Recent Force Field Strategies for Intrinsically Disordered Proteins.
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ABSTRACT:
SUBMITTER: Mu J
PROVIDER: S-EPMC8256680 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Intrinsically disordered proteins (IDPs) constitute a significant fraction of eukaryotic proteomes. High-resolution characterization of IDP conformational ensembles can help elucidate their roles in a wide range of biological processes but remains challenging both experimentally and computationally. Here, we present a generic algorithm to improve the accuracy of coarse-grained IDP models using a diverse set of experimental measurements. It combines maximum entropy optimization and least-squares regression to systematically adjust model parameters and improve the agreement between simulation and experiment. We successfully applied the algorithm to derive a transferable force field, which we term the maximum entropy optimized force field (MOFF), for de novo prediction of IDP structures. Statistical analysis of force field parameters reveals features of amino acid interactions not captured by potentials designed to work well for folded proteins. We anticipate its combination of efficiency and accuracy will make MOFF useful for studying the phase separation of IDPs, which drives the formation of various biological compartments.
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Project description:The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.
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Project description:Over 40% of eukaryotic proteomic sequences have been predicted to be intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) and confirmed to be associated with many diseases. However, widely used force fields cannot well reproduce the conformers of IDPs. Previously the ff99IDPs force field was released to simulate IDPs with CMAP energy corrections for the eight disorder-promoting residues. In order to further confirm the performance of ff99IDPs, three representative IDP systems (arginine-rich HIV-1 Rev, aspartic proteinase inhibitor IA3, and α-synuclein) were used to test and evaluate the simulation results. The results show that for free disordered proteins, the chemical shifts from the ff99IDPs simulations are in quantitative agreement with those from reported NMR measurements and better than those from ff99SBildn. Thus, ff99IDPs can sample more clusters of disordered conformers than ff99SBildn. For structural proteins, both ff99IDPs and ff99SBildn can well reproduce the conformations. In general, ff99IDPs can successfully be used to simulate the conformations of IDPs and IDRs in both bound and free states. However, relative errors could still be found at the boundaries of ordered residues scattered in long disorder-promoting sequences. Therefore, polarizable force fields might be one of the possible ways to further improve the performance on IDPs.
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Project description:The associative memory, water-mediated, structure and energy model (AWSEM) has been successfully used to study protein folding, binding, and aggregation problems. In this work, we introduce AWSEM-IDP, a new AWSEM branch for simulating intrinsically disordered proteins (IDPs), where the weights of the potentials determining secondary structure formation have been finely tuned, and a novel potential is introduced that helps to precisely control both the average extent of protein chain collapse and the chain's fluctuations in size. AWSEM-IDP can efficiently sample large conformational spaces, while retaining sufficient molecular accuracy to realistically model proteins. We applied this new model to two IDPs, demonstrating that AWSEM-IDP can reasonably well reproduce higher-resolution reference data, thus providing the foundation for a transferable IDP force field. Finally, we used thermodynamic perturbation theory to show that, in general, the conformational ensembles of IDPs are highly sensitive to fine-tuning of force field parameters.
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Project description:Biomolecular force fields optimized for globular proteins fail to properly reproduce properties of intrinsically disordered proteins. In particular, parameters of the water model need to be modified to improve applicability of the force fields to both ordered and disordered proteins. Here, we compared performance of force fields recommended for intrinsically disordered proteins in molecular dynamics simulations of three proteins differing in the content of ordered and disordered regions (two proteins consisting of a well-structured domain and of a disordered region with and without a transient helical motif and one disordered protein containing a region of increased helical propensity). The obtained molecular dynamics trajectories were used to predict measurable parameters, including radii of gyration of the proteins and chemical shifts, residual dipolar couplings, paramagnetic relaxation enhancement, and NMR relaxation data of their individual residues. The predicted quantities were compared with experimental data obtained within this study or published previously. The results showed that the NMR relaxation parameters, rarely used for benchmarking, are particularly sensitive to the choice of force-field parameters, especially those defining the water model. Interestingly, the TIP3P water model, leading to an artificial structural collapse, also resulted in unrealistic relaxation properties. The TIP4P-D water model, combined with three biomolecular force-field parameters for the protein part, significantly improved reliability of the simulations. Additional analysis revealed only one particular force field capable of retaining the transient helical motif observed in NMR experiments. The benchmarking protocol used in our study, being more sensitive to imperfections than the commonly used tests, is well suited to evaluate the performance of newly developed force fields.
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Project description:Phosphorylation is a common post-translational modification among intrinsically disordered proteins and regions, which helps regulate function by changing the protein conformations, dynamics, and interactions with binding partners. To fully comprehend the effects of phosphorylation, computer simulations are a helpful tool, although they are dependent on the accuracy of the force field used. Here, we compared the conformational ensembles produced by Amber ff99SB-ILDN+TIP4P-D and CHARMM36m, for four phosphorylated disordered peptides ranging in length from 14-43 residues. CHARMM36m consistently produced more compact conformations with a higher content of bends, mainly due to more stable salt bridges. Based on comparisons with experimental size estimates for the shortest and longest peptide, CHARMM36m appeared to overestimate the compactness. The difference between the force fields was largest for the peptide showing the greatest separation between positively charged and phosphorylated residues, in line with the importance of charge distribution. For this peptide, the conformational ensemble did not change significantly upon increasing the ionic strength from 0 mM to 150 mM, despite a reduction of the salt-bridging probability in the CHARMM36m simulations, implying that salt concentration has negligible effects in this study.
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Project description:Intrinsically disordered proteins (IDPs) or intrinsically disordered regions do not have a fixed tertiary structure but play key roles in signal regulation, molecule recognition, and drug targeting. However, it is difficult to study the structure and function of IDPs by traditional experimental methods because of their diverse conformations. Limitations of current generic protein force fields and solvent models were reported in the previous simulations of IDPs. We have also explored overcoming these limitations by developing the ff99IDPs and ff14IDPs force fields to correct the dihedral distribution for eight disorder-promoting residues often observed in IDPs and found encouraging improvements. Here we extend our correction of backbone dihedral terms to all 20 naturally occurring amino acids in the IDP-specific force field ff14IDPSFF to further improve the quality of the modeling of IDPs. Extensive tests of seven IDPs and 14 unstructured short peptides show that the simulated C? chemical shifts obtained with the ff14IDPSFF force field are in quantitative agreement with those from NMR experiments and are more accurate than those obtained with the base generic force field and also our previous ff14IDPs that only corrects the eight disorder-promoting amino acids. The influence of the solvent model was also investigated and found to be less important. Finally, our explicit-solvent MD simulations further show that ff14IDPSFF can still be used to model structural and dynamical properties of two tested folded proteins, with a slightly better agreement in the loop regions for both structural and dynamical properties. These findings confirm that the newly developed IDP-specific force field ff14IDPSFF can improve the conformer sampling of intrinsically disordered proteins.
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Project description:Intrinsically disordered proteins (IDPs) have received increasing attention in recent studies due to their structural heterogeneity and critical biological functions. To fully understand the structural properties and determine accurate ensembles of IDPs, molecular dynamics (MD) simulation was widely used to sample diverse conformations and reveal the structural dynamics. However, the classical state-of-the-art force fields perform well for folded proteins while being unsatisfactory for the simulations of disordered proteins reported in many previous studies. Thus, improved force fields were developed to precisely describe both folded proteins and disordered proteins. Preliminary tests show that our newly developed CHARMM36IDPSFF (C36IDPSFF) force field can well reproduce the experimental observables of several disordered proteins, but more tests on different types of proteins are needed to further evaluate the performance of C36IDPSFF. Here, we extensively simulate short peptides, disordered proteins, and fast-folding proteins as well as folded proteins, and compare the simulated results with the experimental observables. The simulation results show that C36IDPSFF could substantially reproduce the experimental observables for most of the tested proteins but some limitations are also found in the radius of gyration of large disordered proteins and the stability of fast-folding proteins. This force field will facilitate large scale studies of protein structural dynamics and functions using MD simulations.
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Project description:Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10-15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.
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Project description:Many different intrinsically disordered proteins and proteins with intrinsically disordered regions are associated with neurodegenerative diseases. These types of proteins including amyloid-β, tau, α-synuclein, CHCHD2, CHCHD10, and G-protein coupled receptors are increasingly becoming evaluated as potential drug targets in the pharmaceutical-based treatment approaches. Here, we focus on the neurobiology of this class of proteins, which lie at the center of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases, Huntington's disease, amyotrophic lateral sclerosis, frontotemporal dementia, Charcot-Marie-Tooth diseases, spinal muscular atrophy, and mitochondrial myopathy. Furthermore, we discuss the current treatment design strategies involving intrinsically disordered proteins and proteins with intrinsically disordered regions in neurodegenerative diseases. In addition, we emphasize that although the G-protein coupled receptors are traditionally investigated using structural biology-based models and approaches, current studies show that these receptors are proteins with intrinsically disordered regions and therefore they require new ways for their analysis.
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