Development and Validation of AMBER-FB15-Compatible Force Field Parameters for Phosphorylated Amino Acids

nP-Collabs: Investigating Counterion-Mediated Bridges in the Multiply Phosphorylated Tau-R2 Repeat

Tau is an intrinsically disordered (IDP) microtubule-associated protein (MAP) that plays a key part in microtubule assembly and organization. The function of tau can be regulated by multiple phosphorylation sites. These post-translational modifications are known to decrease the binding affinity of tau for microtubules, and abnormal tau phosphorylation patterns are involved in Alzheimer's disease. Using all-atom molecular dynamics simulations, we compared the conformational landscapes explored by the tau R2 repeat domain (which comprises a strong tubulin binding site) in its native state and with multiple phosphorylations on the S285, S289, and S293 residues, with four different standard force field (FF)/water model combinations. We find that the different parameters used for the phosphate groups (which can be more or less flexible) in these FFs and the specific interactions between bulk cations and water lead to the formation of a specific type of counterion bridge, termed nP-collab (for nphosphate collaboration, with n being an integer), where counterions form stable structures binding with two or three phosphate groups simultaneously. The resulting effect of nP-collabs on the tau-R2 conformational space differs when using sodium or potassium cations and is likely to impact the peptide overall dynamics and how this MAP interacts with tubulins. We also investigated the effect of phosphoresidue spacing and ionic concentration by modeling polyalanine peptides containing two phosphoserines located one-six residues apart. Three new metrics specifically tailored for IDPs (proteic Menger curvature, local curvature, and local flexibility) were introduced, which allow us to fully characterize the impact of nP-collabs on the dynamics of disordered peptides at the residue level.

HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information

In this study, we introduce a novel approach to enhance the accuracy of molecular dynamics simulations by refining the force fields (FFs) through a combination of transferable parameters and molecule-specific characteristics derived from quantum mechanical (QM) calculations. Traditional FFs often prioritize generality over precision, leading to limitations in the accuracy of accurately capturing intra- and intermolecular interactions. To address this, we present an open-source toolkit, called HessFit, designed to integrate QM-derived bonded parameters and atomic charges into existing FFs. In combination with bond, angle, torsional, and nonbonded parameters derivation, HessFit can easily extract multiple barrier terms of dihedrals from QM Hessian and gradient or return all terms through a fitting procedure scheme of QM potential energy surface. We showcase the effectiveness of HessFit through comprehensive evaluations of vibrational properties across a diverse set of small molecules and demonstrate that experimental results support its ability in predicting thermodynamic properties of organic molecules compared to previous state-of-the-art approaches. We further explore its application to Zn2+ metalloprotein models, which are hard systems to treat with automatic approaches. Our results demonstrate that HessFit parameters compete with GAFF2 and OPLS parameters to describing small organic molecules, and its feasibility is also comparable to current FFs used to modeling nonstandard residues in Zn proteins for molecular dynamics simulations. The effectiveness of the HessFit protocol makes it a valuable tool for deriving or extending force field parameters for novel compounds in several molecular modeling applications.

Machine Learning Subtle Conformational Change due to Phosphorylation in Intrinsically Disordered Proteins

Phosphorylation of intrinsically disordered proteins/regions (IDPs/IDRs) has a profound effect in biological functions such as cell signaling, protein folding or unfolding, and long-range allosteric effects. However, here we focus on two IDPs, namely 83-residue IDR transcription factor Ash1 and 92-residue long N-terminal region of CDK inhibitor Sic1 protein, found in Saccharomyces cerevisiae, for which experimental measurements of average conformational properties, namely, radius of gyration and structure factor, indicate negligible changes upon phosphorylation. Here, we show that a judicious dissection of conformational ensemble via combination of unsupervised machine learning and extensive molecular dynamics (MD) trajectories can highlight key differences and similarities among the phosphorylated and wild-type IDP. In particular, we develop Markov state model (MSM) using the latent-space dimensions of an autoencoder, trained using multi-microsecond long MD simulation trajectories. Examination of structural changes among the states, prior to and upon phosphorylation, captured several similarities and differences in their backbone contact maps, secondary structure, and torsion angles. Hydrogen bonding analysis revealed that phosphorylation not only increases the number of hydrogen bonds but also switches the pattern of hydrogen bonding between the backbone and side chain atoms with the phosphorylated residues. We also observe that although phosphorylation introduces salt bridges, there is a loss of the cation-π interaction. Phosphorylation also improved the probability for long-range hydrophobic contacts and also enhanced interaction with water molecules and improved the local structure of water as evident from the geometric order parameters. The observations on these machine-learnt states gave important insights, as it would otherwise be difficult to determine experimentally which is important, if we were to understand the role of phosphorylation of IDPs in their biological functions.

Molecular-dynamics simulations of macromolecular diffraction, part I: Preparation of protein crystal simulations

Molecular-dynamics (MD) simulations of protein crystals enable the prediction of structural and dynamical features of both the protein and the solvent components of macromolecular crystals, which can be validated against diffraction data from X-ray crystallographic experiments. The simulations have been useful for studying and predicting both Bragg and diffuse scattering in protein crystallography; however, the preparation is not yet automated and includes choices and tradeoffs that can impact the results. Here we examine some of the intricacies and consequences of the choices involved in setting up MD simulations of protein crystals for the study of diffraction data, and provide a recipe for preparing the simulations, packaged in an accompanying Jupyter notebook. This article and the accompanying notebook are intended to serve as practical resources for researchers wishing to put these models to work.

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.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

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.

Development and Validation of Fluorinated, Aromatic Amino Acid Parameters for Use with the AMBER ff15ipq Protein Force Field

We developed force field parameters for fluorinated, aromatic amino acids enabling molecular dynamics (MD) simulations of fluorinated proteins. These parameters are tailored to the AMBER ff15ipq protein force field and enable the modeling of 4, 5, 6, and 7F-tryptophan, 3F- and 3,5F-tyrosine, and 4F- or 4-CF3-phenylalanine. The parameters include 181 unique atomic charges derived using the implicitly polarized charge (IPolQ) scheme in the presence of SPC/Eb explicit water molecules and 9 unique bond, angle, or torsion terms. Our simulations of benchmark peptides and proteins maintain expected conformational propensities on the μs time scale. In addition, we have developed an open-source Python program to calculate fluorine relaxation rates from MD simulations. The extracted relaxation rates from protein simulations are in good agreement with experimental values determined by 19F NMR. Collectively, our results illustrate the power and robustness of the IPolQ lineage of force fields for modeling the structure and dynamics of fluorine-containing proteins at the atomic level.