Personalized biochemistry and biophysics

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. Of these apparent class II variants, 67 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 relative 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 that many of the least sensitive variants also directly compromise binding. Finally, we evaluate the functional properties of three previous uncharacterized, retinal-sensitive variants (ΔN73, S131P, and R135G) and show that two of these 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.

Towards generalizable predictions for G protein-coupled receptor variant expression

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins 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 membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate mutagenic trends within rhodopsin and can differentiate pathogenic transmembrane domain variants that cause it to misfold from those that compromise its signaling. This network also generates statistically significant predictions for the relative PME of transmembrane domain variants for another class A G protein-coupled receptor (β₂ 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.

Compendium of causative genes and their encoded proteins for common monogenic disorders

A compendium is presented of inherited monogenic disorders that have a prevalence of >1:20,000 in the human population, along with their causative genes and encoded proteins. "Simple" monogenic diseases are those for which the clinical features are caused by mutations impacting a single gene, usually in a manner that alters the sequence of the encoded protein. Of course, for a given "monogenic disorder", there is sometimes more than one potential disease gene, mutations in any one of which is sufficient to cause phenotypes of that disorder. Disease-causing mutations for monogenic disorders are usually passed on from generation to generation in a Mendelian fashion, and originate from spontaneous (de novo) germline founder mutations. In the past monogenic disorders have often been written off as targets for drug discovery because they sometimes are assumed to be rare disorders, for which the meager projected financial payoff of drug discovery and development has discouraged investment. However, not all monogenic diseases are rare. Here, we report that that currently available data identifies 72 disorders with a prevalence of at least 1 in 20,000 humans. For each, we tabulate the gene(s) for which mutations cause the spectrum of phenotypes associated with that disorder. We also identify the gene and protein that most commonly causes each disease. 34 of these disorders are caused exclusively by mutations in only a single gene and encoded protein.

An empirical pipeline for personalized diagnosis of Lafora disease mutations

Lafora disease (LD) is a fatal childhood dementia characterized by progressive myoclonic epilepsy manifesting in the teenage years, rapid neurological decline, and death typically within ten years of onset. Mutations in either EPM2A, encoding the glycogen phosphatase laforin, or EPM2B, encoding the E3 ligase malin, cause LD. Whole exome sequencing has revealed many EPM2A variants associated with late-onset or slower disease progression. We established an empirical pipeline for characterizing the functional consequences of laforin missense mutations in vitro using complementary biochemical approaches. Analysis of 26 mutations revealed distinct functional classes associated with different outcomes that were supported by clinical cases. For example, F321C and G279C mutations have attenuated functional defects and are associated with slow progression. This pipeline enabled rapid characterization and classification of newly identified EPM2A mutations, providing clinicians and researchers genetic information to guide treatment of LD patients.

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Lafora disease (LD) patients present with varying clinical progression

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LD missense mutations differentially affect laforin function

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An empirical in vitro pipeline is used to classify laforin missense mutations

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Patient progression can be predicted based on mutation class

Systematic Profiling of Temperature- and Retinal-Sensitive Rhodopsin Variants by Deep Mutational Scanning

Membrane protein variants with diminished conformational stability often exhibit enhanced cellular expression at reduced growth temperatures. The expression of “temperature-sensitive” variants is also typically sensitive to corrector molecules that bind and stabilize the native conformation. There are many examples of temperature-sensitive rhodopsin variants, the misfolding of which is associated with the molecular basis of retinitis pigmentosa. In this work, we employ deep mutational scanning to compare the effects of reduced growth temperature and 9-cis-retinal, an investigational corrector, on the plasma membrane expression of 700 rhodopsin variants in HEK293T cells. We find that the change in expression at reduced growth temperatures correlates with the response to 9-cis-retinal among variants bearing mutations within a hydrophobic transmembrane domain (TM2). The most sensitive variants appear to disrupt a native helical kink within this transmembrane domain. By comparison, mutants that alter the structure of a polar transmembrane domain (TM7) exhibit weaker responses to temperature and retinal that are poorly correlated. Statistical analyses suggest that this observed insensitivity cannot be attributed to a single variable, but likely arises from the composite effects of mutations on the energetics of membrane integration, the stability of the native conformation, and the integrity of the retinal-binding pocket. Finally, we show that the characteristics of purified temperature- and retinal-sensitive variants suggest that the proteostatic effects of retinal may be manifested during translation and cotranslational folding. Together, our findings highlight several biophysical constraints that appear to influence the sensitivity of genetic variants to temperature and small-molecule correctors.

Fundamentals to function: Quantitative and scalable approaches for measuring protein stability

Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.

Enzyme dysfunction at atomic resolution: Disease-associated variants of human phosphoglucomutase-1

Once experimentally prohibitive, structural studies of individual missense variants in proteins are increasingly feasible, and can provide a new level of insight into human genetic disease. One example of this is the recently identified inborn error of metabolism known as phosphoglucomutase-1 (PGM1) deficiency. Just as different variants of a protein can produce different patient phenotypes, they may also produce distinct biochemical phenotypes, affecting properties such as catalytic activity, protein stability, or 3D structure/dynamics. Experimental studies of missense variants, and particularly structural characterization, can reveal details of the underlying biochemical pathomechanisms of missense variants. Here, we review four examples of enzyme dysfunction observed in disease-related variants of PGM1. These studies are based on 11 crystal structures of wild-type (WT) and mutant enzymes, and multiple biochemical assays. Lessons learned include the value of comparing mutant and WT structures, synergy between structural and biochemical studies, and the rich understanding of molecular pathomechanism provided by experimental characterization relative to the use of predictive algorithms. We further note functional insights into the WT enzyme that can be gained from the study of pathogenic variants.

Structures Illuminate Cardiac Ion Channel Functions in Health and in Long QT Syndrome

The cardiac action potential is critical to the production of a synchronized heartbeat. This electrical impulse is governed by the intricate activity of cardiac ion channels, among them the cardiac voltage-gated potassium (Kv) channels KCNQ1 and hERG as well as the voltage-gated sodium (Nav) channel encoded by SCN5A. Each channel performs a highly distinct function, despite sharing a common topology and structural components. These three channels are also the primary proteins mutated in congenital long QT syndrome (LQTS), a genetic condition that predisposes to cardiac arrhythmia and sudden cardiac death due to impaired repolarization of the action potential and has a particular proclivity for reentrant ventricular arrhythmias. Recent cryo-electron microscopy structures of human KCNQ1 and hERG, along with the rat homolog of SCN5A and other mammalian sodium channels, provide atomic-level insight into the structure and function of these proteins that advance our understanding of their distinct functions in the cardiac action potential, as well as the molecular basis of LQTS. In this review, the gating, regulation, LQTS mechanisms, and pharmacological properties of KCNQ1, hERG, and SCN5A are discussed in light of these recent structural findings.

Probing biophysical sequence constraints within the transmembrane domains of rhodopsin by deep mutational scanning

Membrane proteins must balance the sequence constraints associated with folding and function against the hydrophobicity required for solvation within the bilayer. We recently found the expression and maturation of rhodopsin are limited by the hydrophobicity of its seventh transmembrane domain (TM7), which contains polar residues that are essential for function. On the basis of these observations, we hypothesized that rhodopsin's expression should be less tolerant of mutations in TM7 relative to those within hydrophobic TM domains. To test this hypothesis, we used deep mutational scanning to compare the effects of 808 missense mutations on the plasma membrane expression of rhodopsin in HEK293T cells. Our results confirm that a higher proportion of mutations within TM7 (37%) decrease rhodopsin's plasma membrane expression relative to those within a hydrophobic TM domain (TM2, 25%). These results in conjunction with an evolutionary analysis suggest solvation energetics likely restricts the evolutionary sequence space of polar TM domains.

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (K_V7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, I_KS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor–pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.

Genetic intolerance analysis as a tool for protein science

Recent advances in whole genome and exome sequencing have dramatically increased the database of human gene variations. There are now enough sequenced human exomes and genomes to begin to identify gene variations that are notable because they are NOT observed in sequenced human genomes, apparently because they are subject to "purifying selection", exemplifying genetic intolerance. Such "dysprocreative" gene variations are embryonic lethal or prevent reproduction through any one of a number of possible mechanisms. Here we review an emerging quantitative approach, "Missense Tolerance Ratio" (MTR) analysis, that is used to assess protein-encoding gene (cDNA) sequence intolerance to missense mutations based on analysis of the >100 K and growing number of currently available human genome and exome sequences. This approach is already useful for analyzing intolerance to mutations in cDNA segments with a resolution on the order of 90 bases. Moreover, as the number of sequenced genomes/exomes increases by orders of magnitude it may eventually be possible to assess mutational tolerance in a statistically robust manner at or near single site resolution. Here we focus on how cDNA intolerance analysis complements other bioinformatic methods to illuminate structure-folding-function relationships for the encoded proteins. A set of disease-linked membrane proteins is employed to provide examples.

Folding and Misfolding of Human Membrane Proteins in Health and Disease: From Single Molecules to Cellular Proteostasis

Advances over the past 25 years have revealed much about how the structural properties of membranes and associated proteins are linked to the thermodynamics and kinetics of membrane protein (MP) folding. At the same time biochemical progress has outlined how cellular proteostasis networks mediate MP folding and manage misfolding in the cell. When combined with results from genomic sequencing, these studies have established paradigms for how MP folding and misfolding are linked to the molecular etiologies of a variety of diseases. This emerging framework has paved the way for the development of a new class of small molecule "pharmacological chaperones" that bind to and stabilize misfolded MP variants, some of which are now in clinical use. In this review, we comprehensively outline current perspectives on the folding and misfolding of integral MPs as well as the mechanisms of cellular MP quality control. Based on these perspectives, we highlight new opportunities for innovations that bridge our molecular understanding of the energetics of MP folding with the nuanced complexity of biological systems. Given the many linkages between MP misfolding and human disease, we also examine some of the exciting opportunities to leverage these advances to address emerging challenges in the development of therapeutics and precision medicine.

Functional and Structural Features of Disease-Related Protein Variants

Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations.

Protein structure aids predicting functional perturbation of missense variants in SCN5A and KCNQ1

Rare variants in the cardiac potassium channel K_V7.1 (KCNQ1) and sodium channel Na_V1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of Na_V1.5 (n = 356) and K_V7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported Na_V1.5 and K_V7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting Na_V1.5 peak current (adj. R² = 0.27) and K_V7.1 + KCNE1 half-maximal voltage of activation (adj. R² = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers.

Asp263 missense variants perturb the active site of human phosphoglucomutase 1

The enzyme phosphoglucomutase 1 (PGM1) plays a central role in glucose homeostasis. Clinical studies have identified mutations in human PGM1 as the cause of PGM1 deficiency, an inherited metabolic disease. One residue, Asp263, has two known variants associated with disease: D263G and D263Y. Biochemical studies have shown that these mutants are soluble and well folded, but have significant catalytic impairment. To better understand this catalytic defect, we determined crystal structures of these two missense variants, both of which reveal a similar and indirect structural change due to the loss of a conserved salt bridge between Asp263 and Arg293. The arginine reorients into the active site, making interactions with residues responsible for substrate binding. Biochemical studies also show that the catalytic phosphoserine of the missense variants is more stable to hydrolysis relative to wild-type enzyme. The structural perturbation resulting from mutation of this single amino acid reveals the molecular mechanism underlying PGM1 deficiency in these missense variants.

DATABASE

Structural data are available in the PDB under the accession numbers 5JN5 and 5TR2.

Impact of disease-Linked mutations targeting the oligomerization interfaces of aldehyde dehydrogenase 7A1

Aldehyde dehydrogenase 7A1 (ALDH7A1) is involved in lysine catabolism, catalyzing the oxidation of α-aminoadipate semialdehyde to α-aminoadipate. Certain mutations in the ALDH7A1 gene, which are presumed to reduce catalytic activity, cause an autosomal recessive seizure disorder known as pyridoxine-dependent epilepsy (PDE). Although the genetic association between ALDH7A1 and PDE is well established, little is known about the impact of PDE-mutations on the structure and catalytic function of the enzyme. Herein we report the first study of the molecular consequences of PDE mutations using purified ALDH7A1 variants. Eight variants, with mutations in the oligomer interfaces, were expressed in Escherichia coli: P78L, G83E, A129P, G137V, G138V, A149E, G255D, and G263E. All but P78L and G83E were soluble and could be purified. All six soluble mutants were catalytically inactive. The impact of the mutations on oligomerization was assessed by analytical ultracentrifugation. Wild-type ALDH7A1 is shown to exist in a dimer-tetramer equilibrium with a dissociation constant of 16 μM. In contrast to the wild-type enzyme, the variants reside in monomer-dimer equilibria and are apparently incapable of forming a tetrameric species, even at high enzyme concentration. The available evidence suggests that they are misfolded assemblies lacking the three-dimensional structure required for catalysis.

Synchrotron-based macromolecular crystallography module for an undergraduate biochemistry laboratory course

This paper describes the introduction of synchrotron-based macromolecular crystallography (MX) into an undergraduate laboratory class. An introductory 2 week experimental module on MX, consisting of four laboratory sessions and two classroom lectures, was incorporated into a senior-level biochemistry class focused on a survey of biochemical techniques, including the experimental characterization of proteins. Students purified recombinant protein samples, set up crystallization plates and flash-cooled crystals for shipping to a synchrotron. Students then collected X-ray diffraction data sets from their crystals via the remote interface of the Molecular Biology Consortium beamline (4.2.2) at the Advanced Light Source in Berkeley, CA, USA. Processed diffraction data sets were transferred back to the laboratory and used in conjunction with partial protein models provided to the students for refinement and model building. The laboratory component was supplemented by up to 2 h of lectures by faculty with expertise in MX. This module can be easily adapted for implementation into other similar undergraduate classes, assuming the availability of local crystallographic expertise and access to remote data collection at a synchrotron source.

Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes

Genetic testing for hereditary breast cancer is an integral part of individualized care in the new era of precision medicine. The accuracy of an assay is reliant on not only the technology and bioinformatics analysis utilized but also the experience and infrastructure required to correctly classify genetic variants as disease-causing. Interpreting the clinical significance of germline variants identified by hereditary cancer testing is complex and has a significant impact on the management of patients who are at increased cancer risk. In this review we give an overview of our clinical laboratory's integrated approach to variant assessment. We discuss some of the nuances that should be considered in the assessment of genomic variants. In addition, we highlight lines of evidence such as functional assays and structural analysis that can be useful in the assessment of rare and complex variants.

Structural basis for KCNE3 modulation of potassium recycling in epithelia

The single-span membrane protein KCNE3 modulates a variety of voltage-gated ion channels in diverse biological contexts. In epithelial cells, KCNE3 regulates the function of the KCNQ1 potassium ion (K(+)) channel to enable K(+) recycling coupled to transepithelial chloride ion (Cl(-)) secretion, a physiologically critical cellular transport process in various organs and whose malfunction causes diseases, such as cystic fibrosis (CF), cholera, and pulmonary edema. Structural, computational, biochemical, and electrophysiological studies lead to an atomically explicit integrative structural model of the KCNE3-KCNQ1 complex that explains how KCNE3 induces the constitutive activation of KCNQ1 channel activity, a crucial component in K(+) recycling. Central to this mechanism are direct interactions of KCNE3 residues at both ends of its transmembrane domain with residues on the intra- and extracellular ends of the KCNQ1 voltage-sensing domain S4 helix. These interactions appear to stabilize the activated "up" state configuration of S4, a prerequisite for full opening of the KCNQ1 channel gate. In addition, the integrative structural model was used to guide electrophysiological studies that illuminate the molecular basis for how estrogen exacerbates CF lung disease in female patients, a phenomenon known as the "CF gender gap."

Documentation of an Imperative To Improve Methods for Predicting Membrane Protein Stability

There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < −0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.

Induced Structural Disorder as a Molecular Mechanism for Enzyme Dysfunction in Phosphoglucomutase 1 Deficiency

Human phosphoglucomutase 1 (PGM1) plays a central role in cellular glucose homeostasis, mediating the switch between glycolysis and gluconeogenesis through the conversion of glucose 1-phosphate and glucose 6-phosphate. Recent clinical studies have identified mutations in this enzyme as the cause of PGM1 deficiency, an inborn error of metabolism classified as both a glycogen storage disease and a congenital disorder of glycosylation. Reported here are the first crystal structures of two disease-related missense variants of PGM1, along with the structure of the wild-type enzyme. Two independent glycine-to-arginine substitutions (G121R and G291R), both affecting key active site loops of PGM1, are found to induce regions of structural disorder, as evidenced by a nearly complete loss of electron density for as many as 23 aa. The disordered regions are not contiguous in sequence to the site of mutation, and even cross domain boundaries. Other structural rearrangements include changes in the conformations of loops and side chains, some of which occur nearly 20 Å away from the site of mutation. The induced structural disorder is correlated with increased sensitivity to proteolysis and lower-resolution diffraction, particularly for the G291R variant. Examination of the multi-domain effects of these G➔R mutations establishes a correlation between interdomain interfaces of the enzyme and missense variants of PGM1 associated with disease. These crystal structures provide the first insights into the structural basis of enzyme dysfunction in PGM1 deficiency and highlight a growing role for biophysical characterization of proteins in the field of precision medicine.

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation

Oncogenic mutations in the monomeric Casitas B-lineage lymphoma (Cbl) gene have been found in many tumors, but their significance remains largely unknown. Several human c-Cbl (CBL) structures have recently been solved, depicting the protein at different stages of its activation cycle and thus providing mechanistic insight underlying how stability-activity tradeoffs in cancer-related proteins-may influence disease onset and progression. In this study, we computationally modeled the effects of missense cancer mutations on structures representing four stages of the CBL activation cycle to identify driver mutations that affect CBL stability, binding, and activity. We found that recurrent, homozygous, and leukemia-specific mutations had greater destabilizing effects on CBL states than random noncancer mutations. We further tested the ability of these computational models, assessing the changes in CBL stability and its binding to ubiquitin-conjugating enzyme E2, by performing blind CBL-mediated EGFR ubiquitination assays in cells. Experimental CBL ubiquitin ligase activity was in agreement with the predicted changes in CBL stability and, to a lesser extent, with CBL-E2 binding affinity. Two thirds of all experimentally tested mutations affected the ubiquitin ligase activity by either destabilizing CBL or disrupting CBL-E2 binding, whereas about one-third of tested mutations were found to be neutral. Collectively, our findings demonstrate that computational methods incorporating multiple protein conformations and stability and binding affinity evaluations can successfully predict the functional consequences of cancer mutations on protein activity, and provide a proof of concept for mutations in CBL.

Folding Thermodynamics and Mechanism of Five Trp-Cage Variants from Replica-Exchange MD Simulations with RSFF2 Force Field

To test whether our recently developed residue-specific force field RSFF2 can reproduce the mutational effect on the thermal stability of Trp-cage mini-protein and decipher its detailed folding mechanism, we carried out long-time replica-exchange molecular dynamics (REMD) simulations on five Trp-cage variants, including TC5b and TC10b. Initiated from their unfolded structures, the simulations not only well-reproduce their experimental structures but also their melting temperatures and folding enthalpies reasonably well. For each Trp-cage variant, the overall folding free energy landscape is apparently two-state, but some intermediate states can be observed when projected on more detailed coordinates. We also found different variants have the same major folding pathway, including the well formed PII-helix in the unfolded state, the formation of W6-P12/P18/P19 contacts and the α-helix before the transition state, the following formation of most native contacts, and the final native loop formation. The folding mechanism derived here is consistent with many previous simulations and experiments.