Deciphering Protein Dynamics from NMR Data Using Explicit Structure Sampling and Selection

Protein engineering, the process by which novel proteins with desired properties are developed, has grown by leaps and bounds since the first examples of protein mutagenesis were described over three decades ago. In the last 30 years, protein engineers have successfully created a wide range of proteins tailored to specific industrial, medical, and research applications. Yet challenges remain that prevent the engineering of complex protein functions on demand. With the aim of creating a forum for the protein engineering community, the inaugural Protein Engineering Canada Conference was held on June 20–22, 2014, in Ottawa, Canada. Over the course of the 2-day conference, 115 protein scientists from over 30 institutions representing 5 countries shared ideas, networked with colleagues, and learned about exciting new research covering a broad array of topics such as enzyme engineering, computational protein design, X-ray crystallography, protein NMR, molecular modeling, and protein evolution. This special issue highlights the breadth of research that was covered during the Protein Engineering Canada Conference by including previously unpublished research articles submitted by speakers as well as some selections that were submitted directly to Protein Science.

Protein function hinges on dynamic shifts of tertiary structure, but it remains challenging to map protein conformational landscapes at high resolution using traditional approaches in structural biology. Our lab leverages avant-garde computational and experimental X-ray crystallographic methods to illuminate protein structural excursions to excited states, and explores their fundamental connections to biological functions including enzyme catalysis and allostery. In previous work, we and others have shown that crystallography at room temperature (RT), in contrast to the usual cryogenic temperature (cryo), reveals alternative protein and ligand conformations. More recently, we sought to contrast the effects of temperature to those of a complementary biophysical perturbation that has been under-explored by crystallography: pressure. Using the idiosyncratic therapeutic target enzyme STEP (PTPN5), we quantitatively explored the temperature and pressure axes with respect to protein structure. Our results reveal that high temperature vs. high pressure have different effects on global unit cell and protein volumes, ordered solvation patterns, and protein conformational ensembles, with each perturbation stabilizing distinct aspects of active-like STEP states (Guerrero*, Ebrahim*, Comms Bio, 2024). Overall, our lab's research highlights the importance of moving beyond the single-structure paradigm to build a more fundamental understanding of dynamic protein function.

Protein dynamics play a crucial role in their diverse functions. The intracellular environment significantly influences protein dynamics, particularly for intrinsically disordered proteins (IDPs). To comprehensively capture structural information from various proteins wihtin cells and characterize protein dynamics, chemical cross-linking mass spectrometry was employed. In this study, we introduce a hierarchical decoding strategy that enables the investigation of protein dynamics in vivo. Computational analysis based on distance restraints derived from cross-links is used to infer protein dynamics in cells. To facilitate this analysis, we leverage the prior structure obtained from AlphaFold2. By employing this strategy, we can characterize the full-length structure of multi-domain proteins taking into account their distinct dynamic features. Furthermore, by combining restraint sampling with an unbiased sampling and evaluation approach, we can provide a comprehensive description of the intrinsic motion of IDPs. Consequently, the hierarchical strategy we propose holds significant potential in advancing our understanding of the molecular mechanisms that undelie protein functions in cells.

Protein dynamics play a crucial role in their diverse functions. The intracellular environment significantly influences protein dynamics, particularly for intrinsically disordered proteins (IDPs). To comprehensively capture structural information from various proteins within cells and characterize protein dynamics, chemical cross‐linking mass spectrometry was employed. In this study, we introduce a hierarchical decoding strategy that enables the investigation of protein dynamics in vivo. Computational analysis based on distance restraints derived from cross‐links is used to infer protein dynamics in cells. To facilitate this analysis, we leverage the prior structure obtained from AlphaFold2. By employing this strategy, we can characterize the full‐length structure of multi‐domain proteins taking into account their distinct dynamic features. Furthermore, by combining restraint sampling with an unbiased sampling and evaluation approach, we can provide a comprehensive description of the intrinsic motion of IDPs. Consequently, the hierarchical strategy we propose holds significant potential in advancing our understanding of the molecular mechanisms that undelie protein functions in cells.

Ensemble-function relationships: From qualitative to quantitative relationships between protein structure and function

It is well-established that dynamics are central to protein function; their importance is implicitly acknowledged in the principles of the Monod, Wyman and Changeux model of binding cooperativity, which was originally proposed in 1965. Nowadays the concept of protein dynamics is formulated in terms of the energy landscape theory, which can be used to understand protein folding and conformational changes in proteins. Because protein dynamics are so important, a key to understanding protein function at the molecular level is to design experiments that allow their quantitative analysis. Nuclear magnetic resonance (NMR) spectroscopy is uniquely suited for this purpose because major advances in theory, hardware, and experimental methods have made it possible to characterize protein dynamics at an unprecedented level of detail. Unique features of NMR include the ability to quantify dynamics (i) under equilibrium conditions without external perturbations, (ii) using many probes simultaneously, and (iii) over large time intervals. Here we review NMR techniques for quantifying protein dynamics on fast (ps-ns), slow (μs-ms), and very slow (s-min) time scales. These techniques are discussed with reference to some major discoveries in protein science that have been made possible by NMR spectroscopy.

An integrated view of protein structure, dynamics, and function is emerging, where proteins are considered as dynamically active assemblies and internal motions are closely linked to function such as enzyme catalysis. Further, the motion of solvent bound to external regions of protein impacts internal motions and, therefore, protein function. Recently, we discovered a network of protein vibrations in enzyme cyclophilin A, coupled to its catalytic activity of peptidyl-prolyl cis-trans isomerization. Detailed studies suggest that this network, extending from surface regions to active site, is a conserved part of enzyme structure and has a role in promoting catalysis. In this report, theoretical investigations of concerted conformational fluctuations occurring on microsecond and longer time scales within the discovered network are presented. Using a new technique, kinetic energy was added to protein vibrational modes corresponding to conformational fluctuations in the network. The results reveal that protein dynamics promotes catalysis by altering transition state barrier crossing behavior of reaction trajectories. An increase in transmission coefficient and number of productive trajectories with increasing amounts of kinetic energy in vibrational modes is observed. Variations in active site enzyme-substrate interactions near transition state are found to be correlated with barrier recrossings. Simulations also showed that energy transferred from first solvation shell to surface residues impacts catalysis through network fluctuations. The detailed characterization of network presented here indicates that protein dynamics plays a role in rate enhancement by enzymes. Therefore, coupled networks in enzymes have wide implications in understanding allostericity and cooperative effects, as well as protein engineering and drug design.

A method is proposed for defining a probability distribution on an ensemble of protein conformations from a 2D NOE spectrum, while at the same time back-calculating the experimental spectrum from the ensemble. This enables one to assess the relative quality and significance of the conformations, and to test the consistency of the ensemble as a whole with the experimental spectrum. The method eliminates the need to integrate the cross-peak intensities and is surprisingly insensitive to random noise in the spectrum. In this communication, these advantages are demonstrated by applying the method to simulated data, for which the correct result is already known.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. Peptidylprolyl cis-trans isomerase A (PPIA) is a molecular chaperone involved in protein folding, and its dysfunction has been linked to ALS pathogenesis, as proline is recognized as a key residue for maintaining proper folding of ALS-related proteins. A recent study identified a K76E mutation in PPIA in sporadic ALS patients, but its effect on protein function and structure remain unclear. In this study, we used biochemical and biophysical techniques to investigate the structural and functional consequences of the K76E mutation. Our results show that K76E significantly reduces enzyme activity without affecting structure, monodispersity, or substrate recognition. Significant effects of K76E mutation were identified by relaxation dispersion NMR experiments, showing that K76E disrupts key protein dynamics and alters an allosteric network essential for isomerase activity. Corroborated by theoretical kinetic analysis, these dynamics data, revealing the exchange process for K76E to be approximately 1 order of magnitude slower than that of the wild type, explain the reduced cis-trans isomerase activity of the K76E mutant. These findings suggest that the pathogenic effect of K76E arises primarily from impaired protein dynamics rather than direct structural disruption. Our study provides new insights into the molecular mechanisms underlying ALS-associated mutations and their impact on protein function.

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

Parallel simulated tempering dynamics of ligand–protein binding with ensembles of protein conformations

Conformational ensembles are increasingly recognized as a useful representation to describe fundamental relationships between protein structure, dynamics and function. Here we present an ensemble of ubiquitin in solution that is created by sampling conformational space without experimental information using “Backrub” motions inspired by alternative conformations observed in sub-Angstrom resolution crystal structures. Backrub-generated structures are then selected to produce an ensemble that optimizes agreement with nuclear magnetic resonance (NMR) Residual Dipolar Couplings (RDCs). Using this ensemble, we probe two proposed relationships between properties of protein ensembles: (i) a link between native-state dynamics and the conformational heterogeneity observed in crystal structures, and (ii) a relation between dynamics of an individual protein and the conformational variability explored by its natural family. We show that the Backrub motional mechanism can simultaneously explore protein native-state dynamics measured by RDCs, encompass the conformational variability present in ubiquitin complex structures and facilitate sampling of conformational and sequence variability matching those occurring in the ubiquitin protein family. Our results thus support an overall relation between protein dynamics and conformational changes enabling sequence changes in evolution. More practically, the presented method can be applied to improve protein design predictions by accounting for intrinsic native-state dynamics.

Analysing protein conformational ensembles whether from molecular dynamics (MD) simulation or other sources for functionally relevant conformational changes can be very challenging. In the nineteen nineties dimensional reduction methods were developed primarily for analysing MD trajectories to determine dominant motions with the aim of understanding their relationship to function. Coarse-graining methods were also developed so the conformational change between two structures could be described in terms of the relative motion of a small number of quasi-rigid regions rather than in terms of a large number of atoms. When these methods are combined, they can characterize the large-scale motions inherent in a conformational ensemble providing insight into possible functional mechanism. The dimensional reduction methods first applied to protein conformational ensembles were referred to as Quasi-Harmonic Analysis, Principal Component Analysis and Essential Dynamics Analysis. A retrospective on the origin of these methods is presented, the relationships between them explained, and more recent developments reviewed.

A versatile method to directly identify and analyze short- or long-range coupled or communicating residues in a protein conformational ensemble is of extreme relevance to achieve a complete understanding of protein dynamics and structural communication routes. Here, we present xPyder, an interface between one of the most employed molecular graphics systems, PyMOL, and the analysis of dynamical cross-correlation matrices (DCCM). The approach can also be extended, in principle, to matrices including other indexes of communication propensity or intensity between protein residues, as well as the persistence of intra- or intermolecular interactions, such as those underlying protein dynamics. The xPyder plugin for PyMOL 1.4 and 1.5 is offered as Open Source software via the GPL v2 license, and it can be found, along with the installation package, the user guide, and examples, at http://linux.btbs.unimib.it/xpyder/.

Apart from playing key roles in drug metabolism and adverse drug-drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.