Activating point mutations in the MET kinase domain represent a unique molecular subset of lung cancer and other malignancies targetable with MET inhibitors.

Activating point mutations in the MET tyrosine kinase domain (TKD) are oncogenic in a subset of papillary renal cell carcinomas (PRCC). Here, using comprehensive genomic profiling among >600,000 patients, we identify activating MET TKD point mutations as putative oncogenic driver across diverse cancers, with a frequency of ~0.5%. The most common mutations in the MET TKD defined as oncogenic or likely oncogenic according to OncoKB resulted in amino acid substitutions at positions H1094, L1195, F1200, D1228, Y1230, M1250, and others. Preclinical modeling of these alterations confirmed their oncogenic potential, and also demonstrated differential patterns of sensitivity to type I and type II MET inhibitors. Two patients with metastatic lung adenocarcinoma harboring MET TKD mutations (H1094Y, F1200I) and no other known oncogenic drivers achieved confirmed partial responses to a type I MET inhibitor. Activating MET TKD mutations occur in multiple malignancies and may confer clinical sensitivity to currently available MET inhibitors.

Predictors of protein losing enteropathy after Fontan completion: An 8-year retrospective study at Sheikh Khalifa Medical City.

BACKGROUND: The Fontan procedure is the final stage of a three-stage palliation process in patients born with a univentricular heart as part of hypoplastic left heart syndrome (HLHS) or other pathologies with a univentricular heart. As essential as this procedure has proven to be for such cases, the Fontan physiology diminishes cardiac output and expands systemic venous pressure, which then leads to venous congestion that can be complicated by protein-losing enteropathy (PLE). This retrospective study aimed to identify the predictors of such complications in all patients who underwent completion of the Fontan procedure at our center (Sheikh Khalifa Medical City/SKMC) in the past eight years. METHODS: This study examined the medical records of patients who underwent completion of Fontan repair at our center since the inauguration of the cardiac surgery program of SKMC in the United Arab Emirates (UAE) - 01 Jan 2012 to 31 Dec 2020. Exclusion criteria included the absence of any of the undermentioned data in patient files. Patients were divided into two groups: those who developed PLE and those who did not. For each group, the following data were collected: demographics data (current age and age at completion of Fontan), clinical and laboratory data (oxygen saturation, serum albumin), echocardiographic data (classification of original cardiac diagnosis, degree of atrio-ventricular valve regurgitation, ventricular functions), hemodynamic data (mean pressures of superior vena cava and pulmonary arteries before Fontan completion), operative data (type of initial palliation, type of Fontan, presence of fenestrations and its size) and the need for any cardiac intervention prior to Fontan completion, such as atrio-ventricular valve repair, peripheral pulmonary stenting and arch balloon dilatation. RESULTS: Of the 48 included patients,13 (25%) developed PLE. Multivariate regression analysis proved that the best predictors of PLE were superior vena cava mean pressure (P = 0.012) and the degree of atrio-ventricular valve regurgitation (P = 0.013). An oxygen saturation <83% prior to Fontan completion was 92% sensitive in predicting PLE after Fontan completion. CONCLUSION: This is a single-center study of the predictors of PLE after Fontan procedure and, as expected from similar studies, SVC pressure higher than 11 mmHg and moderate-to-severe atrio-ventricular valve regurgitation were predictors of Fontan failure. The higher prevalence of PLE in our cohort, as well as lower cut-offs of SVC pressure that can predict complications, may be related to the predominance of hypoplastic left heart in the operated patients, which has been the main referral center for cardiac surgeries in UAE in the last decade.

Allosteric inhibition of PPM1D serine/threonine phosphatase via an altered conformational state.

PPM1D encodes a serine/threonine phosphatase that regulates numerous pathways including the DNA damage response and p53. Activating mutations and amplification of PPM1D are found across numerous cancer types. GSK2830371 is a potent and selective allosteric inhibitor of PPM1D, but its mechanism of binding and inhibition of catalytic activity are unknown. Here we use computational, biochemical and functional genetic studies to elucidate the molecular basis of GSK2830371 activity. These data confirm that GSK2830371 binds an allosteric site of PPM1D with high affinity. By further incorporating data from hydrogen deuterium exchange mass spectrometry and sedimentation velocity analytical ultracentrifugation, we demonstrate that PPM1D exists in an equilibrium between two conformations that are defined by the movement of the flap domain, which is required for substrate recognition. A hinge region was identified that is critical for switching between the two conformations and was directly implicated in the high-affinity binding of GSK2830371 to PPM1D. We propose that the two conformations represent active and inactive forms of the protein reflected by the position of the flap, and that binding of GSK2830371 shifts the equilibrium to the inactive form. Finally, we found that C-terminal truncating mutations proximal to residue 400 result in destabilization of the protein via loss of a stabilizing N- and C-terminal interaction, consistent with the observation from human genetic data that nearly all PPM1D mutations in cancer are truncating and occur distal to residue 400. Taken together, our findings elucidate the mechanism by which binding of a small molecule to an allosteric site of PPM1D inhibits its activity and provides insights into the biology of PPM1D.

Characterization of intrinsically disordered regions in proteins informed by human genetic diversity.

All proteomes contain both proteins and polypeptide segments that don't form a defined three-dimensional structure yet are biologically active-called intrinsically disordered proteins and regions (IDPs and IDRs). Most of these IDPs/IDRs lack useful functional annotation limiting our understanding of their importance for organism fitness. Here we characterized IDRs using protein sequence annotations of functional sites and regions available in the UniProt knowledgebase ("UniProt features": active site, ligand-binding pocket, regions mediating protein-protein interactions, etc.). By measuring the statistical enrichment of twenty-five UniProt features in 981 IDRs of 561 human proteins, we identified eight features that are commonly located in IDRs. We then collected the genetic variant data from the general population and patient-based databases and evaluated the prevalence of population and pathogenic variations in IDPs/IDRs. We observed that some IDRs tolerate 2 to 12-times more single amino acid-substituting missense mutations than synonymous changes in the general population. However, we also found that 37% of all germline pathogenic mutations are located in disordered regions of 96 proteins. Based on the observed-to-expected frequency of mutations, we categorized 34 IDRs in 20 proteins (DDX3X, KIT, RB1, etc.) as intolerant to mutation. Finally, using statistical analysis and a machine learning approach, we demonstrate that mutation-intolerant IDRs carry a distinct signature of functional features. Our study presents a novel approach to assign functional importance to IDRs by leveraging the wealth of available genetic data, which will aid in a deeper understating of the role of IDRs in biological processes and disease mechanisms.

Regulation of purine metabolism connects KCTD13 to a metabolic disorder with autistic features.

Genetic variation of the 16p11.2 deletion locus containing the KCTD13 gene and of CUL3 is linked with autism. This genetic connection suggested that substrates of a CUL3-KCTD13 ubiquitin ligase may be involved in disease pathogenesis. Comparison of Kctd13 mutant (Kctd13 -/- ) and wild-type neuronal ubiquitylomes identified adenylosuccinate synthetase (ADSS), an enzyme that catalyzes the first step in adenosine monophosphate (AMP) synthesis, as a KCTD13 ligase substrate. In Kctd13 -/- neurons, there were increased levels of succinyl-adenosine (S-Ado), a metabolite downstream of ADSS. Notably, S-Ado levels are elevated in adenylosuccinate lyase deficiency, a metabolic disorder with autism and epilepsy phenotypes. The increased S-Ado levels in Kctd13 -/- neurons were decreased by treatment with an ADSS inhibitor. Lastly, functional analysis of human KCTD13 variants suggests that KCTD13 variation may alter ubiquitination of ADSS. These data suggest that succinyl-AMP metabolites accumulate in Kctd13 -/- neurons, and this observation may have implications for our understanding of 16p11.2 deletion syndrome.

Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.e., point mutations) from all genes on 40 3D protein features, accounting for the structural, chemical, and functional context of the variations' positions, we identify features that are generally associated with pathogenic and population missense variants. We then perform the same amino acid-level analysis individually for 24 protein functional classes, which reveals unique characteristics of the positions of the altered amino acids: We observe up to 46% divergence of the class-specific features from the general characteristics obtained by the analysis on all genes, which is consistent with the structural diversity of essential regions across different protein classes. We demonstrate that the function-specific 3D features of the variants match the readouts of mutagenesis experiments for BRCA1 and PTEN, and positively correlate with an independent set of clinically interpreted pathogenic and benign missense variants. Finally, we make our results available through a web server to foster accessibility and downstream research. Our findings represent a crucial step toward translational genetics, from highlighting the impact of mutations on protein structure to rationalizing the variants' pathogenicity in terms of the perturbed molecular mechanisms.

Rare De Novo Missense Variants in RNA Helicase DDX6 Cause Intellectual Disability and Dysmorphic Features and Lead to P-Body Defects and RNA Dysregulation.

The human RNA helicase DDX6 is an essential component of membrane-less organelles called processing bodies (PBs). PBs are involved in mRNA metabolic processes including translational repression via coordinated storage of mRNAs. Previous studies in human cell lines have implicated altered DDX6 in molecular and cellular dysfunction, but clinical consequences and pathogenesis in humans have yet to be described. Here, we report the identification of five rare de novo missense variants in DDX6 in probands presenting with intellectual disability, developmental delay, and similar dysmorphic features including telecanthus, epicanthus, arched eyebrows, and low-set ears. All five missense variants (p.His372Arg, p.Arg373Gln, p.Cys390Arg, p.Thr391Ile, and p.Thr391Pro) are located in two conserved motifs of the RecA-2 domain of DDX6 involved in RNA binding, helicase activity, and protein-partner binding. We use functional studies to demonstrate that the first variants identified (p.Arg373Gln and p.Cys390Arg) cause significant defects in PB assembly in primary fibroblast and model human cell lines. These variants' interactions with several protein partners were also disrupted in immunoprecipitation assays. Further investigation via complementation assays included the additional variants p.Thr391Ile and p.Thr391Pro, both of which, similarly to p.Arg373Gln and p.Cys390Arg, demonstrated significant defects in P-body assembly. Complementing these molecular findings, modeling of the variants on solved protein structures showed distinct spatial clustering near known protein binding regions. Collectively, our clinical and molecular data describe a neurodevelopmental syndrome associated with pathogenic missense variants in DDX6. Additionally, we suggest DDX6 join the DExD/H-box genes DDX3X and DHX30 in an emerging class of neurodevelopmental disorders involving RNA helicases.

PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence.

Motivation: Machine learning plays a substantial role in bioscience owing to the explosive growth in sequence data and the challenging application of computational methods. Peptide-recognition domains (PRDs) are critical as they promote coupled-binding with short peptide-motifs of functional importance through transient interactions. It is challenging to build a reliable predictor of peptide-binding residue in proteins with diverse types of PRDs from protein sequence alone. On the other hand, it is vital to cope up with the sequencing speed and to broaden the scope of study. Results: In this paper, we propose a machine-learning-based tool, named PBRpredict, to predict residues in peptide-binding domains from protein sequence alone. To develop a generic predictor, we train the models on peptide-binding residues of diverse types of domains. As inputs to the models, we use a high-dimensional feature set of chemical, structural and evolutionary information extracted from protein sequence. We carefully investigate six different state-of-the-art classification algorithms for this application. Finally, we use the stacked generalization approach to non-linearly combine a set of complementary base-level learners using a meta-level learner which outperformed the winner-takes-all approach. The proposed predictor is found competitive based on statistical evaluation. Availability and implementation: PBRpredict-Suite software: http://cs.uno.edu/~tamjid/Software/PBRpredict/pbrpredict-suite.zip. Supplementary information: Supplementary data are available at Bioinformatics online.

A balanced secondary structure predictor.

Secondary structure (SS) refers to the local spatial organization of a polypeptide backbone atoms of a protein. Accurate prediction of SS can provide crucial features to form the next higher level of 3D structure of a protein accurately. SS has three different major components, helix (H), beta (E) and coil (C). Most of the SS predictors express imbalanced accuracies by claiming higher prediction performances in predicting H and C, and on the contrary having low accuracy in E predictions. E component being in low count, a predictor may show very good overall performance by over-predicting H and C and under predicting E, which can make such predictors biologically inapplicable. In this work we are motivated to develop a balanced SS predictor by incorporating 33 physicochemical properties into 15-tuble peptides via Chou׳s general PseAAC, which allowed obtaining higher accuracies in predicting all three SS components. Our approach uses three different support vector machines for binary classification of the major classes and then form optimized multiclass predictor using genetic algorithm (GA). The trained three binary SVMs are E versus non-E (i.e., E/¬E), C/¬C and H/¬H. This GA based optimized and combined three class predictor, called cSVM, is further combined with SPINE X to form the proposed final balanced predictor, called MetaSSPred. This novel paradigm assists us in optimizing the precision and recall. We prepared two independent test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. MetaSSPred significantly increases beta accuracy (QE) for both the datasets. QE score of MetaSSPred on CB471 and N295 were 71.7% and 74.4% respectively. These scores are 20.9% and 19.0% improvement over the QE scores given by SPINE X alone on CB471 and N295 datasets respectively. Standard deviations of the accuracies across three SS classes of MetaSSPred on CB471 and N295 datasets were 4.2% and 2.3% respectively. On the other hand, for SPINE X, these values are 12.9% and 10.9% respectively. These findings suggest that the proposed MetaSSPred is a well-balanced SS predictor compared to the state-of-the-art SPINE X predictor.