Context-independent, temperature-dependent helical propensities for amino acid residues

A synergy between site-specific and transient interactions drives the phase separation of a disordered, low-complexity domain

TAR DNA-binding protein 43 (TDP-43) is involved in key processes in RNA metabolism and is frequently implicated in many neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia. The prion-like, disordered C-terminal domain (CTD) of TDP-43 is aggregation-prone, can undergo liquid-liquid phase separation (LLPS) in isolation, and is critical for phase separation (PS) of the full-length protein under physiological conditions. While a short conserved helical region (CR, spanning residues 319-341) promotes oligomerization and is essential for LLPS, aromatic residues in the flanking disordered regions (QN-rich, IDR1/2) are also found to play a critical role in PS and aggregation. Compared with other phase-separating proteins, TDP-43 CTD has a notably distinct sequence composition including many aliphatic residues such as methionine and leucine. Aliphatic residues were previously suggested to modulate the apparent viscosity of the resulting phases, but their direct contribution toward CTD phase separation has been relatively ignored. Using multiscale simulations coupled with in vitro saturation concentration (csat) measurements, we identified the importance of aromatic residues while also suggesting an essential role for aliphatic methionine residues in promoting single-chain compaction and LLPS. Surprisingly, NMR experiments showed that transient interactions involving phenylalanine and methionine residues in the disordered flanking regions can directly enhance site-specific, CR-mediated intermolecular association. Overall, our work highlights an underappreciated mode of biomolecular recognition, wherein both transient and site-specific hydrophobic interactions act synergistically to drive the oligomerization and phase separation of a disordered, low-complexity domain.

Impact of non-proteinogenic amino acid norvaline and proteinogenic valine misincorporation on a secondary structure of a model peptide

Norvaline is a straight-chain, hydrophobic, non-proteinogenic amino acid, isomeric with valine. Both amino acids can be misincorporated into proteins at isoleucine positions by isoleucyl-tRNA synthetase when the mechanisms of translation fidelity are impaired. Our previous study showed that the proteome-wide substitution of isoleucine with norvaline resulted in higher toxicity in comparison to the proteome-wide substitution of isoleucine with valine. Although mistranslated proteins/peptides are considered to have non-native structures responsible for their toxicity, the observed difference in protein stability between norvaline and valine misincorporation has not yet been fully understood. To examine the observed effect, we chose the model peptide with three isoleucines in the native structure, introduced selected amino acids at isoleucine positions and applied molecular dynamics simulations at different temperatures. The obtained results showed that norvaline has the highest destructive effect on the β-sheet structure and suggested that the higher toxicity of norvaline over valine is predominantly due to the misincorporation within the β-sheet secondary elements.

Developing Bonded Potentials for a Coarse-Grained Model of Intrinsically Disordered Proteins

Recent advances in residue-level coarse-grained (CG) computational models have enabled molecular-level insights into biological condensates of intrinsically disordered proteins (IDPs), shedding light on the sequence determinants of their phase separation. The existing CG models that treat protein chains as flexible molecules connected via harmonic bonds cannot populate common secondary-structure elements. Here, we present a CG dihedral angle potential between four neighboring beads centered at Cα atoms to faithfully capture the transient helical structures of IDPs. In order to parameterize and validate our new model, we propose Cα-based helix assignment rules based on dihedral angles that succeed in reproducing the atomistic helicity results of a polyalanine peptide and folded proteins. We then introduce sequence-dependent dihedral angle potential parameters (εd) and use experimentally available helical propensities of naturally occurring 20 amino acids to find their optimal values. The single-chain helical propensities from the CG simulations for commonly studied prion-like IDPs are in excellent agreement with the NMR-based α-helix fraction, demonstrating that the new HPS-SS model can accurately produce structural features of IDPs. Furthermore, this model can be easily implemented for large-scale assembly simulations due to its simplicity.

Met35 Oxidation Hinders Aβ25-35 Peptide Aggregation within the Dimyristoylphosphatidylcholine Bilayer

Using all-atom explicit solvent replica exchange molecular dynamics simulations, we studied the aggregation of oxidized (ox) Aβ25-35 peptides into dimers mediated by the zwitterionic dimyristoylphosphatidylcholine (DMPC) lipid bilayer. By comparing oxAβ25-35 aggregation with that observed for reduced and phosphorylated Aβ25-35 peptides, we elucidated plausible impact of post-translational modifications on cytotoxicity of Aβ peptides involved in Alzheimer's disease. We found that Met35 oxidation reduces helical propensity in oxAβ25-35 peptides bound to the lipid bilayer and enhances backbone fluctuations. These factors destabilize the wild-type head-to-tail dimer interface and lower the aggregation propensity. Met35 oxidation diversifies aggregation pathways by adding monomeric species to the bound conformational ensemble. The oxAβ25-35 dimer becomes partially expelled from the DMPC bilayer and as a result inflicts limited disruption to the bilayer structure compared to wild-type Aβ25-35. Interestingly, the effect of Ser26 phosphorylation is largely opposite, as it preserves the wild-type head-to-tail aggregation interface and strengthens, not weakens, aggregation propensity. The differing effects can be attributed to the sequence locations of these post-translational modifications, since in contrast to Ser26 phosphorylation, Met35 oxidation directly affects the wild-type C-terminal aggregation interface. A comparison with experimental data is provided.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

On the Role of Entropy in the Stabilization of α-Helices

Protein folding evolves by exploring the conformational space with a subtle balance between enthalpy and entropy changes which eventually leads to a decrease of free energy upon reaching the folded structure. A complete understanding of this process requires, therefore, a deep insight into both contributions to free energy. In this work, we clarify the role of entropy in favoring the stabilization of folded structures in polyalanine peptides with up to 12 residues. We use a novel method referred to as K2V that allows us to obtain the potential-energy landscapes in terms of residue conformations extracted from molecular dynamics simulations at conformational equilibrium and yields folding thermodynamic magnitudes, which are in agreement with the experimental data available. Our results demonstrate that the folded structures of the larger polyalanine chains are stabilized with respect to the folded structures of the shorter chains by both an energetic contribution coming from the formation of the intramolecular hydrogen bonds and an entropic contribution coming from an increase of the entropy of the solvent with approximate weights of 60 and 40%, respectively, thus unveiling a key piece in the puzzle of protein folding. In addition, the ability of the K2V method to provide the enthalpic and entropic contributions for individual residues along the peptide chain makes it clear that the energetic and entropic stabilizations are basically governed by the nearest neighbor residue conformations, with the folding propensity being rationalized in terms of triads of residues.

Development of a Force Field for the Simulation of Single-Chain Proteins and Protein-Protein Complexes

The accuracy of atomistic physics-based force fields for the simulation of biological macromolecules has typically been benchmarked experimentally using biophysical data from simple, often single-chain systems. In the case of proteins, the careful refinement of force field parameters associated with torsion-angle potentials and the use of improved water models have enabled a great deal of progress toward the highly accurate simulation of such monomeric systems in both folded and, more recently, disordered states. In living organisms, however, proteins constantly interact with other macromolecules, such as proteins and nucleic acids, and these interactions are often essential for proper biological function. Here, we show that state-of-the-art force fields tuned to provide an accurate description of both ordered and disordered proteins can be limited in their ability to accurately describe protein-protein complexes. This observation prompted us to perform an extensive reparameterization of one variant of the Amber protein force field. Our objective involved refitting not only the parameters associated with torsion-angle potentials but also the parameters used to model nonbonded interactions, the specification of which is expected to be central to the accurate description of multicomponent systems. The resulting force field, which we call DES-Amber, allows for more accurate simulations of protein-protein complexes, while still providing a state-of-the-art description of both ordered and disordered single-chain proteins. Despite the improvements, calculated protein-protein association free energies still appear to deviate substantially from experiment, a result suggesting that more fundamental changes to the force field, such as the explicit treatment of polarization effects, may simultaneously further improve the modeling of single-chain proteins and protein-protein complexes.

ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution

Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.

Arginine side chain stacking with peptide plane stabilizes the protein helix conformation in a cooperative way

A combined experimental and computational study is performed for arginine side chain stacking with the protein α-helix. Theremostability measurements of Aristaless homeodomain, a helical protein, suggest that mutating the arginine residue R106, R137 or R141, which has the guanidino side chain stacking with the peptide plane, to alanine, destabilizes the protein. The R-PP stacking has an energy of ∼0.2-0.4 kcal/mol. This stacking interaction mainly comes from dispersion and electrostatics, based on MP2 calculations with the energy decomposition analysis. The calculations also suggest that the stacking stabilizes 2 backbone-backbone h-bonds (i→i-4 and i-3→i-7) in a cooperative way. Desolvation and electrostatic polarization are responsible for cooperativity with the i→i-4 and i-3→i-7 h-bonds, respectively. This cooperativity is supported by a protein α-helices h-bond survey in the pdb databank where stacking shortens the corresponding h-bond distances.

Using Cooperatively Folded Peptides To Measure Interaction Energies and Conformational Propensities

The rates and equilibria of the folding of biopolymers are determined by the conformational preferences of the subunits that make up the sequence of the biopolymer and by the interactions that are formed in the folded state in aqueous solution. Because of the centrality of these processes to life, quantifying conformational propensities and interaction strengths is vitally important to understanding biology. In this Account, we describe our use of peptide model systems that fold cooperatively yet are small enough to be chemically synthesized to measure such quantities. The necessary measurements are made by perturbing an interaction or conformation of interest by mutation and measuring the difference between the folding free energies of the wild type (in which the interaction or conformation is undisturbed) and the mutant model peptides (in which the interaction has been eliminated or the conformational propensities modified). With the proper controls and provided that the peptide model system in question folds via a two-state process, these folding free energy differences can be accurate measures of interaction strengths or conformational propensities. This method has the advantage of having high sensitivity and high dynamic range because the energies of interest are coupled to folding free energies, which can be measured with precisions on the order of a few tenths of a kilocalorie by well-established biophysical methods, like chaotrope or thermal denaturation studies monitored by fluorescence or circular dichroism. In addition, because the model peptides can be chemically synthesized, the full arsenal of natural and unnatural amino acids can be used to tune perturbations to be as drastic or subtle as desired. This feature is particularly noteworthy because it enables the use of analytical tools developed for physical organic chemistry, especially linear free energy relationships, to decompose interaction energies into their component parts to obtain a deeper understanding of the forces that drive interactions in biopolymers. We have used this approach, primarily with the WW domain derived from the human Pin1 protein as our model system, to assess hydrogen bond strengths (especially those formed by backbone amides), the dependence of hydrogen bond strengths on the environment in which they form, β-turn propensities of both natural sequences and small molecule β-turn mimics, and the energetics of carbohydrate-protein interactions. In each case, the combination of synthetic accessibility, the ease of measuring folding energies, and the robustness of the structure of the Pin1 WW domain to mutation enabled us to obtain incisive measurements of quantities that have been challenging to measure by other methods.

Computational Prediction of New Intein Split Sites

Split inteins have emerged as a powerful tool in protein engineering. We describe a reliable in silico method to predict viable split sites for the design of new split inteins. A computational circular permutation (CP) prediction method facilitates the search for internal permissive sites to create artificial circular permutants. In this procedure, the original amino- and carboxyl-termini are connected and new termini are created. The identified new terminal sites are promising candidates for the generation of new split sites with the backbone opening being tolerated by the structural scaffold. Here we show how to integrate the online usage of the CP predictor, CPred, in the search of new split intein sites.

Advances in free-energy-based simulations of protein folding and ligand binding

Free-energy-based simulations are increasingly providing the narratives about the structures, dynamics and biological mechanisms that constitute the fabric of protein science. Here, we review two recent successes. It is becoming practical: first, to fold small proteins with free-energy methods without knowing substructures and second, to compute ligand-protein binding affinities, not just their binding poses. Over the past 40 years, the timescales that can be simulated by atomistic MD are doubling every 1.3 years--which is faster than Moore's law. Thus, these advances are not simply due to the availability of faster computers. Force fields, solvation models and simulation methodology have kept pace with computing advancements, and are now quite good. At the tip of the spear recently are GPU-based computing, improved fast-solvation methods, continued advances in force fields, and conformational sampling methods that harness external information.

Conformation-specific spectroscopy of capped, gas-phase Aib oligomers: tests of the Aib residue as a 310-helix former

Single-conformation spectroscopy is used to probe the preference for helical structural in Aib-homopeptides.

Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations

Force fields, such as Amber's ff12SB, can be fairly accurate models of the physical forces in proteins and other biomolecules. When coupled with accurate solvation models, force fields are able to bring insight into the conformational preferences, transitions, pathways, and free energies for these biomolecules. When computational speed/cost matters, implicit solvent is often used but at the cost of accuracy. We present an empirical grid-like correction term, in the spirit of cMAPs, to the combination of the ff12SB protein force field and the GBneck2 implicit-solvent model. Ff12SB-cMAP is parametrized on experimental helicity data. We provide validation on a set of peptides and proteins. Ff12SB-cMAP successfully improves the secondary structure biases observed in ff12SB + Gbneck2. Ff12SB-cMAP can be downloaded ( https://github.com/laufercenter/Amap.git ) and used within the Amber package. It can improve the agreement of force fields + implicit solvent with experiments.

Dependence of internal friction on folding mechanism

An outstanding challenge in protein folding is understanding the origin of "internal friction" in folding dynamics, experimentally identified from the dependence of folding rates on solvent viscosity. A possible origin suggested by simulation is the crossing of local torsion barriers. However, it was unclear why internal friction varied from protein to protein or for different folding barriers of the same protein. Using all-atom simulations with variable solvent viscosity, in conjunction with transition-path sampling to obtain reaction rates and analysis via Markov state models, we are able to determine the internal friction in the folding of several peptides and miniproteins. In agreement with experiment, we find that the folding events with greatest internal friction are those that mainly involve helix formation, while hairpin formation exhibits little or no evidence of friction. Via a careful analysis of folding transition paths, we show that internal friction arises when torsion angle changes are an important part of the folding mechanism near the folding free energy barrier. These results suggest an explanation for the variation of internal friction effects from protein to protein and across the energy landscape of the same protein.

Protonation-dependent conformational variability of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by substantial conformational plasticity and undergo rearrangements of the time-averaged conformational ensemble on changes of environmental conditions (e.g., in ionic strength, pH, molecular crowding). In contrast to stably folded proteins, IDPs often form compact conformations at acidic pH. The biological relevance of this process was, for example, demonstrated by nuclear magnetic resonance studies of the aggregation prone (low pH) state of α-synuclein. In this study, we report a large-scale analysis of the pH dependence of disordered proteins using the recently developed meta-structure approach. The meta-structure analysis of a large set of IDPs revealed a significant tendency of IDPs to form α-helical secondary structure elements and to preferentially fold into more compact structures under acidic conditions. The predictive validity of this novel approach was demonstrated with applications to the tumor-suppressor BASP1 and the transcription factor Tcf4.

Inclusion of many-body effects in the additive CHARMM protein CMAP potential results in enhanced cooperativity of α-helix and β-hairpin formation

Folding simulations on peptides and proteins using empirical force fields have demonstrated the sensitivity of the results to details of the backbone potential. A recently revised version of the additive CHARMM protein force field, which includes optimization of the backbone CMAP potential to achieve good balance between different types of secondary structure, correcting the α-helical bias present in the former CHARMM22/CMAP energy function, is shown to result in improved cooperativity for the helix-coil transition. This is due to retention of the empirical corrections introduced in the original CMAP to reproduce folded protein structures-corrections that capture many-body effects missing from an energy surface fitted to gas phase calculations on dipeptides. The experimental temperature dependence of helix formation in (AAQAA)(3) and parameters for helix nucleation and elongation are in much better agreement with experiment than those obtained with other recent force fields. In contrast, CMAP parameters derived by fitting to a vacuum quantum mechanical surface for the alanine dipeptide do not reproduce the enhanced cooperativity, showing that the empirical backbone corrections, and not some other feature of the force field, are responsible. We also find that the cooperativity of β-hairpin formation is much improved relative to other force fields we have studied. Comparison with (ϕ,ψ) distributions from the Protein Data Bank further justifies the inclusion of many-body effects in the CMAP. These results suggest that the revised energy function will be suitable for both simulations of unfolded or intrinsically disordered proteins and for investigating protein-folding mechanisms.

Residue-specific α-helix propensities from molecular simulation

Formation of α-helices is a fundamental process in protein folding and assembly. By studying helix formation in molecular simulations of a series of alanine-based peptides, we obtain the temperature-dependent α-helix propensities of all 20 naturally occurring residues with two recent additive force fields, Amber ff03w and Amber ff99SB(∗). Encouragingly, we find that the overall helix propensity of many residues is captured well by both energy functions, with Amber ff99SB(∗) being more accurate. Nonetheless, there are some residues that deviate considerably from experiment, which can be attributed to two aspects of the energy function: i), variations of the charge model used to determine the atomic partial charges, with residues whose backbone charges differ most from alanine tending to have the largest error; ii), side-chain torsion potentials, as illustrated by the effect of modifications to the torsion angles of I, L, D, N. We find that constrained refitting of residue charges for charged residues in Amber ff99SB(∗) significantly improves their helix propensity. The resulting parameters should more faithfully reproduce helix propensities in simulations of protein folding and disordered proteins.

Deciphering the preference and predicting the viability of circular permutations in proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology.

Dissecting the functions of conserved prolines within transmembrane helices of the D2 dopamine receptor

G protein-coupled receptors (GPCRs) contain a number of conserved proline residues in their transmembrane helices, and it is generally assumed these play important functional and/or structural roles. Here we use unnatural amino acid mutagenesis, employing α-hydroxy acids and proline analogues, to examine the functional roles of five proline residues in the transmembrane helices of the D2 dopamine receptor. The well-known tendency of proline to disrupt helical structure is important at all sites, while we find no evidence for a functional role for backbone amide cis-trans isomerization, another feature associated with proline. At most proline sites, the loss of the backbone NH is sufficient to explain the role of the proline. However, at one site, P210(5.50), a substituent on the backbone N appears to be essential for proper function. Interestingly, the pattern in functional consequences that we see is mirrored in the pattern of structural distortions seen in recent GPCR crystal structures.