX) Tyr319Cys mutation (Y99C as chymotrypsin numbering) was identified in a patient with severe bleeding. Unlike the earlier reported Y99A mutant, this mutant can bind and cleave its specific chromogenetic substrate at a normal level, suggesting an intact binding pocket. Here, using molecular dynamics simulations and MM-PBSA calculations on a FX-rivaroxaban (RIV) complex, we confirmed a much stronger binding of RIV in Y99C than in Y99A on a molecular level, which is actually the average result of multiple binding poses in dynamics. Detailed structural analyses also indicated the moderate flexibility of the 99-loop and the importance of the flexible side chain of Trp215 in the different binding poses. This case again emphasizes that binding of ligands may not only be a dynamic process but also a dynamic state, which is often neglected in drug design and screening based on static X-ray structures. In addition, the computational results somewhat confirmed our hypothesis on the activated Tyr319Cys FX (Y99C FXa) with an impaired procoagulant function to bind inhibitors of FXa and to be developed into a potential reversal agent for novel oral anticoagulants (NOAC).

Early aggregation mechanism of Aβ16-22 revealed by Markov state models

Aβ_16-22 is believed to have critical role in early aggregation of full length amyloids that are associated with the Alzheimer's disease and can aggregate to form amyloid fibrils. However, the early aggregation mechanism is still unsolved. Here, multiple long-term molecular dynamics simulations combining with Markov state model were used to probe the early oligomerization mechanism of Aβ_16-22 peptides. The identified dimeric form adopted either globular random-coil or extended β-strand like conformations. The observed dimers of these variants shared many overall conformational characteristics but differed in several aspects at detailed level. In all cases, the most common type of secondary structure was intermolecular antiparallel β-sheets. The inter-state transitions were very frequent ranges from few to hundred nanoseconds. More strikingly, those states which contain fraction of β secondary structure and significant amount of extended coiled structures, therefore exposed to the solvent, were majorly participated in aggregation. The assembly of low-energy dimers, in which the peptides form antiparallel β sheets, occurred by multiple pathways with the formation of an obligatory intermediates. We proposed that these states might facilitate the Aβ_16-22 aggregation through a significant component of the conformational selection mechanism, because they might increase the aggregates population by promoting the inter-chain hydrophobic and the hydrogen bond contacts. The formation of early stage antiparallel β sheet structures is critical for oligomerization, and at the same time provided a flat geometry to seed the ordered β-strand packing of the fibrils. Our findings hint at reorganization of this part of the molecule as a potentially critical step in Aβ aggregation and will insight into early oligomerization for large β amyloids.

RNA-Specific Force Field Optimization with CMAP and Reweighting

RNA plays a key role in a variety of cell activities. However, it is difficult to capture its structure dynamics by the traditional experimental methods because of the inherent limitations. Molecular dynamics simulation has become a valuable complement to the experimental methods. Previous studies have indicated that the current force fields cannot accurately reproduce the conformations and structural dynamics of RNA. Therefore, an RNA-specific force field was developed to improve the conformation sampling of RNA. The distribution of ζ/α dihedrals of tetranucleotides was optimized by a reweighting method, and the grid-based energy correction map (CMAP) term was first introduced into the Amber RNA force field of ff99bsc0χOL3, named ff99OL3_CMAP1. Extensive validations of tetranucleotides and tetraloops show that ff99OL3_CMAP1 can significantly decrease the population of an incorrect structure, increase the consistency between the simulation results and experimental values for tetranucleotides, and improve the stability of tetraloops. ff99OL3_CMAP1 can also precisely reproduce the conformation of a duplex and riboswitches. These findings confirm that the newly developed force field ff99OL3_CMAP1 can improve the conformer sampling of RNA.

An allosteric regulation mechanism of Arabidopsis Serine/Threonine kinase 1 (SIK1) through phosphorylation

The Arabidopsis Serine/Threonine Kinase 1 (SIK1) is a Sterile 20 (STE20)/Hippo orthologue that is also categorized as a Mitogen-Activated Protein Kinase Kinase Kinase Kinase (MAP4K). Like its animal and fungi orthologues, SIK1 is required for cell cycle exit, cell expansion, polarity establishment, as well as pathogenic response. The catalytic activity of SIK1, like other MAPKs, is presumably regulated by its phosphorylation states. Since no crystal structure for SIK1 has been reported yet, we built structural models for SIK1 kinase domain in different phosphorylation states with different pocket conformation to see how this kinase may be regulated. Using computational structural biology methods, we outlined a conduction path in which a phosphorylation site on the A-loop regulates the catalytic activity of SIK1 by controlling the closing or opening of the catalytic pocket at the G-loop. Furthermore, with analyses on the dynamic motions and in vitro kinase assay, we confirmed that three key residues in this conduction path, Lys278, Glu295, and Arg370, are indeed important for the kinase activity of SIK1. Since these residues are conserved in all STE20 kinases examined, the regulatory mechanism that we discovered may be common in STE20 kinases.

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.

Balanced Solvent Model for Intrinsically Disordered and Ordered Proteins

Intrinsically disordered proteins (IDPs) have no fixed three-dimensional (3D) structures under physiological conditions, with the content being about 51% in human proteomics. IDPs are associated with many human diseases, such as cancer, diabetes, and neurodegenerative diseases. Because IDPs do not crystallize and have diverse conformers, traditional experimental methods such as crystallization and NMR can hardly capture their conformation ensemble and just provide average structural characters of IDPs. Therefore, molecular dynamics (MD) simulations become a valuable complement to the experimental data. However, the accuracy of molecular dynamics simulation for IDPs depends on the combination of force fields and solvent models. Recently, we released an environment-specific force field (ESFF1) for IDPs, which can well reproduce the local structural properties (such as J-coupling and secondary chemical shifts). However, there is still a large deviation for the radius of gyration (R_g). Therefore, a solvent model combined with ESFF1 is necessary to capture the local and global characters for IDPs and ordered proteins. Here, we investigated the underestimation or overestimation of the solvent interaction for four solvent models (TIP3P, TIP4P-Ew, TIP4P-D, OPC) under ESFF1 and found the important ε parameter of the solvent model to play a key role in scaling R_g. A near-linear relationship between the simulation R_g and the ε parameter was used to develop the new solvent model, named TIP4P-B. The results indicate that the simulated R_g with TIP4P-B is in better agreement with the experimental observations than the other four solvent models. Simultaneously, TIP4P-B can also maintain the advantages of the ESFF1 force field for the local structural properties. Additionally, TIP4P-B can successfully sample the conformation of ordered proteins. These findings confirm that TIP4P-B is a balanced solvent model and can improve sampling R_g performance for folded proteins and IDPs.

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.

An Overview of Several Inhibitors for Alzheimer's Disease: Characterization and Failure

Amyloid beta (Aβ) oligomers are the most neurotoxic aggregates causing neuronal death and cognitive damage. A detailed elucidation of the aggregation pathways from oligomers to fibril formation is crucial to develop therapeutic strategies for Alzheimer's disease (AD). Although experimental techniques rely on the measure of time- and space-average properties, they face severe difficulties in the investigation of Aβ peptide aggregation due to their intrinsically disorder character. Computer simulation is a tool that allows tracing the molecular motion of molecules; hence it complements Aβ experiments, as it allows to explore the binding mechanism between metal ions and Aβ oligomers close to the cellular membrane at the atomic resolution. In this context, integrated studies of experiments and computer simulations can assist in mapping the complete pathways of aggregation and toxicity of Aβ peptides. Aβ oligomers are disordered proteins, and due to a rapid exploration of their intrinsic conformational space in real-time, they are challenging therapeutic targets. Therefore, no good drug candidate could have been identified for clinical use. Our previous investigations identified two small molecules, M30 (2-Octahydroisoquinolin-2(1H)-ylethanamine) and Gabapentin, capable of Aβ binding and inhibiting molecular aggregation, synaptotoxicity, intracellular calcium signaling, cellular toxicity and memory losses induced by Aβ. Thus, we recommend these molecules as novel candidates to assist anti-AD drug discovery in the near future. This review discusses the most recent research investigations about the Aβ dynamics in water, close contact with cell membranes, and several therapeutic strategies to remove plaque formation.

Catalytic mechanism of butane anaerobic oxidation for alkyl-coenzyme M reductase

Methane is among the most potent of the greenhouse gases, which plays a key role in global climate change. As an excellent carbon and energy source, methane can be utilized by anaerobic methane oxidizing archaea and aerobic methane oxidizing bacteria. The previous work shows that an anaerobic thermophilic enrichment culture composed of dense consortia of archaea and bacteria apparently uses partly similar pathways to oxidize the C4 hydrocarbon butane. However, the catalytic mechanism of butane anaerobic oxidation for alkyl-coenzyme M reductase is still unknown. Therefore, molecular dynamics (MD) simulation was used to investigate the dynamics differences of catalytic mechanism between methane coenzyme M reductase (MCR) and alkyl-coenzyme M reductase (ACR). At first, the binding pocket of ACR is larger than that of MCR. Then, the complex of butane and ACR is more stable than that of methane and ACR. Protein conformation cloud suggests that the position of methane is dynamics and methane escapes from the binding pocket of ACR during most of the simulation time, while butane tightly binds in the pocket of ACR. The hydrophobic interactions between butane and ACR are more and stronger than those between methane and ACR. At the same time, the binding free energy between butane and ACR is significantly lower than that between methane and ACR. The dynamics correlation network indicates that the transformation of information flow for ACR-butane is smoother than that for ACR-methane. The shortest pathway for ACR-butane is from Gln144, Ala141, Hie135, Ile133, Ala160, Arg206, Asp97, Met94, Tyr347 to Phe345 with synergistic effect for two butane molecules. This study can insight into the catalytic mechanism for butane/ACR complex.

Extensive evaluation of environment-specific force field for ordered and disordered proteins

Intrinsically disordered proteins (IDPs) have no fixed tertiary structure under physiological conditions and are associated with many human diseases. Because IDPs have the characteristic of possessing diverse conformations, current experimental methods cannot capture all the conformations of IDPs. However, molecular dynamics simulation can sample these atomistically diverse conformations as a valuable complement to experimental data. To accurately describe the properties of IDPs, the environment-specific precise force field (ESFF1) was successfully released to reproduce the conformer character of ordered and disordered proteins. Here, three typical IDPs and thirteen folded proteins were used to further evaluate the performance of this force field. The results indicate that the NMR observables of ESFF1 better approach experimental data than do those of ff14SB for IDPs. The sampling conformations by ESFF1 are more diverse than those of ff14SB. For folded proteins, these force fields have comparable performances for reproducing conformers. Therefore, ESFF1 can be used to reveal the model of sequence-disorder-function for IDPs.

Kirkwood-Buff-Derived Force Field for Peptides and Proteins: Philosophy and Development of KBFF20

A new classical nonpolarizable force field, KBFF20, for the simulation of peptides and proteins is presented. The force field relies heavily on the use of Kirkwood-Buff theory to provide a comparison of simulated and experimental Kirkwood-Buff integrals for solutes containing the functional groups common in proteins, thus ensuring intermolecular interactions that provide a good balance between the peptide-peptide, peptide-solvent, and solvent-solvent distributions observed in solution mixtures. In this way, it differs significantly from other biomolecular force fields. Further development and testing of the intermolecular potentials are presented here. Subsequently, rotational potentials for the ϕ/ψ and χ dihedral degrees of freedom are obtained by analysis of the Protein Data Bank, followed by small modifications to provide a reasonable balance between simulated and observed α and β percentages for small peptides. This, the first of two articles, describes in detail the philosophy and development behind KBFF20.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Recent Force Field Strategies for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

Disaggregation mechanism of prion amyloid for tweezer inhibitor

The aggregation of amyloid has been an important event in the pathology of amyloidogenicity. A number of small molecules have been designed for Amyloidosis treatment. Molecular tweezer CLR01, a potential drug for misfolded β-amyloids inhibition, was reportedly bind directly to Lysine residues and interrupt oligomerization. However, the disaggregation mechanism of amyloid for this inhibitor is unclear. Here we used long timescale of molecular dynamic simulation to reveal the mechanism of disaggregation for pentamer prion amyloid. Molecular docking and molecular dynamics simulation demonstrate that CLR01 is attached with Lysine₂₂₂ nitrogen by π-cation interaction of its nine aromatic rings and formation of salt bridge/hydrogen bond of one of the two rotatable peripheral anionic phosphate groups. Upon CLR01 binding, we found a major shifting occurs in initial conformation of the oligomer and stretch out the N-terminal chain A from the rest of the amyloid which seems to be the first stage of disaggregated the fibrils slowly yet efficiently. Moreover, the CLR01 remodelled the pentamer Prion_220-272 into a compact structure which might be the resistant conformation for further oligomerization. Our work will contribute to better understand the interaction and deterioration mechanism of molecular tweezer for prions and similar amyloids, and offer significant insights into therapeutic development for Amyloidosis treatment.

Secondary structure dependence of amyloid-β(1-40) on simulation techniques and force field parameters

Our recent studies revealed that none of the selected widely used force field parameters and molecular dynamics simulation techniques yield structural properties for the intrinsically disordered α-synuclein that are in agreement with various experiments via testing different force field parameters. Here, we extend our studies on the secondary structure properties of the disordered amyloid-β(1-40) peptide in aqueous solution. For these purposes, we conducted extensive replica exchange molecular dynamics simulations and obtained extensive molecular dynamics simulation trajectories from David E. Shaw group. Specifically, these molecular dynamics simulations were conducted using various force field parameters and obtained results are compared to our replica exchange molecular dynamics simulations and experiments. In this study, we calculated the secondary structure abundances and radius of gyration values for amyloid-β(1-40) that were simulated using varying force field parameter sets and different simulation techniques. In addition, the intrinsic disorder propensity, as well as sequence-based secondary structure predisposition of amyloid-β(1-40) and compared the findings with the results obtained from molecular simulations using various force field parameters and different simulation techniques. Our studies clearly show that the epitope region identification of amyloid-β(1-40) depends on the chosen simulation technique and chosen force field parameters. Based on comparison with experiments, we find that best computational results in agreement with experiments are obtained using the a99sb*-ildn, charmm36m, and a99sb-disp parameters for the amyloid-β(1-40) peptide in molecular dynamics simulations without parallel tempering or via replica exchange molecular dynamics simulations.

Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

Computational modeling suggests binding-induced expansion of Epsin disordered regions upon association with AP2

Intrinsically disordered regions (IDRs) are prevalent in the eukaryotic proteome. Common functional roles of IDRs include forming flexible linkers or undergoing allosteric folding-upon-binding. Recent studies have suggested an additional functional role for IDRs: generating steric pressure on the plasma membrane during endocytosis, via molecular crowding. However, in order to accomplish useful functions, such crowding needs to be regulated in space (e.g., endocytic hotspots) and time (e.g., during vesicle formation). In this work, we explore binding-induced regulation of IDR steric volume. We simulate the IDRs of two proteins from Clathrin-mediated endocytosis (CME) to see if their conformational spaces are regulated via binding-induced expansion. Using Monte-Carlo computational modeling of excluded volumes, we generate large conformational ensembles (3 million) for the IDRs of Epsin and Eps15 and dock the conformers to the alpha subunit of Adaptor Protein 2 (AP2α), their CME binding partner. Our results show that as more molecules of AP2α are bound, the Epsin-derived ensemble shows a significant increase in global dimensions, measured as the radius of Gyration (R_G) and the end-to-end distance (EED). Unlike Epsin, Eps15-derived conformers that permit AP2α binding at one motif were found to be more likely to accommodate binding of AP2α at other motifs, suggesting a tendency toward co-accessibility of binding motifs. Co-accessibility was not observed for any pair of binding motifs in Epsin. Thus, we speculate that the disordered regions of Epsin and Eps15 perform different roles during CME, with accessibility in Eps15 allowing it to act as a recruiter of AP2α molecules, while binding-induced expansion of the Epsin disordered region could impose steric pressure and remodel the plasma membrane during vesicle formation.

Protein functions were originally believed to arise from ordered protein structures. This dogma was later challenged by the identification of intrinsically disordered proteins that lack specific structure. The functional roles of such proteins usually fell in two categories–exploiting the disorder for flexibility (like floppy connector), or imposing order upon binding to an external partner. In this study we explore the possibility of an alternative mechanism that harnesses disorder for function through regulated molecular crowding. Specifically, we use modeling to study two proteins involved in reshaping the cell membrane, Epsin and Eps15. We ask if they undergo binding-induced expansion, where binding of an external partner AP2 causes not a transition toward order, but rather an energetically favorable increase in propensity to occupy larger volumes. Our results show that Epsin tends to occupy a larger volume when bound to AP2, consistent with increased molecular crowding, which could help reshape the cell membrane. Such regulation of disorder via binding (without folding) opens hitherto unexplored avenues that cells might employ to harness disorder.

Comparison and Evaluation of Force Fields for Intrinsically Disordered Proteins

Molecular dynamics (MD) simulations of six upgraded empirical force fields were compared and evaluated with short peptides, intrinsically disordered proteins, and folded proteins using trajectories of 1, 1.5, 5, or 10 μs (five replicates of 200 ns, 300 ns, 1 μs, or 2 μs) for each system. Previous studies have shown that different force fields, water models, simulation methods, and parameters can affect simulation outcomes. Here, the MD simulations were done in an explicit solvent with RS-peptide, HEWL19, HIV-rev, β amyloid (Aβ)-40, Aβ-42, phosphodiesterase-γ, CspTm, and ubiquitin using ff99IDPs, ff14IDPs, ff14IDPSFF, ff03w, CHARMM36m, and CHARMM22* force fields. The IDP ensembles generated by six all-atom empirical force fields were compared against NMR data. Despite using identical starting structures and simulation parameters, ensembles obtained with different force fields exhibit significant differences in NMR RMDs, secondary structure contents, and global properties such as the radius of gyration. The intrinsically disordered protein (IDP)-specific force fields could substantially reproduce the experimental observables in force field comparison: they have the lowest error in chemical shifts and J-couplings for short peptides/proteins, reasonably well for large IDPs and reasonably well with the radius of gyration. A high population of disorderness was observed in the IDP-specific force field for the IDP ensemble with a fraction of β sheets for β-amyloids. CHARMM22* performs better for many observables; however, it still has a preference toward the helicity for short peptides. The results of β-amyloid 42 starting from two different initial structures (Aβ42^1Z0Q and Aβ42^model) were also compared with DSSP and NMR data. The results obtained with IDP-specific force fields within 2 μs simulation time are similar, even though starting from different structures. The current force fields perform equally well for folded proteins. The results of currently developed or modified force fields for IDPs are capable of enlightening the overall performance of the force field for disordered as well as folded proteins, thereby contributing to force field development.

Environment-Specific Force Field for Intrinsically Disordered and Ordered Proteins

The need for accurate and efficient force fields for modeling 3D structures of macrobiomolecules and in particular intrinsically disordered proteins (IDPs) has increased with recent findings to associate IDPs and human diseases. However, most conventional protein force fields and recent IDP-specific force fields are limited in reproducing accurate structural features of IDPs. Here, we present an environmental specific precise force field (ESFF1) based on CMAP corrections of 71 different sequence environments to improve the accuracy and efficiency of MD simulation for both IDPs and folded proteins. MD simulations of 84 different short peptides, IDPs, and structured proteins show that ESFF1 can accurately reproduce spectroscopic properties for different peptides and proteins whether they are disordered or ordered. The successful ab initio folding of five fast-folding proteins further supports the reliability of ESFF1. The extensive analysis documented here shows that ESFF1 is able to achieve a reasonable balance between ordered and disordered states in protein simulations.