Biology subjects

Stein, R. A.

Publications and source records attributed to Stein, R. A..

13 recordsLinked to original sources

bioRxiv2026

Sequence determinants of pathogenicity in glucose-6-phosphatase linked to glycogen storage disease type 1a

Glycogen storage disease type 1a (GSD1a) is an autosomal recessive Mendelian disorder that can be caused by missense variants in glucose-6-phosphatase catalytic subunit 1 (G6PC1). Although hundreds of missense variants have been identified, the vast majority are of unknown clinical significance, and the molecular mechanism(s) of bona fide pathogenic variants are ill-defined. We combine bioinformatic data and clinical associations with the protein language model AlphaMissense to guide mechanistic exploration of 78 missense variants at 55 residue positions using robust biochemical and biophysical assays to distill general principles of enzyme dysfunction. Correlation analysis established a strong linear relationship between folded G6PC1 abundance and catalytic capacity for most variants. Pathogenic variants within this paradigm were linked to compromised stability and activation of the unfolded protein response. However, outliers characterized by relatively high abundance, yet low activity clustered to a network of sidechains adjacent to the active site that allosterically modulate catalysis. Contextualized by recent high-resolution structures and AlphaFold modeling, our holistic analysis of G6PC1 in vitro metrics facilitates clinical (re)classification of variants according to explicit molecular phenotypes and identifies therapeutic design directions. Moreover, our approach illustrates a blueprint for variant characterization that integrates computational prediction with experimental validation to discover disease etiology.

biochemistry↗

bioRxiv2024

Alternating Access of a Neurotransmitter:Sodium Symporter Bacterial Homolog Determined from AlphaFold2 Ensembles and DEER Spectroscopy

Neurotransmitter:sodium symporters (NSSs) play critical roles in neural signaling by regulating neurotransmitter uptake into cells powered by sodium electrochemical gradients. Bacterial NSSs orthologs, including MhsT from Bacillus halodurans, have emerged as model systems to understands the structural motifs of alternating access in NSSs and the extent of conservation of these motifs across the family. Here, we apply a novel computational/experimental methodology to illuminate the energy landscape of MhsT alternating access. Capitalizing on our recently developed method, Sampling Protein Ensembles and Conformational Heterogeneity with AlphaFold2 (SPEACH_AF), we derived clusters of MhsT models spanning the transition from inward-facing to outward-facing conformations. Systematic application of double electron-electron resonance (DEER) spectroscopy revealed ligand-dependent movement of multiple structural motifs that underpins MhsTs conformational cycle. Remarkably, comparative DEER analysis in detergent micelles and lipid nanodiscs highlight the profound effect of the environment on the energetics of conformational changes. Through experimentally-derived selection of collective variables, we present a model of ion and substrate powered transport by MhsT consistent with the conformational cycle derived from DEER. Our findings not only advance the understanding of MhsTs function but also uncover motifs of conformational dynamics conserved within the broader context of the NSS family and within the LeuT-fold class of transporters. Importantly, our methodological blueprint introduces a novel approach that can be applied across a diverse spectrum of transporters to describe their energy landscapes. Significance StatementThe neurotransmitter:sodium symporter (NSS) family plays a crucial role in neurotransmitter reuptake, a sodium-dependent process that transports neurotransmitters from the synapse back into the neuron. This study investigates the bacterial tryptophan transporter MhsT, a homolog of human NSSs, using the deep learning method AlphaFold2 in conjunction with double electron-electron resonance spectroscopy. This combined approach enables us to map the energy landscape that dictates the conformational shifts crucial for MhsTs function. Furthermore, we reveal how the environment modulates the transporters dynamics. From our research, we develop a model of MhsT transport that highlights the extent of mechanistic conservation across the NSS family. Additionally, we introduce a comprehensive framework for exploring the energetic landscapes of transporters, effectively integrating computational and experimental methods.

biophysics↗

bioRxiv2023

Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles

There has been an explosive growth in the applications of AlphaFold2, and other structure prediction platforms, to accurately predict protein structures from a multiple sequence alignment (MSA) for downstream structural analysis. However, two outstanding questions persist in the field regarding the robustness of AlphaFold2 predictions of the consequences of point mutations and the completeness of its prediction of protein conformational ensembles. We combined our previously developed method SPEACH_AF with model relaxation and energetic analysis with Rosetta to address these questions. SPEACH_AF introduces residue substitutions across the MSA and not just within the input sequence. With respect to conformational ensembles, we combined SPEACH_AF and a new MSA subsampling method, AF_cluster, and for a benchmarked set of proteins, we found that the energetics of the conformational ensembles generated by AlphaFold2 correspond to those of experimental structures and explored by standard molecular dynamic methods. With respect to point mutations, we compared the structural and energetic consequences of having the mutation(s) in the input sequence versus in the whole MSA (SPEACH_AF). Both methods yielded models different from the wild-type sequence, with more robust changes when the mutation(s) were in the whole MSA. While our findings demonstrate the robustness of AlphaFold2 in analyzing point mutations and exploring conformational ensembles, they highlight the need for multi parameter structural and energetic analyses of these models to generate experimentally testable hypotheses.

biochemistry↗

bioRxiv2023

Cucurbituril Enhances Distance Measurements of Spin-Labeled Proteins

We report complex formation between the chloroacetamide 2,6-diazaadamantane nitroxide radical (ClA-DZD) and cucurbit[7]uril (CB-7), for which the association constant in water, Ka = 1.9 x 106 M-1, is at least one order of magnitude higher than the previously studied organic radicals. The radical is highly immobilized by CB-7, as indicated by the increase of the rotational correlation time,{tau} rot, by a factor of 36, relative to that in the buffer solution. The X-ray structure of ClA-DZD@CB-7 shows the encapsulated DZD guest inside the undistorted CB-7 host, with the pendant group protruding outside. Upon addition of CB-7 to T4 Lysozyme (T4L) doubly spin-labeled with the iodoacetamide derivative of DZD, we observe the increase in{tau} rot and electron spin coherence time, Tm, along with the narrowing of inter-spin distance distributions. Sensitivity of the DEER measurements at 83 K increases by a factor 4 - 9, compared to the common spin label such as MTSL, which is not affected by CB-7. Inter-spin distances of 3-nm could be reliably measured in water/glycerol up to temperatures near the glass transition/melting temperature of the matrix at 200 K, thus bringing us closer to the goal of supramolecular recognition-enabled long-distance DEER measurements at near physiological temperatures. The X-ray structure of DZD-T4L 65 at 1.12 [A] resolution allows for unambiguous modeling of the DZD label (0.88 occupancy), indicating undisturbed structure and conformation of the protein. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/554361v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1171bd0org.highwire.dtl.DTLVardef@231612org.highwire.dtl.DTLVardef@17e22e5org.highwire.dtl.DTLVardef@250f1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Determining how MAFA and MAFB transcription factors activity is influenced by structural differences predicted by AlphaFold2

MAFA and MAFB are related basic-leucine-zipper domain containing transcription factors which have important overlapping and distinct regulatory roles in a variety of cellular contexts, including hormone production in pancreatic islet and {beta} cells. Here we first examined how mutating conserved MAF protein-DNA contacts obtained from X-ray crystal structure analysis impacted their DNA-binding and Insulin enhancer-driven activity. While most of these interactions were essential and their disruption severely compromised activity, we identified that regions outside of the contact areas also contributed to activity. AlphaFold 2, an artificial intelligence-based structural prediction program, was next used to determine if there were also differences in the three-dimensional organization of the non-DNA binding/dimerization sequences of MAFA and MAFB. This analysis was conducted on the wildtype (WT) proteins as well as the pathogenic MAFASer64Phe and MAFBSer70Ala trans-activation domain mutants, with differences revealed between MAFAWT and MAFBWT as well as between MAFASer64Phe and MAFAWT, but not between MAFBSer70Ala and MAFBWT. Moreover, dissimilarities between these proteins were also observed in their ability to cooperatively stimulate Insulin enhancer-driven activity in the presence of other islet-enriched transcription factors. Analysis of MAFA and MAFB chimeras disclosed that these properties were greatly influenced by unique C-terminal region structural differences predicted by AlphaFold 2. Importantly, these results have revealed features of these closely related proteins that are functionally significant in islet biology.

molecular biology↗

bioRxiv2023

Approximating conformational Boltzmann distributions with AlphaFold2 predictions

Protein dynamics are intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria are evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, the computational and experimental methods capable of quantifying them are limited to systems of modest size. Here, we demonstrate that AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions, which we evaluate through examination of the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Further, we show that contact distance probability distributions generated by AlphaFold2 are sensitive to points mutations thus AF2 can predict the structural effects of mutations in some systems. We anticipate that our approach will be a valuable tool to model the thermodynamics of conformational changes in large biomolecular systems.

biophysics↗

bioRxiv2023

Asymmetric conformations and lipid interactions shape the ATP-coupled cycle of a heterodimeric ABC transporter

To illuminate the structural origin of catalytic asymmetry of heterodimeric ABC transporters and how it shapes the energetics of their conformational cycles, we used cryo-electron microscopy (cryo-EM), double electron-electron resonance spectroscopy (DEER), and molecular dynamics (MD) simulations, to capture and characterize conformational states of the heterodimeric ABC multidrug exporter BmrCD in lipid nanodiscs. In addition to multiple ATP- and substrate-bound inward-facing (IF) conformations, we obtained the structure of an occluded (OC) conformation wherein the unique extracellular domain (ECD) twists to partially open the extracellular gate. In conjunction with DEER analysis of the populations of these conformations, the structures reveal that ATP-powered isomerization entails changes in the relative symmetry of the BmrC and BmrD subunits that propagates from the transmembrane domain (TMD) to the nucleotide binding domain (NBD). The structures uncover asymmetric substrate and Mg2+ binding which we hypothesize are required for triggering ATP hydrolysis preferentially in one of the nucleotide-binding sites. MD simulations demonstrated that multiple lipid molecules, identified from the cryo-EM density maps, differentially bind the IF versus the OC conformation thus modulating their relative stability. In addition to establishing how lipid interactions with BmrCD modulate the energy landscape, our findings are framed in a distinct transport model that highlights the role of asymmetric conformations in the ATP-coupled cycle with implications to the mechanism of ABC transporters in general.

biophysics↗

bioRxiv2023

Molecular mechanisms of functional impairment for active site mutations in glucose-6-phosphatase catalytic subunit 1 linked to glycogen storage disease type 1a.

Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein G6PC1 regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate (G6P) within the lumen of the endoplasmic reticulum. Consistent with its vital contribution to glucose homeostasis, inactivating mutations in G6PC1 cause glycogen storage disease (GSD) type 1a characterized by hepatomegaly and severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. Exploiting a computational model of G6PC1 derived from the groundbreaking structure prediction algorithm AlphaFold2 (AF2), we combine molecular dynamics (MD) simulations and computational predictions of thermodynamic stability with a robust in vitro screening platform to define the atomic interactions governing G6P binding as well as explore the energetic perturbations imposed by disease-linked variants. We identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network stabilizing G6P in the active site. Introduction of GSD type 1a mutations into the G6PC1 sequence elicits changes in G6P binding energy, thermostability and structural properties, suggesting multiple pathways of catalytic impairment. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm active site structural organization but also suggest novel mechanistic contributions of catalytic and non-catalytic side chains.

biophysics↗

bioRxiv2021

Modeling Alternate Conformations with Alphafold2 via Modification of the Multiple Sequence Alignment

The unprecedented performance of Deepminds Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes is reshaping structural biology. Moreover, the availability of Alphafold2s architecture and code has stimulated a number of questions on how to harness the capabilities of this remarkable tool. A question of central importance is whether Alphafold2s architecture is amenable to predict the intrinsic conformational heterogeneity of proteins. A general approach presented here builds on a simple manipulation of the multiple sequence alignment, via in silico mutagenesis, and subsequent modeling by Alphafold2. The approach is based in the concept that the multiple sequence alignment encodes for the structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations and potentially structural alterations due to point mutations. This modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

bioinformatics↗

bioRxiv2019

Structural characterization of an intermediate reveals a unified mechanism for the CLC Cl-/H+ transport cycle

Among coupled exchangers, CLCs uniquely catalyze the exchange of oppositely charged ions (Cl- for H+). Transport-cycle models to describe and explain this unusual mechanism have been proposed based on known CLC structures. While the proposed models harmonize many experimental findings, there have remained gaps and inconsistencies in our understanding. One limitation has been that global conformational change - which occurs in all conventional transporter mechanisms - has not been observed in any high-resolution structure. Here, we describe the 2.6 [A] structure of a CLC mutant designed to mimic the fully H+-loaded transporter. This structure reveals a global conformational change to a state that has improved accessibility for the Cl- substrate from the extracellular side and new conformations for two key glutamate residues. Based on this new structure, together with DEER measurements, MD simulations, and functional studies, we propose a unified model of the CLC transport mechanism that reconciles existing data on all CLC-type proteins.

biophysics↗

bioRxiv2019

Sequence and Structural Determinants of Ligand-dependent Alternating Access of a MATE Transporter

MATE transporters are ubiquitous ion-coupled antiporters that extrude structurally- and chemically-dissimilar molecules and have been implicated in conferring multidrug resistance. Here, we integrate Double Electron Electron Resonance (DEER) in conjunction with functional assays and site-directed mutagenesis of conserved residues to illuminate principles of ligand-dependent alternating access of PfMATE, a proton-coupled MATE from the hyperthermophilic archaeon Pyrococcus furiosus. Pairs of spin labels monitoring the two sides of the transporter reconstituted into nanodiscs reveal large amplitude movement of helices that alter the orientation of a putative substrate binding cavity. We found that acidic pH favors formation of an inward-facing (IF) conformation, whereas elevated pH (>7) and the substrate rhodamine 6G stabilizes an outward-facing (OF) conformation. PfMATE isomerization between outward-facing and inward-facing conformations is driven by protonation of a previously unidentified intracellular glutamate residue that is critical for drug resistance. Our results can be framed in a mechanistic model of transport that addresses central aspects of ligand coupling and alternating access.

biophysics↗

bioRxiv2019

Protein fold prediction using simulated DEER distance distributions and decay traces

Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy (SDSL-EPR) to improve protein structure prediction and refinement outcomes. However, existing computational implementations must choose between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. Existing methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and may contain artifacts introduced during this processing step. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating distance distributions and echo decay traces between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters, allowing RosettaDEER to discriminate between poorly-folded and native-like models even when the time window of EPR data collection is truncated, rendering them unsuitable for accurate transformation into distance distributions. Finally, we demonstrate that one decay trace per nine residues is sufficient to predict the folds of Bax and the C-terminus of ExoU, two soluble proteins with surface-exposed amphipathic structural features that prevent the Rosetta energy function from correctly identifying native-like models in the absence of experimental data. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

biophysics↗

bioRxiv2018

Confidence Analysis of DEER Data and its Structural Interpretation with Ensemble-Biased Metadynamics

Given its ability to measure multicomponent distance distributions between electron-spin probes, Double Electron-Electron Resonance spectroscopy (DEER) has become a leading technique to assess the structural dynamics of biomolecules. However, methodologies to evaluate the statistical error of these distributions are not standard, often hampering a rigorous interpretation of the experimental results. Distance distributions are often determined from the experimental DEER data through a mathematical method known as Tikhonov regularization, but this approach makes rigorous error estimates difficult. Here, we build upon an alternative model-based approach in which the distance probability distribution is represented as a sum of Gaussian components and use propagation of errors to calculate an associated confidence band. Our approach considers all sources of uncertainty, including the experimental noise, the uncertainty in the fitted background signal, and the limited time-span of the data collection. The resulting confidence band reveals the most and least reliable features of the probability distribution, thereby informing the structural interpretation of DEER experiments. To facilitate this interpretation, we also generalize the molecular-simulation method known as Ensemble-Biased Metadynamics. This method, originally designed to generate maximum-entropy structural ensembles consistent with one or more probability distributions, now also accounts for the uncertainty in those target distributions, exactly as dictated by their confidence bands. After careful benchmarks, we demonstrate the proposed techniques using DEER results from spin-labeled T4 lysozyme.

biophysics↗