Discovering conformational sub-states relevant to protein function

Modeling Ensembles of Enzyme Reaction Pathways with Hi-MSM Reveals the Importance of Accounting for Pathway Diversity

Conventional quantum mechanical-molecular mechanics (QM/MM) simulation approaches for modeling enzyme reactions often assume that there is one dominant reaction pathway and that this pathway can be sampled starting from an X-ray structure of the enzyme. These assumptions reduce computational cost; however, their validity has not been extensively tested. This is due in part to the lack of a rigorous formalism for integrating disparate pathway information from dynamical QM/MM calculations. Here, we present a way to model ensembles of reaction pathways efficiently using a divide-and-conquer strategy through Hierarchical Markov State Modeling (Hi-MSM). This approach allows information on multiple, distinct pathways to be incorporated into a chemical kinetic model, and it allows us to test these two assumptions. Applying Hi-MSM to the reaction carried out by dihydrofolate reductase (DHFR) we find (i) there are multiple, distinct pathways significantly contributing to the overall flux of the reaction that the conventional approach does not identify and (ii) that the conventional approach does not identify the dominant reaction pathway. Thus, both assumptions underpinning the conventional approach are violated. Since DHFR is a relatively small enzyme, and configuration space scales exponentially with protein size, accounting for multiple reaction pathways is likely to be necessary for most enzymes.

Harris S, Ramanathan A. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Temperature-dependent hydrogen deuterium exchange shows impact of analog binding on adenosine deaminase flexibility but not embedded thermal networks

The analysis of hydrogen deuterium exchange by mass spectrometry as a function of temperature and mutation has emerged as a generic and efficient tool for the spatial resolution of protein networks that are proposed to function in the thermal activation of catalysis. In this work, we extend temperature-dependent hydrogen deuterium exchange from apo-enzyme structures to protein-ligand complexes. Using adenosine deaminase as a prototype, we compared the impacts of a substrate analog (1-deaza-adenosine) and a very tight-binding inhibitor/transition state analog (pentostatin) at single and multiple temperatures. At a single temperature, we observed different hydrogen deuterium exchange-mass spectrometry properties for the two ligands, as expected from their 106-fold differences in strength of binding. By contrast, analogous patterns for temperature-dependent hydrogen deuterium exchange mass spectrometry emerge in the presence of both 1-deaza-adenosine and pentostatin, indicating similar impacts of either ligand on the enthalpic barriers for local protein unfolding. We extended temperature-dependent hydrogen deuterium exchange to a function-altering mutant of adenosine deaminase in the presence of pentostatin and revealed a protein thermal network that is highly similar to that previously reported for the apo-enzyme (Gao et al., 2020, JACS 142, 19936-19949). Finally, we discuss the differential impacts of pentostatin binding on overall protein flexibility versus site-specific thermal transfer pathways in the context of models for substrate-induced changes to a distributed protein conformational landscape that act in synergy with embedded protein thermal networks to achieve efficient catalysis.

Phenol sensing in nature is modulated via a conformational switch governed by dynamic allostery

The NtrC family of proteins senses external stimuli and accordingly stimulates stress and virulence pathways via activation of associated σ⁵⁴-dependent RNA polymerases. However, the structural determinants that mediate this activation are not well understood. Here, we establish using computational, structural, biochemical, and biophysical studies that MopR, an NtrC protein, harbors a dynamic bidirectional electrostatic network that connects the phenol pocket to two distal regions, namely the “G-hinge” and the “allosteric linker.” While the G-hinge influences the entry of phenol into the pocket, the allosteric linker passes the signal to the downstream ATPase domain. We show that phenol binding induces a rewiring of the electrostatic connections by eliciting dynamic allostery and demonstrates that perturbation of the core relay residues results in a complete loss of ATPase stimulation. Furthermore, we found a mutation of the G-hinge, ∼20 Å from the phenol pocket, promotes altered flexibility by shifting the pattern of conformational states accessed, leading to a protein with 7-fold enhanced phenol binding ability and enhanced transcriptional activation. Finally, we conducted a global analysis that illustrates that dynamic allostery-driven conserved community networks are universal and evolutionarily conserved across species. Taken together, these results provide insights into the mechanisms of dynamic allostery-mediated conformational changes in NtrC sensor proteins.

Gain and loss of TASK3 channel function and its regulation by novel variation cause KCNK9 imprinting syndrome

BACKGROUND

Genomics enables individualized diagnosis and treatment, but large challenges remain to functionally interpret rare variants. To date, only one causative variant has been described for KCNK9 imprinting syndrome (KIS). The genotypic and phenotypic spectrum of KIS has yet to be described and the precise mechanism of disease fully understood.

METHODS

This study discovers mechanisms underlying KCNK9 imprinting syndrome (KIS) by describing 15 novel KCNK9 alterations from 47 KIS-affected individuals. We use clinical genetics and computer-assisted facial phenotyping to describe the phenotypic spectrum of KIS. We then interrogate the functional effects of the variants in the encoded TASK3 channel using sequence-based analysis, 3D molecular mechanic and dynamic protein modeling, and in vitro electrophysiological and functional methodologies.

RESULTS

We describe the broader genetic and phenotypic variability for KIS in a cohort of individuals identifying an additional mutational hotspot at p.Arg131 and demonstrating the common features of this neurodevelopmental disorder to include motor and speech delay, intellectual disability, early feeding difficulties, muscular hypotonia, behavioral abnormalities, and dysmorphic features. The computational protein modeling and in vitro electrophysiological studies discover variability of the impact of KCNK9 variants on TASK3 channel function identifying variants causing gain and others causing loss of conductance. The most consistent functional impact of KCNK9 genetic variants, however, was altered channel regulation.

CONCLUSIONS

This study extends our understanding of KIS mechanisms demonstrating its complex etiology including gain and loss of channel function and consistent loss of channel regulation. These data are rapidly applicable to diagnostic strategies, as KIS is not identifiable from clinical features alone and thus should be molecularly diagnosed. Furthermore, our data suggests unique therapeutic strategies may be needed to address the specific functional consequences of KCNK9 variation on channel function and regulation.

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Why are large conformational changes well described by harmonic normal modes?

Low-frequency normal modes generated by elastic network models tend to correlate strongly with large conformational changes of proteins, despite their reliance on the harmonic approximation, which is only valid in close proximity of the native structure. We consider 12 variants of the torsional network model (TNM), an elastic network model in torsion angle space, that adopt different sets of torsion angles as degrees of freedom and reproduce with similar quality the thermal fluctuations of proteins but present drastic differences in their agreement with conformational changes. We show that these differences are related to the extent of the deviations from the harmonic approximation, assessed through an anharmonic energy function whose harmonic approximation coincides with the TNM. Our results indicate that mode anharmonicity is more strongly related to its collectivity, i.e., the number of atoms displaced by the mode, than to its amplitude; low-frequency modes can remain harmonic even at large amplitudes, provided they are sufficiently collective. Finally, we assess the potential benefits of different strategies to minimize the impact of anharmonicity. The reduction of the number of degrees of freedom or their regularization by a torsional harmonic potential significantly improves the collectivity and harmonicity of normal modes and the agreement with conformational changes. In contrast, the correction of normal mode frequencies to partially account for anharmonicity does not yield substantial benefits. The TNM program is freely available at https://github.com/ugobas/tnm.

Compost Samples from Different Temperature Zones as a Model to Study Co-occurrence of Thermophilic and Psychrophilic Bacterial Population: a Metagenomics Approach

In this study, using a metagenomic approach, we explore the bacterial diversity of compost sites categorized based on their ambient temperatures. The two sites were Reckong Peo in the lower Himalayas and Tambaram in the southern region of the country, namely, CPR and CT. Following assembly of the raw reads from shotgun metagenomics, similarity hits were generated using NCBI BLAST + and SILVA database. A total of 1463 and 1483 species were annotated from CPR and CT. A species-level annotation was performed using a python-based literature search pipeline revealing their growth characteristics. Thermophiles Thermomonospora curvata and Thermus scotoductus were among the prominent species in CT. CPR too was seen abundant with Acidothermus cellulolyticus and Moorella thermoacetica, constituting 10% of the population. Nearly 3% of the identified species in the site CPR were psychrophilic. Although found higher in CPR, psychrophilic species were identified in CT too. Flavobacterium and Psychrobacter spp. were present in both sites without any significant changes in their relative distribution contrary to the thermophilic species abundance (z = - 4.3). Akin to the sequenced samples, database-derived metagenomes also showed similar distribution of thermophiles and psychrophiles. Identifying such peculiar prevalence of extremophiles can be central to understanding extended growth temperatures.

A Single-Point Mutation in d-Arginine Dehydrogenase Unlocks a Transient Conformational State Resulting in Altered Cofactor Reactivity

Proteins are inherently dynamic, and proper enzyme function relies on conformational flexibility. In this study, we demonstrated how an active site residue changes an enzyme's reactivity by modulating fluctuations between conformational states. Replacement of tyrosine 249 (Y249) with phenylalanine in the active site of the flavin-dependent d-arginine dehydrogenase yielded an enzyme with both an active yellow FAD (Y249F-y) and an inactive chemically modified green FAD, identified as 6-OH-FAD (Y249F-g) through various spectroscopic techniques. Structural investigation of Y249F-g and Y249F-y variants by comparison to the wild-type enzyme showed no differences in the overall protein structure and fold. A closer observation of the active site of the Y249F-y enzyme revealed an alternative conformation for some active site residues and the flavin cofactor. Molecular dynamics simulations probed the alternate conformations observed in the Y249F-y enzyme structure and showed that the enzyme variant with FAD samples a metastable conformational state, not available to the wild-type enzyme. Hybrid quantum/molecular mechanical calculations identified differences in flavin electronics between the wild type and the alternate conformation of the Y249F-y enzyme. The computational studies further indicated that the alternate conformation in the Y249F-y enzyme is responsible for the higher spin density at the C6 atom of flavin, which is consistent with the formation of 6-OH-FAD in the variant enzyme. The observations in this study are consistent with an alternate conformational space that results in fine-tuning the microenvironment around a versatile cofactor playing a critical role in enzyme function.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

Structure-based mechanisms: On the way to apply alcohol dehydrogenases/reductases to organic-aqueous systems

Alcohol dehydrogenases/reductases catalyze enantioselective syntheses of versatile chiral compounds relying on direct hydride transfer from cofactor to substrates, or to an intermediate and then to substrates. Since most of the substrates catalyzed by alcohol dehydrogenases/reductases are insoluble in aqueous solutions, increasing interest has been turning to organic-aqueous systems. However, alcohol dehydrogenases/reductases are normally instable in organic solvents, leading to the unsatisfied enantioselective synthesis efficiency. The behaviors of these enzymes in organic solvents at an atomic level are unclear, thus it is of great importance to understand its structure-based mechanisms in organic-aqueous systems to improve their relative stability. Here, we summarized the accessible structures of alcohol dehydrogenases/reductases in Protein Data Bank crystallized in organic-aqueous systems, and compared the structures of alcohol dehydrogenases/reductases which have different tolerance towards organic solvents. By understanding the catalytic behaviors and mechanisms of these enzymes in organic-aqueous systems, the efficient enantioselective syntheses mediated by alcohol dehydrogenases/reductases and further challenges are also discussed through solvent engineering and enzyme-immobilization in the last decade.

Protein Conformational States-A First Principles Bayesian Method

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

Enzyme dynamics: Looking beyond a single structure

Conventional understanding of how enzymes function strongly emphasizes the role of structure. However, increasing evidence clearly indicates that enzymes do not remain fixed or operate exclusively in or close to their native structure. Different parts of the enzyme (from individual residues to full domains) undergo concerted motions on a wide range of time-scales, including that of the catalyzed reaction. Information obtained on these internal motions and conformational fluctuations has so far uncovered and explained many aspects of enzyme mechanisms, which could not have been understood from a single structure alone. Although there is wide interest in understanding enzyme dynamics and its role in catalysis, several challenges remain. In addition to technical difficulties, the vast majority of investigations are performed in dilute aqueous solutions, where conditions are significantly different than the cellular milieu where a large number of enzymes operate. In this review, we discuss recent developments, several challenges as well as opportunities related to this topic. The benefits of considering dynamics as an integral part of the enzyme function can also enable new means of biocatalysis, engineering enzymes for industrial and medicinal applications.

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Study on effects of co-solvents on the structure of DhaA by molecular dynamics simulation

With the increasing application of enzymes in various research fields, the choices of co-solvents in enzymatic preparations which directly related to the catalytic activity have been attracted attention. Thus, researching on the stabilization or destabilization behaviors of enzymes in different solvents is extremely essential. In this study, the structural changes of DhaA in two typical aprotic co-solvents (acetonitrile and tetrahydrofuran) were firstly investigated by molecular dynamics (MD) simulation. The simulation results revealed the strong van der Waals force between co-solvents and DhaA which could induce the structural change of enzyme. Interestingly, the differences of molecular size and the electrostatic force with enzyme of two co-solvents led to quite different influences on DhaA. As for acetonitrile, solvent molecules could penetrate into the catalytic site of DhaA which promoted by the electrostatic interaction. On the contrary, tetrahydrofuran molecules were mainly distributed around the catalytic site due to the relative weak electrostatic interaction and steric resistance effect. It can be concluded that different co-solvent can affect the key domains, substrate pathway and catalytic pocket of DhaA.Communicated by Ramaswamy H. Sarma.

Insights into the mechanism of inhibition of phospholipase A2 by resveratrol: An extensive molecular dynamics simulation and binding free energy calculation

Phospholipase A2 (PLA2) is one of the enzymes involved in the development of cardiovascular diseases, vascular inflammation, risk of heart attacks, and strokes. This enzyme is responsible for catalyzing the hydrolytic cleavage of ester bonds of phospholipids in the biological pathway of inflammation. To prevent the undesired hydrolysis of phospholipids, the catalytic activity of PLA2 needs to be blocked. Resveratrol is a plant-derived polyphenol inhibitor, proven to have anti-inflammatory properties. However, there is still substantial ambiguity about its inhibitory function. The present study uncovers a detailed molecular mechanism behind the resveratrol action in inhibition of PLA2, by applying and comparing two 200-ns molecular dynamics simulations. The results of structural analyses revealed that the binding of resveratrol to PLA2 reduces the content of β-sheets and increases a 5-helix to PLA2 structure, producing more folding and stability in protein. In the active site, the resveratrol is placed between the N-terminal α-helix and the newly formed 5-helix through the hydrophobic interactions with ILE19 and LEU3 residues, as well as the hydrogen bond interactions. These interactions play the role of a network at the entrance of the enzyme active site and prevent the penetration of water molecules into the PLA2 cavity. A high occupancy hydrogen bonding has been identified between SER23 of the protein and hydroxyl group of resveratrol. Furthermore, the estimation of binding free energy verified the binding affinity of resveratrol is thermodynamically sufficient to be stably bounded to PLA2. It also proved that the van der Waals interactions, particularly hydrophobic interactions, have the most significant role in PLA2-resveratrol binding and stability. Overall, our results provide useful information on the stepwise mechanism of the inhibition of PLA2 enzyme by resveratrol, as a target for improving the pharmacological applications.

Entropy-Based Strategies for Rapid Pre-Processing and Classification of Time Series Data from Single-Molecule Force Experiments

Recent advances in single-molecule science have revealed an astonishing number of details on the microscopic states of molecules, which in turn defined the need for simple, automated processing of numerous time-series data. In particular, large datasets of time series of single protein molecules have been obtained using laser optical tweezers. In this system, each molecular state has a separate time series with a relatively uneven composition from the point of view-point of local descriptive statistics. In the past, uncertain data quality and heterogeneity of molecular states were biased to the human experience. Because the data processing information is not directly transferable to the black-box-framework for an efficient classification, a rapid evaluation of a large number of time series samples simultaneously measured may constitute a serious obstacle. To solve this particular problem, we have implemented a supervised learning method that combines local entropic models with the global Lehmer average. We find that the methodological combination is suitable to perform a fast and simple categorization, which enables rapid pre-processing of the data with minimal optimization and user interventions.

Conformational perturbation, allosteric modulation of cellular signaling pathways, and disease in P23H rhodopsin

In this investigation we use THz spectroscopy and MD simulation to study the functional dynamics and conformational stability of P23H rhodopsin. The P23H mutation of rod opsin is the most common cause of human binding autosomal dominant retinitis pigmentosa (ADRP), but the precise mechanism by which this mutation leads to photoreceptor cell degeneration has not yet been elucidated. Our measurements confirm conformational instability in the global modes of the receptor and an active-state that uncouples the torsional dynamics of the retinal with protein functional modes, indicating inefficient signaling in P23H and a drastically altered mechanism of activation when contrasted with the wild-type receptor. Further, our MD simulations indicate that P23H rhodopsin is not functional as a monomer but rather, due to the instability of the mutant receptor, preferentially adopts a specific homodimerization motif. The preferred homodimer configuration induces structural changes in the receptor tertiary structure that reduces the affinity of the receptor for the retinal and significantly modifies the interactions of the Meta-II signaling state. We conjecture that the formation of the specific dimerization motif of P23H rhodopsin represents a cellular-wide signaling perturbation that is directly tied with the mechanism of P23H disease pathogenesis. Our results also support a direct role for rhodopsin P23H dimerization in photoreceptor rod death.

ANCA: Anharmonic Conformational Analysis of Biomolecular Simulations

Anharmonicity in time-dependent conformational fluctuations is noted to be a key feature of functional dynamics of biomolecules. Although anharmonic events are rare, long-timescale (μs-ms and beyond) simulations facilitate probing of such events. We have previously developed quasi-anharmonic analysis to resolve higher-order spatial correlations and characterize anharmonicity in biomolecular simulations. In this article, we have extended this toolbox to resolve higher-order temporal correlations and built a scalable Python package called anharmonic conformational analysis (ANCA). ANCA has modules to: 1) measure anharmonicity in the form of higher-order statistics and its variation as a function of time, 2) output a storyboard representation of the simulations to identify key anharmonic conformational events, and 3) identify putative anharmonic conformational substates and visualization of transitions between these substates.

Deep clustering of protein folding simulations

BACKGROUND

We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes.

RESULTS

We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features.

CONCLUSIONS

Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

A Biophysical Perspective on Enzyme Catalysis

Even after a century of investigation, our understanding of how enzymes work remains far from complete. In particular, several factors that enable enzymes to achieve high catalytic efficiencies remain only poorly understood. A number of theories have been developed, which propose or reaffirm that enzymes work as structural scaffolds, serving to bring together and properly orient the participants so that the reaction can proceed; therefore, leading to enzymes being viewed as only passive participants in the catalyzed reaction. A growing body of evidence shows that enzymes are not rigid structures but are constantly undergoing a wide range of internal motions and conformational fluctuations. In this Perspective, on the basis of studies from our group, we discuss the emerging biophysical model of enzyme catalysis that provides a detailed understanding of the interconnection among internal protein motions, conformational substates, enzyme mechanisms, and the catalytic efficiency of enzymes. For a number of enzymes, networks of conserved residues that extend from the surface of the enzyme all the way to the active site have been discovered. These networks are hypothesized to serve as pathways of energy transfer that enables thermodynamical coupling of the surrounding solvent with enzyme catalysis and play a role in promoting enzyme function. Additionally, the role of enzyme structure and electrostatic effects has been well acknowledged for quite some time. Collectively, the recent knowledge gained about enzyme mechanisms suggests that the conventional paradigm of enzyme structure encoding function is incomplete and needs to be extended to structure encodes dynamics, and together these enzyme features encode function including catalytic rate acceleration.

Dynamics based clustering of globin family members

A methodology to cluster proteins based on their dynamics’ similarity is presented. For each pair of proteins from a dataset, the structures are superimposed, and the Anisotropic Network Model modes of motions are calculated. The twelve slowest modes from each protein are matched using a local mode alignment algorithm based on the local sequence alignment algorithm of Smith–Waterman. The dynamical similarity distance matrix is calculated based on the top scoring matches of each pair and the proteins are clustered using a hierarchical clustering algorithm. The utility of this method is exemplified on a dataset of protein chains from the globin family and a dataset of tetrameric hemoglobins. The results demonstrate the effect of the quaternary structure of globin members on their intrinsic dynamics and show good ability to distinguish between different states of hemoglobin, revealing the dynamical relations between them.

Modulating Enzyme Activity by Altering Protein Dynamics with Solvent

Optimal enzyme activity depends on a number of factors, including structure and dynamics. The role of enzyme structure is well recognized; however, the linkage between protein dynamics and enzyme activity has given rise to a contentious debate. We have developed an approach that uses an aqueous mixture of organic solvent to control the functionally relevant enzyme dynamics (without changing the structure), which in turn modulates the enzyme activity. Using this approach, we predicted that the hydride transfer reaction catalyzed by the enzyme dihydrofolate reductase (DHFR) from Escherichia coli in aqueous mixtures of isopropanol (IPA) with water will decrease by ∼3 fold at 20% (v/v) IPA concentration. Stopped-flow kinetic measurements find that the pH-independent k_hydride rate decreases by 2.2 fold. X-ray crystallographic enzyme structures show no noticeable differences, while computational studies indicate that the transition state and electrostatic effects were identical for water and mixed solvent conditions; quasi-elastic neutron scattering studies show that the dynamical enzyme motions are suppressed. Our approach provides a unique avenue to modulating enzyme activity through changes in enzyme dynamics. Further it provides vital insights that show the altered motions of DHFR cause significant changes in the enzyme's ability to access its functionally relevant conformational substates, explaining the decreased k_hydride rate. This approach has important implications for obtaining fundamental insights into the role of rate-limiting dynamics in catalysis and as well as for enzyme engineering.

Conservation of Dynamics Associated with Biological Function in an Enzyme Superfamily

Enzyme superfamily members that share common chemical and/or biological functions also share common features. While the role of structure is well characterized, the link between enzyme function and dynamics is not well understood. We present a systematic characterization of intrinsic dynamics of over 20 members of the pancreatic-type RNase superfamily, which share a common structural fold. This study is motivated by the fact that the range of chemical activity as well as molecular motions of RNase homologs spans over 10⁵ folds. Dynamics was characterized using a combination of nuclear magnetic resonance experiments and computer simulations. Phylogenetic clustering led to the grouping of sequences into functionally distinct subfamilies. Detailed characterization of the diverse RNases showed conserved dynamical traits for enzymes within subfamilies. These results suggest that selective pressure for the conservation of dynamical behavior, among other factors, may be linked to the distinct chemical and biological functions in an enzyme superfamily.

Small Angle Neutron Scattering Studies of R67 Dihydrofolate Reductase, a Tetrameric Protein with Intrinsically Disordered N-Termini

R67 dihydrofolate reductase (DHFR) is a homotetramer with a single active site pore and no sequence or structural homology with chromosomal DHFRs. The R67 enzyme provides resistance to trimethoprim, an active site-directed inhibitor of Escherichia coli DHFR. Sixteen to twenty N-terminal amino acids are intrinsically disordered in the R67 dimer crystal structure. Chymotrypsin cleavage of 16 N-terminal residues results in an active enzyme with a decreased stability. The space sampled by the disordered N-termini of R67 DHFR was investigated using small angle neutron scattering. From a combined analysis using molecular dynamics and the program SASSIE ( http://www.smallangles.net/sassie/SASSIE_HOME.html ), the apoenzyme displays a radius of gyration (Rg) of 21.46 ± 0.50 Å. Addition of glycine betaine, an osmolyte, does not result in folding of the termini as the Rg increases slightly to 22.78 ± 0.87 Å. SASSIE fits of the latter SANS data indicate that the disordered N-termini sample larger regions of space and remain disordered, suggesting they might function as entropic bristles. Pressure perturbation calorimetry also indicated that the volume of R67 DHFR increases upon addition of 10% betaine and decreased at 20% betaine because of the dehydration of the protein. Studies of the hydration of full-length R67 DHFR in the presence of the osmolytes betaine and dimethyl sulfoxide find around 1250 water molecules hydrating the protein. Similar studies with truncated R67 DHFR yield around 400 water molecules hydrating the protein in the presence of betaine. The difference of ∼900 waters indicates the N-termini are well-hydrated.

Automaton model of protein: Dynamics of conformational and functional states

In this conceptual paper we propose to explore the analogy between ontic/epistemic description of quantum phenomena and interrelation between dynamics of conformational and functional states of proteins. Another new idea is to apply theory of automata to model the latter dynamics. In our model protein's behavior is modeled with the aid of two dynamical systems, ontic and epistemic, which describe evolution of conformational and functional states of proteins, respectively. The epistemic automaton is constructed from the ontic automaton on the basis of functional (observational) equivalence relation on the space of ontic states. This reminds a few approaches to emergent quantum mechanics in which a quantum (epistemic) state is treated as representing a class of prequantum (ontic) states. This approach does not match to the standard protein structure-function paradigm. However, it is perfect for modeling of behavior of intrinsically disordered proteins. Mathematically space of protein's ontic states (conformational states) is modeled with the aid of p-adic numbers or more general ultrametric spaces encoding the internal hierarchical structure of proteins. Connection with theory of p-adic dynamical systems is briefly discussed.

Applications of NMR and computational methodologies to study protein dynamics

Overwhelming evidence now illustrates the defining role of atomic-scale protein flexibility in biological events such as allostery, cell signaling, and enzyme catalysis. Over the years, spin relaxation nuclear magnetic resonance (NMR) has provided significant insights on the structural motions occurring on multiple time frames over the course of a protein life span. The present review article aims to illustrate to the broader community how this technique continues to shape many areas of protein science and engineering, in addition to being an indispensable tool for studying atomic-scale motions and functional characterization. Continuing developments in underlying NMR technology alongside software and hardware developments for complementary computational approaches now enable methodologies to routinely provide spatial directionality and structural representations traditionally harder to achieve solely using NMR spectroscopy. In addition to its well-established role in structural elucidation, we present recent examples that illustrate the combined power of selective isotope labeling, relaxation dispersion experiments, chemical shift analyses, and computational approaches for the characterization of conformational sub-states in proteins and enzymes.

Aspects of Weak Interactions between Folate and Glycine Betaine

Folate, or vitamin B9, is an important compound in one-carbon metabolism. Previous studies have found weaker binding of dihydrofolate to dihydrofolate reductase in the presence of osmolytes. In other words, osmolytes are more difficult to remove from the dihydrofolate solvation shell than water; this shifts the equilibrium toward the free ligand and protein species. This study uses vapor-pressure osmometry to explore the interaction of folate with the model osmolyte, glycine betaine. This method yields a preferential interaction potential (μ23/RT value). This value is concentration-dependent as folate dimerizes. The μ23/RT value also tracks the deprotonation of folate's N3-O4 keto-enol group, yielding a pKa of 8.1. To determine which folate atoms interact most strongly with betaine, the interaction of heterocyclic aromatic compounds (as well as other small molecules) with betaine was monitored. Using an accessible surface area approach coupled with osmometry measurements, deconvolution of the μ23/RT values into α values for atom types was achieved. This allows prediction of μ23/RT values for larger molecules such as folate. Molecular dynamics simulations of folate show a variety of structures from extended to L-shaped. These conformers possess μ23/RT values from -0.18 to 0.09 m-1, where a negative value indicates a preference for solvation by betaine and a positive value indicates a preference for water. This range of values is consistent with values observed in osmometry and solubility experiments. As the average predicted folate μ23/RT value is near zero, this indicates folate interacts almost equally well with betaine and water. Specifically, the glutamate tail prefers to interact with water, while the aromatic rings prefer betaine. In general, the more protonated species in our small molecule survey interact better with betaine as they provide a source of hydrogens (betaine is not a hydrogen bond donor). Upon deprotonation of the small molecule, the preference swings toward water interaction because of its hydrogen bond donating capacities.

Ligand Binding Enhances Millisecond Conformational Exchange in Xylanase B2 from Streptomyces lividans

Xylanases catalyze the hydrolysis of xylan, an abundant carbon and energy source with important commercial ramifications. Despite tremendous efforts devoted to the catalytic improvement of xylanases, success remains limited because of our relatively poor understanding of their molecular properties. Previous reports suggested the potential role of atomic-scale residue dynamics in modulating the catalytic activity of GH11 xylanases; however, dynamics in these studies was probed on time scales orders of magnitude faster than the catalytic time frame. Here, we used nuclear magnetic resonance titration and relaxation dispersion experiments ((15)N-CPMG) in combination with X-ray crystallography and computational simulations to probe conformational motions occurring on the catalytically relevant millisecond time frame in xylanase B2 (XlnB2) and its catalytically impaired mutant E87A from Streptomyces lividans 66. Our results show distinct dynamical properties for the apo and ligand-bound states of the enzymes. The apo form of XlnB2 experiences conformational exchange for residues in the fingers and palm regions of the catalytic cleft, while the catalytically impaired E87A variant displays millisecond dynamics only in the fingers, demonstrating the long-range effect of the mutation on flexibility. Ligand binding induces enhanced conformational exchange of residues interacting with the ligand in the fingers and thumb loop regions, emphasizing the potential role of residue motions in the fingers and thumb loop regions for recognition, positioning, processivity, and/or stabilization of ligands in XlnB2. To the best of our knowledge, this work represents the first experimental characterization of millisecond dynamics in a GH11 xylanase family member. These results offer new insights into the potential role of conformational exchange in GH11 enzymes, providing essential dynamic information to help improve protein engineering and design applications.

Conformational Sub-states and Populations in Enzyme Catalysis

Enzyme function involves substrate and cofactor binding, precise positioning of reactants in the active site, chemical turnover, and release of products. In addition to formation of crucial structural interactions between enzyme and substrate(s), coordinated motions within the enzyme-substrate complex allow reaction to proceed at a much faster rate, compared to the reaction in solution and in the absence of enzyme. An increasing number of enzyme systems show the presence of conserved protein motions that are important for function. A wide variety of motions are naturally sampled (over femtosecond to millisecond time-scales) as the enzyme complex moves along the energetic landscape, driven by temperature and dynamical events from the surrounding environment. Areas of low energy along the landscape form conformational sub-states, which show higher conformational populations than surrounding areas. A small number of these protein conformational sub-states contain functionally important structural and dynamical features, which assist the enzyme mechanism along the catalytic cycle. Identification and characterization of these higher-energy (also called excited) sub-states and the associated populations are challenging, as these sub-states are very short-lived and therefore rarely populated. Specialized techniques based on computer simulations, theoretical modeling, and nuclear magnetic resonance have been developed for quantitative characterization of these sub-states and populations. This chapter discusses these techniques and provides examples of their applications to enzyme systems.

Role of Conformational Motions in Enzyme Function: Selected Methodologies and Case Studies

It is now common knowledge that enzymes are mobile entities relying on complex atomic-scale dynamics and coordinated conformational events for proper ligand recognition and catalysis. However, the exact role of protein dynamics in enzyme function remains either poorly understood or difficult to interpret. This mini-review intends to reconcile biophysical observations and biological significance by first describing a number of common experimental and computational methodologies employed to characterize atomic-scale residue motions on various timescales in enzymes, and second by illustrating how the knowledge of these motions can be used to describe the functional behavior of enzymes and even act upon it. Two biologically relevant examples will be highlighted, namely the HIV-1 protease and DNA polymerase β enzyme systems.

Effect of the Solvent Temperatures on Dynamics of Serine Protease Proteinase K

To obtain detailed information about the effect of the solvent temperatures on protein dynamics, multiple long molecular dynamics (MD) simulations of serine protease proteinase K with the solute and solvent coupled to different temperatures (either 300 or 180 K) have been performed. Comparative analyses demonstrate that the internal flexibility and mobility of proteinase K are strongly dependent on the solvent temperatures but weakly on the protein temperatures. The constructed free energy landscapes (FELs) at the high solvent temperatures exhibit a more rugged surface, broader spanning range, and higher minimum free energy level than do those at the low solvent temperatures. Comparison between the dynamic hydrogen bond (HB) numbers reveals that the high solvent temperatures intensify the competitive HB interactions between water molecules and protein surface atoms, and this in turn exacerbates the competitive HB interactions between protein internal atoms, thus enhancing the conformational flexibility and facilitating the collective motions of the protein. A refined FEL model was proposed to explain the role of the solvent mobility in facilitating the cascade amplification of microscopic motions of atoms and atomic groups into the global collective motions of the protein.

Perturbation of the Conformational Dynamics of an Active-Site Loop Alters Enzyme Activity

The role of internal dynamics in enzyme function is highly debated. Specifically, how small changes in structure far away from the reaction site alter protein dynamics and overall enzyme mechanisms is of wide interest in protein engineering. Using RNase A as a model, we demonstrate that elimination of a single methyl group located >10 Å away from the reaction site significantly alters conformational integrity and binding properties of the enzyme. This A109G mutation does not perturb structure or thermodynamic stability, both in the apo and ligand-bound states. However, significant enhancement in conformational dynamics was observed for the bound variant, as probed over nano- to millisecond timescales, resulting in major ligand repositioning. These results illustrate the large effects caused by small changes in structure on long-range conformational dynamics and ligand specificities within proteins, further supporting the importance of preserving wild-type dynamics in enzyme systems that rely on flexibility for function.

Targeting the cyclophilin domain of Ran-binding protein 2 (Ranbp2) with novel small molecules to control the proteostasis of STAT3, hnRNPA2B1 and M-opsin

Cyclophilins are peptidyl cis-trans prolyl isomerases (PPIases), whose activity is typically inhibited by cyclosporine A (CsA), a potent immunosuppressor. Cyclophilins are also chaperones. Emerging evidence supports that cyclophilins present nonoverlapping PPIase and chaperone activities. The proteostasis of the disease-relevant substrates, signal transducer and activator of transcription 3 and 5 (STAT3/STAT5), heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), and M-opsin, is regulated by nonoverlapping chaperone and PPIase activities of the cyclophilin domain (CY) of Ranbp2, a multifunctional and modular scaffold that controls nucleocytoplasmic shuttling and proteostasis of selective substrates. Although highly homologous, CY and the archetypal cyclophilin A (CyPA) present distinct catalytic and CsA-binding activities owing to unique structural features between these cylophilins. We explored structural idiosyncrasies between CY and CyPA to screen in silico nearly 9 million small molecules (SM) against the CY PPIase pocket and identify SMs with selective bioactivity toward STAT3, hnRNPA2B1, or M-opsin proteostasis. We found three classes of SMs that enhance the cytokine-stimulated transcriptional activity of STAT3 without changing latent and activated STAT3 levels, down-regulate hnRNPA2B1 or M-opsin proteostasis, or a combination of these. Further, a SM that suppresses hnRNPA2B1 proteostasis also inhibits strongly and selectively the PPIase activity of CY. This study unravels chemical probes for multimodal regulation of CY of Ranbp2 and its substrates, and this regulation likely results in the allosterism stemming from the interconversion of conformational substates of cyclophilins. The results also demonstrate the feasibility of CY in drug discovery against disease-relevant substrates controlled by Ranbp2, and they open new opportunities for therapeutic interventions.

Discovering Multi-Scale Co-Occurrence Patterns of Asthma and Influenza with Oak Ridge Bio-Surveillance Toolkit

We describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from 2009 to 2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami, and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.

Computational perspective and evaluation of plausible catalytic mechanisms of peptidyl-prolyl cis-trans isomerases

BACKGROUND

Peptidyl prolyl cis-trans isomerization of the protein backbone is involved in the regulation of many biological processes. Cis-trans isomerization is notoriously slow and is catalyzed by a family of cis-trans peptidyl prolyl isomerases (PPIases) that have been implicated in many diseases. A general consensus on how these enzymes speed up prolyl isomerization has not been reached after decades of both experimental and computational studies.

SCOPE OF REVIEW

Computational studies carried out to understand the catalytic mechanism of the prototypical FK506 binding protein 12, Cyclophilin A and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) are reviewed. A summary and an evaluation of the implications of the proposed mechanisms from computational studies are presented.

MAJOR CONCLUSIONS

The analysis of computational studies and evaluation of the proposed mechanisms provide a general consensus and a better understanding of PPIase catalysis. The speedup of the rate of peptidyl-prolyl isomerization by PPIases can be best described by a catalytic mechanism in which the substrate in transition state configuration is stabilized. The enzymes preferentially bind the transition state configuration of the substrate relative to the cis conformation, which in most cases is bound better than the trans conformation of the substrate. Stabilization of the transition state configuration of the substrate leads to a lower free energy barrier and a faster rate of isomerization when compared to the uncatalyzed isomerization reaction.

GENERAL SIGNIFICANCE

Fully understanding the catalytic mechanism of PPIases has broad implications for drug design, elucidation of the molecular basis of many diseases, protein engineering, and enzyme catalysis in general. This article is part of a Special Issue entitled Proline-directed Foldases: Cell Signaling Catalysts and Drug Targets.

Protein conformational populations and functionally relevant substates

Functioning proteins do not remain fixed in a unique structure, but instead they sample a range of conformations facilitated by motions within the protein. Even in the native state, a protein exists as a collection of interconverting conformations driven by thermodynamic fluctuations. Motions on the fast time scale allow a protein to sample conformations in the nearby area of its conformational landscape, while motions on slower time scales give it access to conformations in distal areas of the landscape. Emerging evidence indicates that protein landscapes contain conformational substates with dynamic and structural features that support the designated function of the protein. Nuclear magnetic resonance (NMR) experiments provide information about conformational ensembles of proteins. X-ray crystallography allows researchers to identify the most populated states along the landscape, and computational simulations give atom-level information about the conformational substates of different proteins. This ability to characterize and obtain quantitative information about the conformational substates and the populations of proteins within them is allowing researchers to better understand the relationship between protein structure and dynamics and the mechanisms of protein function. In this Account, we discuss recent developments and challenges in the characterization of functionally relevant conformational populations and substates of proteins. In some enzymes, the sampling of functionally relevant conformational substates is connected to promoting the overall mechanism of catalysis. For example, the conformational landscape of the enzyme dihydrofolate reductase has multiple substates, which facilitate the binding and the release of the cofactor and substrate and catalyze the hydride transfer. For the enzyme cyclophilin A, computational simulations reveal that the long time scale conformational fluctuations enable the enzyme to access conformational substates that allow it to attain the transition state, therefore promoting the reaction mechanism. In the long term, this emerging view of proteins with conformational substates has broad implications for improving our understanding of enzymes, enzyme engineering, and better drug design. Researchers have already used photoactivation to modulate protein conformations as a strategy to develop a hypercatalytic enzyme. In addition, the alteration of the conformational substates through binding of ligands at locations other than the active site provides the basis for the design of new medicines through allosteric modulation.

Two global conformation states of a novel NAD(P) reductase like protein of the thermogenic appendix of the Sauromatum guttatum inflorescence

A novel NAD(P) reductase like protein (RL) belonging to a class of reductases involved in phenylpropanoid synthesis was previously purified to homogeneity from the Sauromatum guttatum appendix. The Sauromatum appendix raises its temperature above ambient temperature to ~30 °C on the day of inflorescence opening (D-day). Changes in the charge state distribution of the protein in electrospray ionization-mass spectrometry spectra were observed during the development of the appendix. RL adopted two conformations, state A (an extended state) that appeared before heat-production (D - 4 to D - 2), and state B (a compact state) that began appearing on D - 1 and reached a maximum on D-day. RL in healthy leaves of Arabidopsis is present in state A, whereas in thermogenic sporophylls of male cones of Encephalartos ferox is present in state B. These conformational changes strongly suggest an involvement of RL in heat-production. The biophysical properties of this protein are remarkable. It is self-assembled in aqueous solutions into micrometer sizes of organized morphologies. The assembly produces a broad range of cyclic and linear morphologies that resemble micelles, rods, lamellar micelles, as well as vesicles. The assemblies could also form network structures. RL molecules entangle with each other and formed branched, interconnected networks. These unusual assemblies suggest that RL is an oligomer, and its oligomerization can provide additional information needed for thermoregulation. We hypothesize that state A controls the plant basal temperature and state B allows a shift in the temperature set point to above ambient temperature.

Differential loss of prolyl isomerase or chaperone activity of Ran-binding protein 2 (Ranbp2) unveils distinct physiological roles of its cyclophilin domain in proteostasis

The immunophilins, cyclophilins, catalyze peptidyl cis-trans prolyl-isomerization (PPIase), a rate-limiting step in protein folding and a conformational switch in protein function. Cyclophilins are also chaperones. Noncatalytic mutations affecting the only cyclophilins with known but distinct physiological substrates, the Drosophila NinaA and its mammalian homolog, cyclophilin-B, impair opsin biogenesis and cause osteogenesis imperfecta, respectively. However, the physiological roles and substrates of most cyclophilins remain unknown. It is also unclear if PPIase and chaperone activities reflect distinct cyclophilin properties. To elucidate the physiological idiosyncrasy stemming from potential cyclophilin functions, we generated mice lacking endogenous Ran-binding protein-2 (Ranbp2) and expressing bacterial artificial chromosomes of Ranbp2 with impaired C-terminal chaperone and with (Tg-Ranbp2(WT-HA)) or without PPIase activities (Tg-Ranbp2(R2944A-HA)). The transgenic lines exhibit unique effects in proteostasis. Either line presents selective deficits in M-opsin biogenesis with its accumulation and aggregation in cone photoreceptors but without proteostatic impairment of two novel Ranbp2 cyclophilin partners, the cytokine-responsive effectors, STAT3/STAT5. Stress-induced STAT3 activation is also unaffected in Tg-Ranbp2(R2944A-HA)::Ranbp2(-/-). Conversely, proteomic analyses found that the multisystem proteinopathy/amyotrophic lateral sclerosis proteins, heterogeneous nuclear ribonucleoproteins A2/B1, are down-regulated post-transcriptionally only in Tg-Ranbp2(R2944A-HA)::Ranbp2(-/-). This is accompanied by the age- and tissue-dependent reductions of diubiquitin and ubiquitylated proteins, increased deubiquitylation activity, and accumulation of the 26 S proteasome subunits S1 and S5b. These manifestations are absent in another line, Tg-Ranbp2(CLDm-HA)::Ranbp2(-/-), harboring SUMO-1 and S1-binding mutations in the Ranbp2 cyclophilin-like domain. These results unveil distinct mechanistic and biological links between PPIase and chaperone activities of Ranbp2 cyclophilin toward proteostasis of selective substrates and with novel therapeutic potential.

Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments

The growing interest for comparing protein internal dynamics owes much to the realisation that protein function can be accompanied or assisted by structural fluctuations and conformational changes. Analogously to the case of functional structural elements, those aspects of protein flexibility and dynamics that are functionally oriented should be subject to evolutionary conservation. Accordingly, dynamics-based protein comparisons or alignments could be used to detect protein relationships that are more elusive to sequence and structural alignments. Here we provide an account of the progress that has been made in recent years towards developing and applying general methods for comparing proteins in terms of their internal dynamics and advance the understanding of the structure-function relationship.

Event detection and sub-state discovery from biomolecular simulations using higher-order statistics: application to enzyme adenylate kinase

Biomolecular simulations at millisecond and longer time-scales can provide vital insights into functional mechanisms. Because post-simulation analyses of such large trajectory datasets can be a limiting factor in obtaining biological insights, there is an emerging need to identify key dynamical events and relating these events to the biological function online, that is, as simulations are progressing. Recently, we have introduced a novel computational technique, quasi-anharmonic analysis (QAA) (Ramanathan et al., PLoS One 2011;6:e15827), for partitioning the conformational landscape into a hierarchy of functionally relevant sub-states. The unique capabilities of QAA are enabled by exploiting anharmonicity in the form of fourth-order statistics for characterizing atomic fluctuations. In this article, we extend QAA for analyzing long time-scale simulations online. In particular, we present HOST4MD--a higher-order statistical toolbox for molecular dynamics simulations, which (1) identifies key dynamical events as simulations are in progress, (2) explores potential sub-states, and (3) identifies conformational transitions that enable the protein to access those sub-states. We demonstrate HOST4MD on microsecond timescale simulations of the enzyme adenylate kinase in its apo state. HOST4MD identifies several conformational events in these simulations, revealing how the intrinsic coupling between the three subdomains (LID, CORE, and NMP) changes during the simulations. Further, it also identifies an inherent asymmetry in the opening/closing of the two binding sites. We anticipate that HOST4MD will provide a powerful and extensible framework for detecting biophysically relevant conformational coordinates from long time-scale simulations.

Towards the prediction of order parameters from molecular dynamics simulations in proteins

A molecular understanding of how protein function is related to protein structure requires an ability to understand large conformational changes between multiple states. Unfortunately these states are often separated by high free energy barriers and within a complex energy landscape. This makes it very difficult to reliably connect, for example by all-atom molecular dynamics calculations, the states, their energies, and the pathways between them. A major issue needed to improve sampling on the intermediate states is an order parameter--a reduced descriptor for the major subset of degrees of freedom--that can be used to aid sampling for the large conformational change. We present a method to combine information from molecular dynamics using non-linear time series and dimensionality reduction, in order to quantitatively determine an order parameter connecting two large-scale conformationally distinct protein states. This new method suggests an implementation for molecular dynamics calculations that may be used to enhance sampling of intermediate states.

Comparing Conformational Ensembles Using the Kullback-Leibler Divergence Expansion

We present a thermodynamical approach to identify changes in macromolecular structure and dynamics in response to perturbations such as mutations or ligand binding, using an expansion of the Kullback-Leibler Divergence that connects local population shifts in torsion angles to changes in the free energy landscape of the protein. While the Kullback-Leibler Divergence is a known formula from information theory, the novelty and power of our implementation lies in its formal developments, connection to thermodynamics, statistical filtering, ease of visualization of results, and extendability by adding higher-order terms. We present a formal derivation of the Kullback-Leibler Divergence expansion and then apply our method at a first-order approximation to molecular dynamics simulations of four protein systems where ligand binding or pH titration is known to cause an effect at a distant site. Our results qualitatively agree with experimental measurements of local changes in structure or dynamics, such as NMR chemical shift perturbations and hydrogen-deuterium exchange mass spectrometry. The approach produces easy-to-analyze results with low background, and as such has the potential to become a routine analysis when molecular dynamics simulations in two or more conditions are available. Our method is implemented in the MutInf code package and is available on the SimTK website at https://simtk.org/home/mutinf.

Quasi-anharmonic analysis reveals intermediate states in the nuclear co-activator receptor binding domain ensemble

The molten globule nuclear receptor co-activator binding domain (NCBD) of CREB binding protein (CBP) selectively recruits transcription co-activators (TCAs) during the formation of the transcription preinitiation complex. NCBD:TCA interactions have been implicated in several cancers, however, the mechanisms of NCBD:TCA recognition remain uncharacterized. NCBD:TCA intermolecular recognition has challenged traditional investigation as both NCBD and several of its corresponding TCAs are intrinsically disordered. Using 40μs of explicit solvent molecular dynamics simulations, we relate the conformational diversity of ligand-free NCBD to its bound configurations. We introduce two novel techniques to quantify the conformational heterogeneity of ligand-free NCBD, dihedral quasi-anharmonic analysis (dQAA) and hierarchical graph-based diffusive clustering. With this integrated approach we find that three of four ligand-bound states are natively accessible to the ligand-free NCBD simulations with root-mean squared deviation (RMSD) less than 2Å These conformations are accessible via diverse pathways while a rate-limiting barrier must be crossed in order to access the fourth bound state.