Prediction of pathological mutations in proteins: the challenge of integrating sequence conservation and structure stability principles

A New Set of in Silico Tools to Support the Interpretation of ATM Missense Variants Using Graphical Analysis

Establishing the pathogenic nature of variants in ATM, a gene associated with breast cancer and other hereditary cancers, is crucial for providing patients with adequate care. Unfortunately, achieving good variant classification is still difficult. To address this challenge, we extended the range of in silico tools with a series of graphical tools devised for the analysis of computational evidence by health care professionals. We propose a family of fast and easy-to-use graphical representations in which the impact of a variant is considered relative to other pathogenic and benign variants. To illustrate their value, the representations are applied to three problems in variant interpretation. The assessment of computational pathogenicity predictions showed that the graphics provide an intuitive view of prediction reliability, complementing and extending conventional numerical reliability indexes. When applied to variant of unknown significance populations, the representations shed light on the nature of these variants and can be used to prioritize variants of unknown significance for further studies. In a third application, the graphics were used to compare the two versions of the ATM-adapted American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines, obtaining valuable information on their relative virtues and weaknesses. Finally, a server [ATMision (ATM missense in silico interpretation online)] was generated for users to apply these representations in their variant interpretation problems, to check the ATM-adapted guidelines' criteria for computational evidence on their variant(s) and access different sources of information.

The conformational and mutational landscape of the ubiquitin-like marker for autophagosome formation in cancer

Macroautophagy/autophagy is a cellular process to recycle damaged cellular components, and its modulation can be exploited for disease treatments. A key autophagy player is the ubiquitin-like protein MAP1LC3B/LC3B. Mutations and changes in MAP1LC3B expression occur in cancer samples. However, the investigation of the effects of these mutations on MAP1LC3B protein structure is still missing. Despite many LC3B structures that have been solved, a comprehensive study, including dynamics, has not yet been undertaken. To address this knowledge gap, we assessed nine physical models for biomolecular simulations for their capabilities to describe the structural ensemble of MAP1LC3B. With the resulting MAP1LC3B structural ensembles, we characterized the impact of 26 missense mutations from pan-cancer studies with different approaches, and we experimentally validated our prediction for six variants using cellular assays. Our findings shed light on damaging or neutral mutations in MAP1LC3B, providing an atlas of its modifications in cancer. In particular, P32Q mutation was found detrimental for protein stability with a propensity to aggregation. In a broader context, our framework can be applied to assess the pathogenicity of protein mutations or to prioritize variants for experimental studies, allowing to comprehensively account for different aspects that mutational events alter in terms of protein structure and function.Abbreviations: ATG: autophagy-related; Cα: alpha carbon; CG: coarse-grained; CHARMM: Chemistry at Harvard macromolecular mechanics; CONAN: contact analysis; FUNDC1: FUN14 domain containing 1; FYCO1: FYVE and coiled-coil domain containing 1; GABARAP: GABA type A receptor-associated protein; GROMACS: Groningen machine for chemical simulations; HP: hydrophobic pocket; LIR: LC3 interacting region; MAP1LC3B/LC3B microtubule associated protein 1 light chain 3 B; MD: molecular dynamics; OPTN: optineurin; OSF: open software foundation; PE: phosphatidylethanolamine, PLEKHM1: pleckstrin homology domain-containing family M 1; PSN: protein structure network; PTM: post-translational modification; SA: structural alphabet; SLiM: short linear motif; SQSTM1/p62: sequestosome 1; WT: wild-type.

Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants

The present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvements.

IDRMutPred: predicting disease-associated germline nonsynonymous single nucleotide variants (nsSNVs) in intrinsically disordered regions

Motivation

Despite of the lack of folded structure, intrinsically disordered regions (IDRs) of proteins play versatile roles in various biological processes, and many nonsynonymous single nucleotide variants (nsSNVs) in IDRs are associated with human diseases. The continuous accumulation of nsSNVs resulted from the wide application of NGS has driven the development of disease-association prediction methods for decades. However, their performance on nsSNVs in IDRs remains inferior, possibly due to the domination of nsSNVs from structured regions in training data. Therefore, it is highly demanding to build a disease-association predictor specifically for nsSNVs in IDRs with better performance.

Results

We present IDRMutPred, a machine learning-based tool specifically for predicting disease-associated germline nsSNVs in IDRs. Based on 17 selected optimal features that are extracted from sequence alignments, protein annotations, hydrophobicity indices and disorder scores, IDRMutPred was trained using three ensemble learning algorithms on the training dataset containing only IDR nsSNVs. The evaluation on the two testing datasets shows that all the three prediction models outperform 17 other popular general predictors significantly, achieving the ACC between 0.856 and 0.868 and MCC between 0.713 and 0.737. IDRMutPred will prioritize disease-associated IDR germline nsSNVs more reliably than general predictors.

Availability and implementation

The software is freely available at http://www.wdspdb.com/IDRMutPred.

Supplementary information

Supplementary data are available at Bioinformatics online.

BRCA1- and BRCA2-specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene-specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.

Compensated pathogenic variants in coagulation factors VIII and IX present complex mapping between molecular impact and hemophilia severity

Compensated pathogenic deviations (CPDs) are sequence variants that are pathogenic in humans but neutral in other species. In recent years, our molecular understanding of CPDs has advanced substantially. For example, it is known that their impact on human proteins is generally milder than that of average pathogenic mutations and that their impact is suppressed in non-human carriers by compensatory mutations. However, prior studies have ignored the evolutionarily relevant relationship between molecular impact and organismal phenotype. Here, we explore this topic using CPDs from FVIII and FIX and data concerning carriers' hemophilia severity. We find that, regardless of their molecular impact, these mutations can be associated with either mild or severe disease phenotypes. Only a weak relationship is found between protein stability changes and severity. We also characterize the population variability of hemostasis proteins, which constitute the genetic background of FVIII and FIX, using data from the 1000 Genome project. We observe that genetic background can vary substantially between individuals in terms of both the amount and nature of genetic variants. Finally, we discuss how these results highlight the need to include new terms in present models of protein evolution to explain the origin of CPDs.

Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.

Increased dNTP pools rescue mtDNA depletion in human POLG-deficient fibroblasts

Polymerase γ catalytic subunit (POLG) gene encodes the enzyme responsible for mitochondrial DNA (mtDNA) synthesis. Mutations affecting POLG are the most prevalent cause of mitochondrial disease because of defective mtDNA replication and lead to a wide spectrum of clinical phenotypes characterized by mtDNA deletions or depletion. Enhancing mitochondrial deoxyribonucleoside triphosphate (dNTP) synthesis effectively rescues mtDNA depletion in different models of defective mtDNA maintenance due to dNTP insufficiency. In this study, we studied mtDNA copy number recovery rates following ethidium bromide-forced depletion in quiescent fibroblasts from patients harboring mutations in different domains of POLG. Whereas control cells spontaneously recovered initial mtDNA levels, POLG-deficient cells experienced a more severe depletion and could not repopulate mtDNA. However, activation of deoxyribonucleoside (dN) salvage by supplementation with dNs plus erythro-9-(2-hydroxy-3-nonyl) adenine (inhibitor of deoxyadenosine degradation) led to increased mitochondrial dNTP pools and promoted mtDNA repopulation in all tested POLG-mutant cells independently of their specific genetic defect. The treatment did not compromise POLG fidelity because no increase in multiple deletions or point mutations was detected. Our study suggests that physiologic dNTP concentration limits the mtDNA replication rate. We thus propose that increasing mitochondrial dNTP availability could be of therapeutic interest for POLG deficiency and other conditions in which mtDNA maintenance is challenged.-Blázquez-Bermejo, C., Carreño-Gago, L., Molina-Granada, D., Aguirre, J., Ramón, J., Torres-Torronteras, J., Cabrera-Pérez, R., Martín, M. Á., Domínguez-González, C., de la Cruz, X., Lombès, A., García-Arumí, E., Martí, R., Cámara, Y. Increased dNTP pools rescue mtDNA depletion in human POLG-deficient fibroblasts.

Early Versus Late Diagnosis of Complement Factor I Deficiency: Clinical Consequences Illustrated in Two Families with Novel Homozygous CFI Mutations

The complement system is an important effector arm of innate immunity and plays a crucial role in the defense against common pathogens. But effective defense and maintenance of homeostasis requires a careful balance between complement activation and regulation. Factor I (FI) is one of the most important regulators of the complement system. Complete FI deficiency is a rare autosomal recessive disorder typically resulting in severe, recurrent infection by encapsulated bacteria. In the present study, we describe two patients from unrelated families with complete FI deficiency diagnosed at very different ages: Patient 1 is a 60-year-old man who had experienced several severe infections (pneumonia, meningitis, sepsis) since childhood, one of which caused significant and permanent neurologic sequelae. In contrast, patient 2 was diagnosed at the age of 4 years after a single infectious episode (otitis media) and through detection of a flat beta2 peak on serum protein electrophoresis. This early diagnosis of FI deficiency enabled prompt implementation of a therapeutic intervention consisting of vaccination with encapsulated bacteria and prophylactic antibiotics. The two patients had novel homozygous mutations in the CFI gene (p.Gly162Asp and p.His380Arg) that disrupted protein function. Interestingly, p.His380Arg is the first mutation described affecting a residue of the highly conserved FI catalytic triad (His380, Asp429, and Ser525). This study illustrates the importance of early versus late diagnosis of FI deficiency and, in general, highlights the clinical relevance of prompt detection of complement system deficiencies.

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Background

Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation.

Results

We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL).

Conclusion

Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3914-0) contains supplementary material, which is available to authorized users.

Identification and characterization of the novel point mutation m.3634A>G in the mitochondrial MT-ND1 gene associated with LHON syndrome

Leber's hereditary optic neuropathy (LHON) is a mitochondrial genetic disease characterized by bilateral acute or subacute progressive central visual loss. Most cases of LHON syndrome are caused by point mutations in the MT-ND1, MT-ND4, and MT-ND6 genes. Here, we report a novel homoplasmic mutation in the MT-ND1 gene (m.3634A>G, p.Ser110Gly) in a patient with the classical clinical features of LHON syndrome. Several observations support the idea that the mutation is pathogenic and involved in the clinical phenotype of the patient: 1) The mutation affected a highly conserved amino acid, 2) A pathogenic mutation in the same amino acid (m.3635G>A, p.Ser110Asn) was previously reported in a patient with LHON syndrome, 3) The mutation is not recorded in the Mitomap or Human Mitochondrial Genome Database, 4) In silico predictors classified the mutation as "probably damaging", and 5) Cybrids carrying the mutation showed decreased Complex I enzyme activity, lower cell proliferation, and decreased mitochondrial membrane potential relative to control cybrids.

Acute Intermittent Porphyria: Predicted Pathogenicity of HMBS Variants Indicates Extremely Low Penetrance of the Autosomal Dominant Disease

Acute intermittent porphyria results from hydroxymethylbilane synthase (HMBS) mutations that markedly decrease HMBS enzymatic activity. This dominant disease is diagnosed when heterozygotes have life-threatening acute attacks, while most heterozygotes remain asymptomatic and undiagnosed. Although >400 HMBS mutations have been reported, the prevalence of pathogenic HMBS mutations in genomic/exomic databases, and the actual disease penetrance are unknown. Thus, we interrogated genomic/exomic databases, identified non-synonymous variants (NSVs) and consensus splice-site variants (CSSVs) in various demographic/racial groups, and determined the NSV's pathogenicity by prediction algorithms and in vitro expression assays. Caucasians had the most: 58 NSVs and two CSSVs among ∼92,000 alleles, a 0.00575 combined allele frequency. In silico algorithms predicted 14 out of 58 NSVs as "likely-pathogenic." In vitro expression identified 10 out of 58 NSVs as likely-pathogenic (seven predicted in silico), which together with two CSSVs had a combined allele frequency of 0.00056. Notably, six presumably pathogenic mutations/NSVs in the Human Gene Mutation Database were benign. Compared with the recent prevalence estimate of symptomatic European heterozygotes (∼0.000005), the prevalence of likely-pathogenic HMBS mutations among Caucasians was >100 times more frequent. Thus, the estimated penetrance of acute attacks was ∼1% of heterozygotes with likely-pathogenic mutations, highlighting the importance of predisposing/protective genes and environmental modifiers that precipitate/prevent the attacks.

Molecular damage in Fabry disease: characterization and prediction of alpha-galactosidase A pathological mutations

Loss-of-function mutations of the enzyme alpha-galactosidase A (GLA) causes Fabry disease (FD), that is a rare and potentially fatal disease. Identification of these pathological mutations by sequencing is important because it allows an early treatment of the disease. However, before taking any treatment decision, if the mutation identified is unknown, we first need to establish if it is pathological or not. General bioinformatic tools (PolyPhen-2, SIFT, Condel, etc.) can be used for this purpose, but their performance is still limited. Here we present a new tool, specifically derived for the assessment of GLA mutations. We first compared mutations of this enzyme known to cause FD with neutral sequence variants, using several structure and sequence properties. Then, we used these properties to develop a family of prediction methods adapted to different quality requirements. Trained and tested on a set of known Fabry mutations, our methods have a performance (Matthews correlation: 0.56-0.72) comparable or better than that of the more complex method, Polyphen-2 (Matthews correlation: 0.61), and better than those of SIFT (Matthews correl.: 0.54) and Condel (Matthews correl.: 0.51). This result is validated in an independent set of 65 pathological mutations, for which our method displayed the best success rate (91.0%, 87.7%, and 73.8%, for our method, PolyPhen-2 and SIFT, respectively). These data confirmed that our specific approach can effectively contribute to the identification of pathological mutations in GLA, and therefore enhance the use of sequence information in the identification of undiagnosed Fabry patients.