Computational modeling and prediction of deletion mutants

FBXO22 deficiency defines a pleiotropic syndrome of growth restriction and multi-system anomalies associated with a unique epigenetic signature

FBXO22 encodes an F-box protein, which acts as a substrate-recognition component of the SKP1-CUL1-F-box (SCF) E3 ubiquitin ligase complex. Despite its known roles in the post-translational ubiquitination and degradation of specific substrates, including histone demethylases, the impact of FBXO22 on human development remains unknown. Here, we characterize a pleiotropic syndrome with prominent prenatal onset growth restriction and notable neurodevelopmental delay across 16 cases from 14 families. Through exome and genome sequencing, we identify four distinct homozygous FBXO22 variants with loss-of-function effects segregating with the disease: three predicted to lead to premature translation termination due to frameshift effects and a single-amino-acid-deletion variant, which, we show, impacts protein stability in vitro. We confirm that affected primary fibroblasts with a frameshift mutation are bereft of endogenous FBXO22 and show increased levels of the known substrate histone H3K9 demethylase KDM4B. Accordingly, we delineate a unique epigenetic signature for this disease in peripheral blood via long-read sequencing. Altogether, we identify and demonstrate that FBXO22 deficiency leads to a pleiotropic syndrome in humans, encompassing growth restriction and neurodevelopmental delay, the pathogenesis of which may be explained by broad chromatin alterations.

Integration of protein stability and AlphaMissense scores improves bioinformatic impact prediction for p53 missense and in-frame amino acid deletion variants

The clinical classification of germline missense variants and single-amino-acid deletions is challenging. The BayesDel and Align-GVGD bioinformatic prediction tools currently used for ClinGen TP53 variant curation expert panel (VCEP) classification do not directly capture changes in protein folding stability, measured using computed destabilization energies (ΔΔG scores). The AlphaMissense tool recently developed by Google DeepMind to predict pathogenicity for all human proteome missense variants is trained in part using AlphaFold2 architecture. Our study investigated whether protein folding stability and/or AlphaMissense scores could improve impact prediction for p53 missense and single-amino-acid deletion variants. ΔΔG scores were calculated for missense variants using FoldX and for single-amino-acid deletions using an AlphaFold2/RosettaRelax protocol. Residue surface exposure was categorized using relative solvent accessibility (RSA) measures. The predictive values of ΔΔG scores, AlphaMissense, BayesDel, and Align-GVGD were examined using Boruta and binary logistic regression based on functionally defined reference sets. The likelihood ratio (LR) toward pathogenicity was estimated and used to refine optimal categories for predicting variant pathogenicity for different RSA values. We showed that current VCEP predictive approaches for missense variants were improved by integrating ΔΔG scores ≥2.5 kcal/mol for partially buried and buried residues, but better performance was achieved using AlphaMissense with ΔΔG and RSA. For deletion variants, ΔΔG scores ≥4.8 Rosetta energy unit (REU) in buried residues outperformed currently used predictive approaches. Future TP53 VCEP specifications for p53 missense impact prediction may consider AlphaMissense, ΔΔG score, and RSA combined for substitution variants and ΔΔG score alone for deletion variants.

Modulation of the tumor immune microenvironment by Interferon Regulatory Factor 8 enhances immunotherapy in lung adenocarcinoma

Interferon regulatory factors (IRFs) are integral in governing the expression of Type I interferon (IFN) genes. However, the precise role of IRFs in lung adenocarcinoma remains elusive. Our objective is to elucidate the prognostic implications of IRFs and their potential influence on the immunotherapeutic response in patients with lung adenocarcinoma (LUAD). The association between IRFs expression and clinical as well as prognostic features was evaluated utilizing the TCGA database. Prognostic determinants for LUAD were pinpointed via univariate and multivariate analyses. Nomogram to evaluate prognosis predicated on IRF expression levels. Gene enrichments were conducted to elucidate the mechanisms of action. The degree of immune infiltration was using bioinformatics methods and was validated through a single-cell dataset. We compiled our unique cohort of LUAD patients who underwent anti-PD-1 therapy for subsequent immunohistochemistry and multicolor immunofluorescence staining to gauge the conclusion above. Our findings revealed that IRF8 serves as an independent risk factor for overall survival (OS) in patients with LUAD. An analysis of patients undergoing immunotherapy revealed a positive association between the expression of IRF8 and the response to the treatment. In our specific cohort treated with anti-PD-1, high IRF8 expression was observed to enhance immunotherapy response and prolong OS by modulating immune cell infiltration. Our retrospective analysis suggests that elevated IRF8 expression correlates with improved prognosis in LUAD, with higher IRF8 expression being predictive of a more robust immunotherapy response. Mechanistically, IRF8 expression is associated with a modulated tumor immune microenvironment and improved immunotherapeutic response.

Systematic characterization of indel variants using a yeast-based protein folding sensor

Gene variants resulting in insertions or deletions of amino acid residues (indels) have important consequences for evolution and are often linked to disease, yet, compared to missense variants, the effects of indels are poorly understood and predicted. We developed a sensitive protein folding sensor based on the complementation of uracil auxotrophy in yeast by circular permutated orotate phosphoribosyltransferase (CPOP). The sensor reports on the folding of disease-linked missense variants and de-novo-designed proteins. Applying the folding sensor to a saturated library of single-residue indels in human dihydrofolate reductase (DHFR) revealed that most regions that tolerate indels are confined to internal loops, the termini, and a central α helix. Several indels are temperature sensitive, and folding is rescued upon binding to methotrexate. Rosetta and AlphaFold2 predictions correlate with the observed effects, suggesting that most indels destabilize the native fold and that these computational tools are useful for the classification of indels observed in population sequencing.

Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations

Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam.

Insertion-Deletion Events Are Depleted in Protein Regions with Predicted Secondary Structure

A fundamental goal in evolutionary biology and population genetics is to understand how selection shapes the fate of new mutations. Here, we test the null hypothesis that insertion-deletion (indel) events in protein-coding regions occur randomly with respect to secondary structures. We identified indels across 11,444 sequence alignments in mouse, rat, human, chimp, and dog genomes and then quantified their overlap with four different types of secondary structure-alpha helices, beta strands, protein bends, and protein turns-predicted by deep-learning methods of AlphaFold2. Indels overlapped secondary structures 54% as much as expected and were especially underrepresented over beta strands, which tend to form internal, stable regions of proteins. In contrast, indels were enriched by 155% over regions without any predicted secondary structures. These skews were stronger in the rodent lineages compared to the primate lineages, consistent with population genetic theory predicting that natural selection will be more efficient in species with larger effective population sizes. Nonsynonymous substitutions were also less common in regions of protein secondary structure, although not as strongly reduced as in indels. In a complementary analysis of thousands of human genomes, we showed that indels overlapping secondary structure segregated at significantly lower frequency than indels outside of secondary structure. Taken together, our study shows that indels are selected against if they overlap secondary structure, presumably because they disrupt the tertiary structure and function of a protein.

Cancer-Related Mutations in the Sam Domains of EphA2 Receptor and Ship2 Lipid Phosphatase: A Computational Study

The lipid phosphatase Ship2 interacts with the EphA2 receptor by forming a heterotypic Sam (sterile alpha motif)-Sam complex. Ship2 works as a negative regulator of receptor endocytosis and consequent degradation, and anti-oncogenic effects in cancer cells should be induced by hindering its association with EphA2. Herein, a computational approach is presented to investigate the relationship between Ship2-Sam/EphA2-Sam interaction and cancer onset and further progression. A search was first conducted through the COSMIC (Catalogue of Somatic Mutations in Cancer) database to identify cancer-related missense mutations positioned inside or close to the EphA2-Sam and Ship2-Sam reciprocal binding interfaces. Next, potential differences in the chemical-physical properties of mutant and wild-type Sam domains were evaluated by bioinformatics tools based on analyses of primary sequences. Three-dimensional (3D) structural models of mutated EphA2-Sam and Ship2-Sam domains were built as well and deeply analysed with diverse computational instruments, including molecular dynamics, to classify potentially stabilizing and destabilizing mutations. In the end, the influence of mutations on the EphA2-Sam/Ship2-Sam interaction was studied through docking techniques. This in silico approach contributes to understanding, at the molecular level, the mutation/cancer relationship by predicting if amino acid substitutions could modulate EphA2 receptor endocytosis.

Dark nanodiscs for evaluating membrane protein thermostability by differential scanning fluorimetry

Measuring protein thermostability provides valuable information on the biophysical rules that govern the 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 nonfluorescent membrane scaffold protein 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 loaded with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dimyristoyl-sn-glycero-3-phosphorylglycerol (DMPG) lipids show a complex biphasic thermal unfolding pattern 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°C and 77.5°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 DMPC and DMPG nanodisc. To extend the utility of this method, we evaluate and compare the thermostability of DsbB in different lipid environments. 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.

Mind the gap

Amino acid deletions are high-risk, high-reward mutations, yet structural consequences are poorly understood. In this issue of Structure, Woods et al. (2023) individually deleted 65 residues from a small α-helical protein, structurally assayed the 17 soluble variants, and developed a computational model of deletion solubility combining Rosetta and AlphaFold2.

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 °C and 77.5 °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.