Biology subjects

Meiler, J.

Publications and source records attributed to Meiler, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction

A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an -helical IMP. We attached a cyclen-based paramagnetic lanthanide tag to an engineered noncanonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone HN pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The most accurate DsbB models generated in this case gave C-RMSD values over the transmembrane region of 2.11 [A] (best-RMSD) and 3.23 [A] (best-scoring).

biophysics↗

bioRxiv2022

Interpretable Chirality-Aware Graph Neural Network forQuantitative Structure Activity Relationship Modeling in Drug Discovery

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chiralityawareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atoms similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other GNNs in CADD. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our codes are publicly available at https://github.com/meilerlab/MolKGNN.

bioinformatics↗

bioRxiv2022

Template-free prediction of a new protein fold and oligomeric assembly by Alphafold2

AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel {beta}-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and interfaces between them. It also assembles between 7 and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel {beta}-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher order complexes, thus positing a new direction for the continued development of deep learning protein structure prediction approaches.

biophysics↗

bioRxiv2022

Molecular simulations reveal a mechanism for enhanced allosteric coupling between voltage-sensor and pore domains in KCNQ1 explaining its activation by ML277

The voltage-gated potassium channel KCNQ1 (KV7.1) is important for the repolarizing phase of the cardiac action potential. Activators of KCNQ1 may provide a strategy for the pharmacological treatment of congenital long QT syndrome, a genetic disorder caused by pathogenic variants in KCNQ1 that promote arrhythmia susceptibility and elevate risk for sudden cardiac death. The small-molecule agonist ML277 recovers function of mutant KCNQ1 channels in human induced pluripotent stem cell-derived cardiomyocytes and could represent a starting point for drug development. Here we investigated ML277 mode of action by developing a molecular model of the KCNQ1-ML277 interaction corroborated by experimental and computational analyses. Ligand docking and molecular dynamics simulation demonstrated that ML277 binds to the interface between the voltage sensor and pore domains in KCNQ1. Model predicted binding energies for ML277 and 62 chemical analogs of ML277 correlated with EC50 data available for these compounds. We identified novel ML277-interacting residues on the S5 and S6 segments of KCNQ1 by performing MM/PBSA energy calculations and site-directed mutagenesis of KCNQ1 coupled to electrophysiological characterization of the generated channel mutants. Network analysis of the molecular dynamics simulations further showed that ML277 increases the allosteric coupling efficiency between residues in the voltage sensor domain and residues in the pore domain. Derivatives of ML277 that are not active on KCNQ1 fail to increase allosteric coupling efficiency in the computational simulations. Our results reveal atomic details of the ML277 modulation of KCNQ1 activation. These findings may be useful for the design of allosteric modulators of KCNQ1 and other KCNQ channels that bind at the membrane-accessible protein surface. Statement of SignificanceThe potassium ion channel KCNQ1 contributes to the generation of electrical impulses in the heart. Heritable mutations in KCNQ1 can cause channel loss-of-function and predispose to a life-threatening cardiac arrhythmia. Small molecules that bind KCNQ1 and enhance channel function could establish a novel anti-arrhythmic drug paradigm. We used molecular simulations to investigate how a small agonist of KCNQ1 (ML277) binds to the KCNQ1 channel and increases its function. We identified amino acids that are responsible for ML277 binding and show how ML277 promotes signaling in KCNQ1 and channel opening. This work advances our understanding how KCNQ1 and possibly other potassium channels can be activated with small molecules. These data provide a framework for drug development studies.

biophysics↗

bioRxiv2022

Computational epitope mapping of class I fusion proteins using Bayes classification

Antibody epitope mapping of viral proteins plays a vital role in understanding immune system mechanisms of protection. In the case of class I viral fusion proteins, recent advances in cryo-electron microscopy and protein stabilization techniques have highlighted the importance of cryptic or alternative conformations that expose epitopes targeted by potent neutralizing antibodies. Thorough epitope mapping of such metastable conformations is difficult, but is critical for understanding sites of vulnerability in class I fusion proteins that occur as transient conformational states during viral attachment and fusion. We introduce a novel method Accelerated class I fusion protein Epitope Mapping (AxIEM) that accounts for fusion protein flexibility to significantly improve out-of-sample prediction of discontinuous antibody epitopes. Harnessing data from previous experimental epitope mapping efforts of several class I fusion proteins, we demonstrate that accuracy of epitope prediction depends on residue environment and allows for the precise prediction of conformation-dependent antibody target residues. We also show that AxIEM can to identify common epitopes and provide structural insights for the development and rational design of vaccines. Author SummaryEfficient determination of neutralizing epitopes of viral fusion proteins is paramount in the development of antibody-based therapeutics against rapidly evolving or undercharacterized viral pathogens. Advances in the determination of viral fusion proteins in multiple conformations with cryptic epitopes during attachment and fusion has highlighted the importance of epitope accessibility due to viral fusion protein flexibility, a physical trait not accounted for in previous B-cell epitope prediction methods. Given the relatively limited number of viral fusion proteins that have been determined in multiple conformations that also have been extensively subjected to epitope mapping techniques,, which are predominantly class I fusion proteins, we chose a limited feature set in combination with a low-complexity Bayesian classifier model to avoid overfitting. We show that this model demonstrates higher accuracy in out-of-sample performance than publicly available epitope prediction methods. Additionally, due to limited structural annotation of neutralizing epitope residues, we provide examples of how our model better discerns conformation-specific epitopes, which is critical for subunit vaccine design, and how this may provide a novel approach to assess the structural changes of antigenicity of viral fusion protein homologues.

biophysics↗

bioRxiv2022

Structural characterization of a breast cancer-associated mutation in caveolin-1

Caveolin-1 (CAV1) is a membrane sculpting protein that oligomerizes to generate flask-shaped invaginations of the plasma membrane known as caveolae. Mutations in CAV1 have been linked to multiple diseases in humans. Such mutations often interfere with oligomerization and the intracellular trafficking processes required for successful caveolae assembly, but the molecular mechanisms underlying these defects have not been structurally explained. Here, we investigate how a breast cancer-associated mutation in one of the most highly conserved residues in CAV1, P132L, affects CAV1 structure and oligomerization. We show that P132 is positioned at a major site of protomer-protomer interactions within the CAV1 complex, providing a structural explanation for why the mutant protein fails to homo-oligomerize correctly. Using a combination of computational, structural, biochemical, and cell biological approaches, we find that despite its homo-oligomerization defects P132L is capable of forming mixed hetero-oligomeric complexes with wild type CAV1 and that these complexes can be incorporated into caveolae. These findings provide insights into the fundamental mechanisms that control the formation of homo- and hetero-oligomers of caveolins that are essential for caveolae biogenesis, as well as how these processes are disrupted in human disease.

biophysics↗

bioRxiv2022

Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

The Amino Acid-Polyamine-Organocation transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular GABA. Its structure, determined exclusively in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold of a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin reuptake transporter SERT and the glucose-sodium symporter transporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follows the blueprint of these or other well-studied LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced AlphaFold2 methodology. Our experimental results reveal acid-induced conformational changes that dislodge the C-terminus from the permeation pathway coupled with rearrangement of helices that enable isomerization between both inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminate the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different functional families in the LeuT-fold. SIGNIFICANCE STATEMENTThe transporter GadC contributes to acid resistance in bacterial pathogens by exchanging two substrates, glutamate and GABA, using a mechanism termed alternating access. In this study, the conformational dynamics underlying alternating access was studied using a combination of spectroscopy and computational modeling. A conformationally diverse ensemble of models, generated using AlphaFold2, guided the design and interpretation of double electron-electron resonance spectroscopy experiments. We found that whereas GadC was inactive and conformationally homogeneous at neutral pH, low pH induced isomerization between two conformations. From our integrated computational/experimental investigation emerges a transport model that may be relevant to eukaryotic homologs that are involved in other cellular processes.

biophysics↗

bioRxiv2022

First crystal structure of a non-canonical amino acid linked to a paramagnetic lanthanide tag facilitates protein structure determination using NMR-derived restraints

Site-directed spin labeling of proteins via non-canonical amino acids (ncAAs) is a non-traditional method for the measurement of pseudocontact shifts (PCSs) by nuclear magnetic resonance (NMR) spectroscopy. PCSs provide long-range distance and orientational information between a paramagnetic center and protein nuclei that can be used as restraints for computational structural modeling techniques. Here, we present the first experimental structure of an ncAA chemically linked to a lanthanide tag conjugated to the protein, T4-Lysozyme (T4L). T4L was crystallized with a cyclen-based C3 tag coordinated to the paramagnetic ion terbium (Tb3+). The paramagnetic C3-lanthanide tag generated PCSs measured at four different ncAA sites. We show that the addition of these restraints improves structure prediction protocols for T4L using the RosettaNMR framework. Generated models provide insight into T4L conformational flexibility sampled in solution. This integrative modeling protocol is readily transferable to larger proteins. Methods to predict protein structures are advancing into an exciting arena such that reliable experimental data will play important roles for evaluating the biophysical relevance of predicted structural models. Our contribution here caters to the growing interest in using ncAAs for a range of biophysical studies, and these methods can be readily transferred to larger protein systems of interest.

biophysics↗

bioRxiv2022

Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer

Oncogenic mutations within the epidermal growth factor receptor (EGFR) are found in 15-30% of all non-small cell lung carcinomas. The term exon 19 deletion (ex19del) is collectively used to refer to more than 20 distinct genomic alterations within exon 19 that comprise the most common EGFR mutation subtype in lung cancer. Despite this heterogeneity, clinical treatment decisions are made irrespective of which EGFR ex19del variant is present within the tumor, and there is a paucity of information regarding how individual ex19del variants influence protein structure and function. Herein, we identify allele-specific functional differences among ex19del variants attributable to recurring sequence and structure motifs. We build all-atom structural models of 60 ex19del variants identified in patients and combine ~400 s of molecular dynamics simulations with biochemical and biophysical experiments to analyze three ex19del mutations (E746_A750, E746_S752>V, and L747_A750>P). We demonstrate that sequence variation in ex19del alters oncogenic cell growth, dimerization propensity, and tyrosine kinase inhibitor (TKI) sensitivity. We show that in contrast to E746_A750 and E746_S752>V, the L747_A750>P variant forms highly active ligand-independent dimers. E746_S752>V displays the least TKI sensitivity among the variants tested, which enzyme kinetic analysis and TKI inhibition experiments suggest is due to increased ATP Km relative to the common E746_A750 variant. Through these analyses, we propose an expanded framework for interpreting ex19del variants and new considerations for therapeutic intervention. SignificanceEGFR mutations are detected in approximately 30% of all lung adenocarcinomas, and the most common EGFR mutation occurring in ~50% of patients is termed "exon 19 deletion" (ex19del). Despite the existence of dozens of different genomic variants comprising what is generically referred to clinically as ex19del, clinicians currently do not distinguish between ex19del variants in considering treatment options, and the differences between ex19del variants are largely unstudied in the broader scientific community. Herein, we describe functional differences between distinct EGFR ex19del variants attributable to the structural features of each variant. These findings suggest a possible explanation for observed differences in patient outcomes stratified by ex19del subtype and reinforce the need for allele-specific considerations in clinical treatment decision making.

cancer biology↗

bioRxiv2022

Molecular Basis for Variations in the Sensitivity of Pathogenic Rhodopsin Variants to 9-cis-Retinal

Over 100 mutations in the rhodopsin gene have been linked to a spectrum of retinopathies that include retinitis pigmentosa and congenital stationary night blindness. Though most of these variants exhibit a loss of function, the molecular defects caused by these underlying mutations vary considerably. In this work, we utilize deep mutational scanning to quantitatively compare the plasma membrane expression of 123 known pathogenic rhodopsin variants in the presence and absence of the stabilizing cofactor 9-cis-retinal. We identify 69 retinopathy variants, including 20 previously uncharacterized variants, that exhibit diminished plasma membrane expression in HEK293T cells. 67 of these apparent class II variants exhibit a measurable increase in expression in the presence of 9-cis-retinal. However, the magnitude of the response to this molecule varies considerably across this spectrum of mutations. Evaluation of the observed shifts in relation to thermodynamic estimates for the coupling between binding and folding suggests underlying differences in stability constrains the magnitude of their response to retinal. Nevertheless, estimates from computational modeling suggest many of the least sensitive variants also directly compromise binding. Finally, we evaluate the functional properties of three previous uncharacterized, retinal-sensitive variants ({Delta}N73, S131P, and R135G) and show that two retain residual function in vitro. Together, our results provide a comprehensive experimental characterization of the proteostatic properties of retinopathy variants and their response to retinal.

biochemistry↗

bioRxiv2022

Molecular architecture of the human caveolin-1 complex

Membrane sculpting proteins shape the morphology of cell membranes and facilitate remodeling in response to physiological and environmental cues. Complexes of the monotopic membrane protein caveolin function as essential curvature-generating components of caveolae, flask-shaped invaginations that sense and respond to plasma membrane tension. However, the structural basis for caveolins membrane remodeling activity is currently unknown. Here, we show, using cryo-electron microscopy, that the human caveolin-1 complex is composed of 11 protomers organized into a tightly packed disc with a flat membrane-embedded surface. The structural insights suggest a new mechanism for how membrane sculpting proteins interact with membranes and reveal how key regions of caveolin-1, including its scaffolding, oligomerization, and intramembrane domains, contribute to its function. One-Sentence SummaryCryo-electron microscopy reveals that Caveolin-1 oligomerizes into a tightly packed disc with a flat membrane-binding surface.

biophysics↗

bioRxiv2022

Benchmarking Peptide Structure Prediction with AlphaFold2

AlphaFold2 (AF2) is a computational tool developed for the determination of protein structures with high accuracy. AF2 has been used for the modeling of many soluble and membrane proteins, but its performance in modeling peptide structures has not been systematically investigated so far. We benchmarked the accuracy of AF2 in predicting peptide structures between 16 - 60 amino acids using experimentally-determined peptide structures as reference. Our results show that while AF2 can predict the structures of certain peptide scaffolds with RMSD values below 3 [A], it is less successful in predicting the structures of peptides that have kinks, turns, or have extended flexible regions. Further, AF2 had several shortcomings in predicting rotamer recoveries, disulfide bonds, and the lowest RMSD structures based on pLDDT values. In summary, AF2 can be a powerful tool to determine peptide structures, but additional steps may be necessary to analyze and validate the results.

biophysics↗

bioRxiv2022

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins such as transporters or receptors. Despite their importance, the characterization of functionally-relevant structural intermediates is often a hard task for both experimental and computational methods. This limitation can be at least partially relieved by integrating experimental data within computational approaches. Such integrative structural biology strategies can yield an ensemble of models consistent with experimental data. While the macromolecular modeling suite Rosetta has been extensively used to predict protein structures de novo using various types of experimental data, an all-purpose conformational change modeling approach has been absent from this suite. Here, we introduce and benchmark ConfChangeMover (CCM), a new Rosetta Mover tailored to model conformational changes in proteins using sparse experimental data. CCM is able to rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with sparse experimental double electron-electron resonance (DEER) restraints and simulated C - C distance restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. Thus, our method will contribute to the biophysical characterization of protein dynamics.

biophysics↗

bioRxiv2021

Towards Generalizable Predictions for the Effects of Mutations on G-Protein Coupled Receptor Expression

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins (MPs) are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of MPs. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the effects of mutations on the PME of the G-protein coupled receptor (GPCR) rhodopsin from structural and/ or evolutionary features. We show that our best performing network, which we term PMEpred, can differentiate pathogenic rhodopsin variants that induce misfolding from those that primarily compromise signaling. This network also generates statistically significant predictions for the effects of mutations on the PME of another GPCR ({beta}2 adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.

biophysics↗

bioRxiv2021

Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasing applied to problems in biology, biophysical understanding will be critical with respect to explainable AI, i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org. Author summaryHeartbeat is maintained by electrical impulses generated by ion-conducting channel proteins in the heart such as the KCNQ1 potassium channel. Pathogenic variants in KCNQ1 can lead to channel loss-of-function and predisposition to fatal life-threatening irregularities of heart rhythm (arrhythmia). Machine learning methods that can predict the outcome of a mutation on KCNQ1 structure and function would be of great value in helping to assess the risk of a heart rhythm disorder. Recently, machine learning has made great progress in predicting the structures of proteins from their sequences. However, there are limited studies that link the effect of a mutation and change in protein structure with its function. This work presents the development of neural network models designed to predict mutation-induced changes in KCNQ1 functional parameters such as peak current density and voltage dependence of activation. We compare the predictive ability of features extracted from sequence, structure, and physicochemical properties of KCNQ1. Moreover, input sensitivity analysis connects biophysical features with specific functional parameters that provides insight into underlying molecular mechanisms for KCNQ1 channels. The best performing neural network model is publicly available as a webserver, called Q1VarPredBio, that delivers predictions about the functional phenotype of KCNQ1 variants.

biophysics↗

bioRxiv2021

Heterogeneity of the GFP fitness landscape and data-driven protein design

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Counterintuitively, mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

synthetic biology↗

bioRxiv2021

Sampling the conformational landscapes of transporters and receptors with AlphaFold2

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward-facing (IF) and outward-facing (OF) states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein coupled receptors (GPCRs) that are absent from the AF2 training set. Whereas models generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments (MSAs) by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling (TM)-score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

biophysics↗

bioRxiv2021

Structural comparative modeling of multi-domain ΔF508 CFTR

Cystic Fibrosis (CF) is a common genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs - which increases the risk of infection and eventually causes death. CFTR is an ATP binding cassette (ABC) transporter composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 ({Delta}F508), making {Delta}F508 CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain {Delta}F508 CFTR structure has been determined and few studies have modeled {Delta}F508 using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a {Delta}F508 model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and {Delta}F508 CFTR structural models. These models demonstrate the destabilizing effects of {Delta}F508 on NBD1 and the NBD1/TMD interface in both the closed and open conformation of CFTR. Furthermore, we modeled {Delta}F508/R1070W and {Delta}F508 bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of R1070W and VX-809 on multi-domain models of {Delta}F508 CFTR and pave the way for rational design of additional drugs that target {Delta}F508 CFTR for treatment of CF. Author SummaryProteins three-dimension shape determines their function, so when genetic mutation compromises the shape of vital proteins, it may cause disease. Such is the case in Cystic Fibrosis, a chronic genetic disease caused by mutations in the protein Cystic Fibrosis Transmembrane Conductance Regulator. Here, we work backwards from the shape of the wild-type protein - found in healthy people, to computationally model the shape of the most common Cystic Fibrosis mutant. Our computer models reveal distinct defects in the shape of the mutant Cystic Fibrosis Transmembrane Conductance Regulator protein in the area surrounding the mutation. We also model an important FDA approved Cystic Fibrosis drug, VX-809, into the mutant protein structure and show how VX-809 stabilizes the protein around the location of the mutation. The method we developed will pave the way for computational drug design for Cystic Fibrosis.

bioinformatics↗