Experimentally assessing molecular dynamics sampling of the protein native state conformational distribution

Advanced computational approaches to understand protein aggregation

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

Transient conformations in the unliganded FK506 binding domain of FKBP51 correspond to two distinct inhibitor-bound states

Members of the FK506-binding protein (FKBP) family regulate a range of important physiological processes. Unfortunately, current therapeutics such as FK506 and rapamycin exhibit only modest selectivity among these functionally distinct proteins. Recent progress in developing selective inhibitors has been reported for FKBP51 and FKBP52, which act as mutual antagonists in the regulation of steroid hormone signaling. Two structurally similar inhibitors yield distinct protein conformations at the binding site. Localized conformational transition in the binding site of the unliganded FK1 domain of FKBP51 is suppressed by a K58T mutation that also suppresses the binding of these inhibitors. Here, it is shown that the changes in amide hydrogen exchange kinetics arising from this K58T substitution are largely localized to this structural region. Accurate determination of the hydroxide-catalyzed exchange rate constants in both the wildtype and K58T variant proteins impose strong constraints upon the pattern of amide exchange reactivities within either a single or a pair of transient conformations that could give rise to the differences between these two sets of measured rate constants. Poisson-Boltzmann continuum dielectric calculations provide moderately accurate predictions of the structure-dependent hydrogen exchange reactivity for solvent-exposed protein backbone amides. Applying such calculations to the local protein conformations observed in the two inhibitor-bound FKBP51 domains demonstrated that the experimentally determined exchange rate constants for the wildtype domain are robustly predicted by a population-weighted sum of the experimental hydrogen exchange reactivity of the K58T variant and the predicted exchange reactivities in model conformations derived from the two inhibitor-bound protein structures.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Prediction and Validation of a Protein's Free Energy Surface Using Hydrogen Exchange and (Importantly) Its Denaturant Dependence

The denaturant dependence of hydrogen-deuterium exchange (HDX) is a powerful measurement to identify the breaking of individual H-bonds and map the free energy surface (FES) of a protein including the very rare states. Molecular dynamics (MD) can identify each partial unfolding event with atomic-level resolution. Hence, their combination provides a great opportunity to test the accuracy of simulations and to verify the interpretation of HDX data. For this comparison, we use Upside, our new and extremely fast MD package that is capable of folding proteins with an accuracy comparable to that of all-atom methods. The FESs of two naturally occurring and two designed proteins are so generated and compared to our NMR/HDX data. We find that Upside's accuracy is considerably improved upon modifying the energy function using a new machine-learning procedure that trains for proper protein behavior including realistic denatured states in addition to stable native states. The resulting increase in cooperativity is critical for replicating the HDX data and protein stability, indicating that we have properly encoded the underlying physiochemical interactions into an MD package. We did observe some mismatch, however, underscoring the ongoing challenges faced by simulations in calculating accurate FESs. Nevertheless, our ensembles can identify the properties of the fluctuations that lead to HDX, whether they be small-, medium-, or large-scale openings, and can speak to the breadth of the native ensemble that has been a matter of debate.

Insights from molecular dynamics simulations for computational protein design

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations was used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

How amide hydrogens exchange in native proteins

Amide hydrogen exchange (HX) is widely used in protein biophysics even though our ignorance about the HX mechanism makes data interpretation imprecise. Notably, the open exchange-competent conformational state has not been identified. Based on analysis of an ultralong molecular dynamics trajectory of the protein BPTI, we propose that the open (O) states for amides that exchange by subglobal fluctuations are locally distorted conformations with two water molecules directly coordinated to the N-H group. The HX protection factors computed from the relative O-state populations agree well with experiment. The O states of different amides show little or no temporal correlation, even if adjacent residues unfold cooperatively. The mean residence time of the O state is ∼100 ps for all examined amides, so the large variation in measured HX rate must be attributed to the opening frequency. A few amides gain solvent access via tunnels or pores penetrated by water chains including native internal water molecules, but most amides access solvent by more local structural distortions. In either case, we argue that an overcoordinated N-H group is necessary for efficient proton transfer by Grotthuss-type structural diffusion.

Chemical exchange in biomacromolecules: past, present, and future

The perspective reviews quantitative investigations of chemical exchange phenomena in proteins and other biological macromolecules using NMR spectroscopy, particularly relaxation dispersion methods. The emphasis is on techniques and applications that quantify the populations, interconversion kinetics, and structural features of sparsely populated conformational states in equilibrium with a highly populated ground state. Applications to folding, molecular recognition, catalysis, and allostery by proteins and nucleic acids are highlighted.

Analysing the visible conformational substates of the FK506-binding protein FKBP12

The ¹H-¹⁵N 2D NMR correlation spectrum of the widely studied FK506-binding protein FKBP12 (FK506-binding protein of 12 kDa) contains previously unreported peak doublings for at least 31 residues that arise from a minor conformational state (12% of total) which exchanges with the major conformation with a time constant of 3.0 s at 43°C. The largest differences in chemical shift occur for the 80′s loop that forms critical recognition interactions with many of the protein partners for the FKBP family. The residues exhibiting doubling extend into the adjacent strands of the β-sheet, across the active site to the α-helix and into the 50′s loop. Each of the seven proline residues adopts a trans-peptide linkage in both the major and minor conformations, indicating that this slow transition is not the result of prolyl isomerization. Many of the residues exhibiting resonance doubling also participate in conformational line-broadening transition(s) that occur ~10⁵-fold more rapidly, proposed previously to arise from a single global process. The 1.70 Å (1 Å=0.1 nm) resolution X-ray structure of the H87V variant is strikingly similar to that of FKBP12, yet this substitution quenches the slow conformational transition throughout the protein while quenching the line-broadening transition for residues near the 80′s loop. Line-broadening was also decreased for the residues in the α-helix and 50′s loop, whereas line-broadening in the 40′s loop was unaffected. The K44V mutation selectively reduces the line-broadening in the 40′s loop, verifying that at least three distinct conformational transitions underlie the line-broadening processes of FKBP12.

Analysing the visible conformational substates of the FK506-binding protein FKBP12

The 1H-15N 2D NMR correlation spectrum of the widely studied FK506-binding protein FKBP12 (FK506-binding protein of 12 kDa) contains previously unreported peak doublings for at least 31 residues that arise from a minor conformational state (12% of total) which exchanges with the major conformation with a time constant of 3.0 s at 43°C. The largest differences in chemical shift occur for the 80's loop that forms critical recognition interactions with many of the protein partners for the FKBP family. The residues exhibiting doubling extend into the adjacent strands of the β-sheet, across the active site to the α-helix and into the 50's loop. Each of the seven proline residues adopts a trans-peptide linkage in both the major and minor conformations, indicating that this slow transition is not the result of prolyl isomerization. Many of the residues exhibiting resonance doubling also participate in conformational line-broadening transition(s) that occur ~105-fold more rapidly, proposed previously to arise from a single global process. The 1.70 Å (1 Å=0.1 nm) resolution X-ray structure of the H87V variant is strikingly similar to that of FKBP12, yet this substitution quenches the slow conformational transition throughout the protein while quenching the line-broadening transition for residues near the 80's loop. Line-broadening was also decreased for the residues in the α-helix and 50's loop, whereas line-broadening in the 40's loop was unaffected. The K44V mutation selectively reduces the line-broadening in the 40's loop, verifying that at least three distinct conformational transitions underlie the line-broadening processes of FKBP12.

Assessing the chemical accuracy of protein structures via peptide acidity

Although the protein native state is a Boltzmann conformational ensemble, practical applications often require a representative model from the most populated region of that distribution. The acidity of the backbone amides, as reflected in hydrogen exchange rates, is exquisitely sensitive to the surrounding charge and dielectric volume distribution. For each of four proteins, three independently determined X-ray structures of differing crystallographic resolution were used to predict exchange for the static solvent-exposed amide hydrogens. The average correlation coefficients range from 0.74 for ubiquitin to 0.93 for Pyrococcus furiosus rubredoxin, reflecting the larger range of experimental exchange rates exhibited by the latter protein. The exchange prediction errors modestly correlate with the crystallographic resolution. MODELLER 9v6-derived homology models at ~60% sequence identity (36% identity for chymotrypsin inhibitor CI2) yielded correlation coefficients that are ~0.1 smaller than for the cognate X-ray structures. The most recently deposited NOE-based ubiquitin structure and the original NMR structure of CI2 fail to provide statistically significant predictions of hydrogen exchange. However, the more recent RECOORD refinement study of CI2 yielded predictions comparable to the X-ray and homology model-based analyses.