Biology subjects

Meiler, J.

Publications and source records attributed to Meiler, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201

Class 1 major histocompatibility complexes (MHC-I), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8 to 10 amino acids, loaded into MCH-1 within the endoplasmic reticulum and then presented on the cell surface. Foreign peptides presented in this fashion activate CD8+ T cells and their immunogenicity correlates with their affinity for the MHC-1 binding groove. Thus, predicting antigen binding affinity for MHC-I is a valuable tool for identifying potentially immunogenic antigens. While quite a few predictors for MHC-I binding exist, there are no currently available tools that can predict antigen/MHC-I binding affinity for antigens with explicitly labeled post-translational modifications or unusual/non-canonical amino acids (NCAAs). However, such modifications are increasingly recognized as critical mediators of peptide immunogenicity. In this work, we propose a machine learning application that quantifies the binding affinity of epitopes containing NCAAs to MHC-I and compares its performance with other commonly used regressors. Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. This work provides a valuable tool for the computational design and optimization of peptides incorporating NCAAs, potentially accelerating the development of novel peptide-based therapeutics with enhanced properties and efficacy.

bioinformatics↗

bioRxiv2024

SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy. SuperWater surpasses existing methods, delivering state-of-the-art performance in both crystal water coverage and prediction precision, achieving water localization within 0.3 {+/-} 0.06 {degrees}A of experimentally validated positions. We demonstrate the capabilities of SuperWater through case studies involving protein hydration, protein-ligand binding, and protein-protein binding sites. This framework can be adapted for various applications, including structural biology, binding site prediction, multi-body docking, and water-mediated drug design.

bioinformatics↗

bioRxiv2024

VUStruct: a compute pipeline for high throughput and personalized structural biology

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patients genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStructs growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High-Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStructs utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

biochemistry↗

bioRxiv2024

Proteostasis Landscapes of Selective versus Poorly Responsive CFTR Variants Reveals Structural Vulnerabilities to Correction

Cystic Fibrosis (CF) is a lethal genetic disorder caused by variants in CF transmembrane conductance regulator (CFTR). Many variants are treatable with correctors, which enhance the folding and trafficking of CFTR. However, approximately 3% of persons with CF harbor poorly responsive variants. Here, we used affinity purification mass spectrometry proteomics to profile the protein homeostasis (proteostasis) changes of CFTR variants during correction to assess modulated interactions with protein folding and maturation pathways. Responsive variant interactions converged on similar proteostasis pathways during correction. In contrast, poorly responsive variants subtly diverged, revealing a partial restoration of protein quality control surveillance and partial correction. Computational structural modeling showed that corrector VX-445 failed to confer enough NBD1 stability to poor responders. NBD1 secondary stabilizing mutations rescued poorly responsive variants, revealing structural vulnerabilities in NBD1 required for treating poor responders. Our study provides a framework for discerning the underlying protein quality control and structural defects of CFTR variants not reached with existing drugs to expand therapeutics to all susceptible CFTR variants. SIGNIFICANCE STATEMENTCystic Fibrosis (CF) is a lethal genetic disease with variants leading to misfolding of an anion channel protein. Enhancing productive channel folding using a novel class of small molecules called correctors has emerged as the current CF treatment paradigm. However, correctors fail to reach all patient variants. Using high throughput interactomics, Rosetta simulations, and biochemical trafficking assays, this study demonstrates poorly responsive CF variants experience diverse misfolding pathways caused by structural defects in the core of a nucleotide-binding domain. Stabilizing secondary mutations in this domain rescues poorly responsive variants, paving the way for mechanistic-based therapeutic development for untreatable CF variants and future protein misfolding corrector drugs. COMPETING INTERESTSThe authors declare no competing interests.

biochemistry↗

bioRxiv2024

Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

Machine learning (ML) is changing the world of computational protein design, with data- driven methods surpassing biophysical-based methods in experimental success rates. However, they are most often reported as case studies, lack integration and standardization across platforms, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid probabilities inside the Rosetta software framework that allows for the side-by-side comparison of these models. Subsequently, existing protein fitness landscapes were used to benchmark novel self- supervised machine learning methods in realistic protein design settings. We focused on the traditional problems of protein sequence design: sampling and scoring. A major finding of our study is that novel ML approaches are better at purging the sampling space from deleterious mutations. Nevertheless, scoring resulting mutations without model fine-tuning showed no clear improvement over scoring with Rosetta. This study fills an important gap in the field and allows for the first time a comprehensive head-to-head comparison of different ML and biophysical methods. We conclude that ML currently acts as a complement to, rather than a replacement for, biophysical methods in protein design.

bioinformatics↗

bioRxiv2023

Benchmarking AlphaMissense Pathogenicity Predictions Against Cystic Fibrosis Variants

Variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR) result in cystic fibrosis - a lethal autosomal recessive disorder. Missense variants that alter a single amino acid in the CFTR protein are among the most common cystic fibrosis variants, yet tools for accurately predicting molecular consequences of missense variants have been limited to date. AlphaMissense (AM) is a new technology that predicts the pathogenicity of missense variants based on dual learned protein structure and evolutionary features. Here, we evaluated the ability of AM to predict the pathogenicity of CFTR missense variants. AM predicted a high pathogenicity for CFTR residues overall, resulting in a high false positive rate and fair classification performance on CF variants from the CFTR2.org database. AM pathogenicity score correlated modestly with pathogenicity metrics from persons with CF including sweat chloride level, pancreatic insufficiency rate, and Pseudomonas aeruginosa infection rate. Correlation was also modest with CFTR trafficking and folding competency in vitro. By contrast, the AM score correlated well with CFTR channel function in vitro - demonstrating the dual structure and evolutionary training approach learns important functional information despite lacking such data during training. Different performance across metrics indicated AM may determine if polymorphisms in CFTR are recessive CF variants yet cannot differentiate mechanistic effects or the nature of pathophysiology. Finally, AM predictions offered limited utility to inform on the pharmacological response of CF variants i.e., theratype. Development of new approaches to differentiate the biochemical and pharmacological properties of CFTR variants is therefore still needed to refine the targeting of emerging precision CF therapeutics.

bioinformatics↗

bioRxiv2023

Divergent Pairwise Epistasis in the Context of Unstable Membrane Protein Variants

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally. Impact StatementMany of the synergistic interactions between mutations within a gene are modified in the context of unstable proteins in a manner that depends on how the variants promote misfolding in the cell.

biochemistry↗

bioRxiv2023

Modeling membrane geometries implicitly in Rosetta

Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MPs structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.

biophysics↗

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

Discovery of post-translationally modified self-peptides that promote hypertension

Post translational modifications can enhance immunogenicity of self-proteins. In several conditions including hypertension, systemic lupus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and covalently bond with protein lysine residues. Here we show that the murine class-I major histocompatibility complex (MHC-I) variant H-2Db uniquely presents isoLG modified peptides and developed a computational pipeline that identifies structural features for MHC-I accommodation of such peptides. We identified isoLG-adducted peptides from renal proteins including the sodium glucose transporter 2, Cadherin 16, Kelch Domain containing protein 7A and solute carrier family 23, that are recognized by CD8+ T cells in tissues of hypertensive mice, induce T cell proliferation in vitro, and prime hypertension after adoptive transfer. Finally, we find similar patterns of isoLG-adducted antigen restriction in class-I human leukocyte antigens as in murine analogues. Thus, we have used a combined computational and experimental approach to define likely antigenic peptides in hypertension.

immunology↗

bioRxiv2023

Dark nanodiscs as a model membrane for evaluating membrane protein thermostability by differential scanning fluorimetry

Measuring protein thermostability provides valuable information on the biophysical rules that govern structure-energy relationships of proteins. However, such measurements remain a challenge for membrane proteins. Here, we introduce a new experimental system to evaluate membrane protein thermostability. This system leverages a recently-developed non-fluorescent membrane scaffold protein (MSP) to reconstitute proteins into nanodiscs and is coupled with a nano-format of differential scanning fluorimetry (nanoDSF). This approach offers a label-free and direct measurement of the intrinsic tryptophan fluorescence of the membrane protein as it unfolds in solution without signal interference from the "dark" nanodisc. In this work, we demonstrate the application of this method using the disulfide bond formation protein B (DsbB) as a test membrane protein. NanoDSF measurements of DsbB reconstituted in dark nanodiscs show a complex biphasic thermal unfolding pattern in the presence of lipids with a minor unfolding transition followed by a major transition. The inflection points of the thermal denaturation curve reveal two distinct unfolding midpoint melting temperatures (Tm) of 70.5 {degrees}C and 77.5 {degrees}C, consistent with a three-state unfolding model. Further, we show that the catalytically conserved disulfide bond between residues C41 and C130 drives the intermediate state of the unfolding pathway for DsbB in a nanodisc. We introduce this method as a new tool that can be used to understand how compositionally, and biophysically complex lipid environments drive membrane protein stability.

biophysics↗

bioRxiv2023

Integrating Expert Knowledge with Deep Learning Improves QSAR Models for CADD Modeling

The fusion of traditional chemical descriptors with Graph Neural Networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement. Intriguingly, despite SphereNets modest gain, all three models-GCN, SchNet, and SphereNet-achieve comparable performance levels when leveraging this combination strategy. This observation underscores a pivotal insight: sophisticated GNN architectures may be substituted with simpler counterparts without sacrificing efficacy, provided that they are augmented with descriptors. Furthermore, our analysis reveals a set of expert-crafted descriptors robustness in scaffold-split scenarios, frequently outperforming the combined GNN-descriptor models. Given the critical importance of scaffold splitting in accurately mimicking real-world drug discovery contexts, this finding accentuates an imperative for GNN researchers to innovate models that can adeptly navigate and predict within such frameworks. Our work not only validates the potential of integrating descriptors with GNNs in advancing ligand-based virtual screening but also illuminates pathways for future enhancements in model development and application. Our implementation can be found at https://github.com/meilerlab/gnn-descriptor.

bioinformatics↗

bioRxiv2023

MutationExplorer - a webserver for mutation of proteins and 3D visualization of energetic impacts

AO_SCPLOWBSTRACTC_SCPLOWThe possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW strives for efficiency in user experience. For example, we have prepared 45,000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MO_SCPLOWUTATIONC_SCPLOWEO_SCPLOWXPLORERC_SCPLOW is available at: http://proteinformatics.org/mutation_explorer/

bioinformatics↗

bioRxiv2022

Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While experimental methods combined with integrative structural biology has been the most effective way to get high accuracy structures and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high accuracy single chain modeling. Since then, different customizations expanded the number of conformational states accessible through AF2. Here, we further extended AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and Kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in automatic fashion.

biophysics↗

bioRxiv2022

Design principles of caveolins across metazoa and beyond

Caveolins are a unique family of membrane-remodeling proteins present broadly across animals (Metazoa), and in vertebrates form flask-shaped invaginations known as caveolae. While human caveolin-1 assembles into an amphipathic disc composed of 11 spirally packed protomers, the structural basis underlying caveolin function across animals remains elusive. Here, we predicted structures for 73 caveolins spanning animal diversity, as well as a newly identified choanoflagellate caveolin from Salpingoeca rosetta, a unicellular relative to animals. This analysis revealed seven conserved structural elements and a propensity to assemble into amphipathic discs. Despite extreme sequence variability, new cryo-EM structures of caveolins from the choanoflagellate and the purple sea urchin Strongylocentrotus purpuratus exhibit striking structural similarities to human caveolin-1, validating the structural predictions. Lastly, tracing the chromosomal evolutionary history of caveolins revealed evolutionary branches where caveolins translocated and expanded, including a parahoxozoan ancestral chromosome as the origin of most caveolin diversity. These results show that caveolins possess an ancient structural framework predating Metazoa and provide a new structural paradigm to explore the molecular basis of caveolin function across diverse evolutionary lineages.

biophysics↗

bioRxiv2022

Rosetta 's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da that weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF. We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability by RosettaLigand, and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigands ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 {micro}M. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

biochemistry↗

bioRxiv2022

Elucidation of Global Trends in the Effects of VX-661 and VX-445 on the Expression of Clinical CFTR Variants

Cystic fibrosis (CF) is a chronic genetic disease caused by mutations that compromise the expression and/ or function of the cystic fibrosis transmembrane conductance regulator chloride channel (CFTR). Most people with CF harbor a common misfolded CFTR variant ({Delta}F508), which can be rescued by combination therapies containing "corrector" compounds that restore its expression. Nevertheless, there are over 400 other CF variants that differ in their sensitivity to correctors for reasons that remain unclear. In this work, we utilize deep mutational scanning to quantitatively compare the effects of two FDA-approved correctors on the plasma membrane expression of 129 known CF variants, including 45 that are currently unclassified. Across 67 variants with attenuated expression, we find that VX-661-sensitive variants generally exhibit intermediate expression and feature mutations near its binding pocket in transmembrane domains (TMDs) 1, 2, 3, and 6. VX-445 also primarily rescues variants with intermediate expression but is instead uniquely effective towards mutations near its binding pocket in TMDs 10 & 11. Structural calculations suggest corrector binding provides similar stabilization to both sensitive and insensitive variants. These findings collectively suggest the mutation-specific effects of these compounds depend on the degree of variant destabilization and/ or the timing of cotranslational folding defects. Combining these correctors synergistically rescues variants with deficient and intermediate expression alike, presumably by doubling the total binding energy and suppressing defects at different stages of translation. These results provide an unprecedented overview of the properties of rare CFTR variants and establish new tools for CF pharmacology.

biochemistry↗

bioRxiv2022

Acquired secondary HER2 mutations enhance HER2/MAPK signaling and promote resistance to HER2 kinase inhibition in HER2-mutant breast cancer

HER2 mutations drive the growth of a subset of breast cancers and are targeted with HER2 tyrosine kinase inhibitors (TKIs) such as neratinib. However, acquired resistance is common and limits the durability of clinical responses. Most HER2-mutant breast cancers progressing on neratinib-based therapy acquire secondary mutations in HER2. Apart from the HER2T798I gatekeeper mutation, whether these secondary HER2 mutations are causal to neratinib resistance is not known. We show herein that secondary acquired HER2T862A and HER2L755S mutations promote resistance to HER2 TKIs via enhanced HER2 activation and impaired neratinib binding. While cells expressing each acquired HER2 mutation alone were sensitive to neratinib, expression of acquired double mutations enhanced HER2 signaling and reduced neratinib sensitivity in 2D and 3D assays. Computational structural modeling suggested that secondary HER2 mutations stabilize the HER2 active state and reduce neratinib binding affinity. Cells expressing double HER2 mutations exhibited resistance to most HER2 TKIs but retained sensitivity to mobocertinib and poziotinib. Double-mutant cells showed enhanced MEK/ERK signaling which was blocked by combined inhibition of HER2 and MEK, providing a potential treatment strategy to overcome resistance to HER2 TKIs in HER2-mutant breast cancer.

cancer biology↗