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

Inter-BRCT linker is probably the most intolerant region of the BRCA1 BRCT domain

Pathogenic mutations in BRCA1 are associated with an increased risk of hereditary breast, ovarian, and some other cancers; however, the clinical significance of many mutations in this gene remains unknown (Variants of Unknown Significance/VUS). Since mutations in intolerant regions of a protein lead to dysfunction and pathogenicity, identifying these regions helps to predict the clinical importance of VUSs. This study aimed to identify intolerant regions of BRCA1 and understand the possible root of this susceptibility. Intolerant regions appear to carry more pathogenic mutations than expected due to their lower tolerance to missense variations. Therefore, we hypothesized that among the BRCA1 regions, the higher the mutation density, the greater the intolerance. Thus, pathogenic mutation density and regional intolerance scores were calculated to identify BRCA1-intolerant regions. To investigate the pathogenic mechanisms of missense-intolerant regions in BRCA1, transcription activation (TA) experiments and molecular dynamics (MD) simulations were also performed. The results showed that the RING domain, followed by the BRCT domain, has the highest density of pathogenic mutations. In the BRCT domain, a higher density of pathogenic mutations was observed in the inter-BRCT linker. Additionally, scores generated by Missense Tolerance Ratio-3D (MTR3D) and the Missense Tolerance Ratio consensus (MTRX) showed that the inter-BRCT linker is more intolerant than other regions of the BRCT domain. The MD results showed that mutations in the inter-BRCT linker led to cancer susceptibility, likely due to disruption of the interaction between BRCA1 and phosphopeptides. TA laboratory assays further supported the importance of the inter-BRCT linker.Communicated by Ramaswamy H. Sarma.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort

Genetic testing has become an essential component in the diagnosis and management of a wide range of clinical conditions, from cancer to developmental disorders, especially in rare Mendelian diseases. Efforts to identify rare phenotype-associated variants have predominantly focused on protein-truncating variants, while the interpretation of missense variants presents a considerable challenge. Deep learning algorithms excel in various applications across biomedical tasks1,2, yet accurately distinguishing between pathogenic and benign genetic variants remains an elusive goal3-5. Specifically, even the most sophisticated models encounter difficulties in accurately assessing the pathogenicity of missense variants of uncertain significance (VUS). Our investigation of AlphaMissense (AM)5, the latest iteration of deep learning methods for predicting the potential functional impact of missense variants and assessing gene essentiality, reveals important limitations in its ability to identify pathogenic missense variants within a rare disease cohort. Indeed, AM struggles to accurately assess the pathogenicity of variants in intrinsically disordered regions (IDRs), leading to unreliable gene-level essentiality scores for certain genes containing IDRs. This limitation highlights the challenges in applying AM faces in the context of clinical genetics6.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Enigmatic missense mutations can cause disease via creation of de novo nuclear export signals

Disease-causing missense mutations that occur within structurally and functionally unannotated protein regions can guide researchers to new mechanisms of protein regulation and dysfunction. Here, we report that the thrombocytopenia-, myelodysplastic syndromes-, and leukemia-associated P214L mutation in the transcriptional regulator ETV6 creates an XPO1-dependent nuclear export signal to cause protein mislocalization. Strategies to disrupt XPO1 activity fully restore ETV6 P214L protein nuclear localization and transcription regulation activity. Mechanistic insight inspired the design of a 'humanized' ETV6 mice, which we employ to demonstrate that the germline P214L mutation is sufficient to elicit severe defects in thrombopoiesis and hematopoietic stem cell maintenance. Beyond ETV6, we employed computational methods to uncover rare disease-associated missense mutations in unrelated proteins that create a nuclear export signal to disrupt protein function. Thus, missense mutations that operate through this mechanism should be predictable and may suggest rational therapeutic strategies for associated diseases.

A multiscale approach reveals the molecular architecture of the autoinhibited kinesin KIF5A

Kinesin-1 is a microtubule motor that transports cellular cargo along microtubules. KIF5A is one of three kinesin-1 isoforms in humans, all of which are autoinhibited by an interaction between the motor and an IAK motif in the proximal region of the C-terminal tail. The C-terminal tail of KIF5A is ∼80 residues longer than the other two kinesin-1 isoforms (KIF5B and KIF5C) and it is unclear if it contributes to autoinhibition. Mutations in KIF5A cause neuronal diseases and could affect autoinhibition, as reported for a mutation that skips exon 27, altering its C-terminal sequence. Here, we combined negative-stain electron microscopy, crosslinking mass spectrometry (XL-MS) and AlphaFold2 structure prediction to determine the molecular architecture of the full-length autoinhibited KIF5A homodimer, in the absence of light chains. We show that KIF5A forms a compact, bent conformation, through a bend between coiled-coils 2 and 3, around P687. XL-MS of WT KIF5A revealed extensive interactions between residues in the motor, between coiled-coil 1 and the motor, between coiled-coils 1 and 2, with coiled-coils 3 and 4, and the proximal region of the C-terminal tail and the motor in the autoinhibited state, but not between the distal C-terminal region and the rest of the molecule. While negative-stain electron microscopy of exon-27 KIF5A splice mutant showed the presence of autoinhibited molecules, XL-MS analysis suggested that its autoinhibited state is more labile. Our model offers a conceptual framework for understanding how mutations within the motor and stalk domain may affect motor activity.

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

A Comprehensive Report of Intrinsically Disordered Regions in Inherited Retinal Diseases

BACKGROUND/PURPOSE

A comprehensive review of the degree of disorder in all genes in the Retinal Information Network (RetNet) Database is implicated in inherited retinal diseases (IRDs). Their association with a missense variation was evaluated.

METHODS

IRD genes from RetNet were included in this study. Publicly available data on the genome aggregation database (gnomAD) were used to analyze the number of total and pathogenic missense variants. Metapredict, an accurate and high-performance predictor that reproduces consensus disorder scores, was used to calculate disorder.

MAIN OUTCOME MEASURES

The main outcome measures were percent disorder, percent pathogenicity, number of total missense variants, and percent total missense variation.

RESULTS

We included 287 RetNet genes with relevant data available from gnomAD. Mean percent disorder was 26.3% ± 26.0%, mean percent pathogenicity was 5.2% ± 11.0%, mean number of total missense variants was 424.4 ± 450.0, and mean percent total missense was 50.0% ± 13.4%. The percent disorder followed a bimodal distribution with the highest number of occurrences in the 0 to 10th disorder decile. The five outlier proteins in the first disorder decile with a higher-than-expected number of total missense variation were identified (HMCN1, ADGRV, USH2A, DYNC2H1, LAMA1, and SLC38A8). When excluded, % total missense was significantly associated with percent disorder (R = 0.238 and p = 0.0240).

CONCLUSIONS

This novel study examining all genes implicated in IRDs found that the majority genes had a disorder in the 0 to 10th decile and were relatively intolerant to missense variation. This may have future utility when interpreting variants of undetermined significance and missense variants.

Implementation of multigene panel testing for breast and ovarian cancer in South Africa: A step towards excellence in oncology for the public sector

Translation of genomic knowledge into public health benefits requires the implementation of evidence-based recommendations in clinical practice. In this study, we moved beyond BRCA1/2 susceptibility testing in breast and ovarian cancer patients to explore the application of pharmacogenetics across multiple genes participating in homologous recombination DNA damage repair. This involved the utilisation of next-generation sequencing (NGS) at the intersection of research and service delivery for development of a comprehensive genetic testing platform in South Africa. Lack of international consensus regarding risk categorization of established cancer susceptibility genes and the level of evidence required for prediction of drug response supported the development of a central database to facilitate clinical interpretation. Here we demonstrate the value of this approach using NGS to 1) determine the variant spectrum applicable to targeted therapy and implementation of prevention strategies using the 15-gene Oncomine™ BRCA Expanded Panel, and 2) searched for novel and known pathogenic variants in uninformative cases using whole exome sequencing (WES). Targeted NGS performed as a routine clinical service in 414 South African breast and/or ovarian cancer patients resulted in the detection of 48 actionable variants among 319 (15%) cases. BRCA1/2-associated cancers were identified in 70.8% of patients (34/48, including two double-heterozygotes), with the majority (35.3%, 12/34) representing known South African founder variants. Detection of actionable variants in established non-BRCA1/2 risk genes contributed 29% to the total percentage (14/48), distributed amongst ATM, CHEK2, BARD1, BRIP1, PALB2 and TP53. Experimental WES using a virtually constructed multi-cancer NGS panel in 16 genetically unresolved cases (and four controls) revealed novel protein truncating variants in the basal cell carcinoma gene PTCH1 (c.4187delG) and the signal transmission and transduction gene KIT (c.930delA) involved in crucial cellular processes. Based on these findings, the most cost-effective approach would be to perform BRCA1/2 founder variant testing at referral, followed by targeted multigene panel testing if clinically indicated and addition of WES in unresolved cases. This inventive step provides a constant flow of new knowledge into the diagnostic platform via a uniquely South African pathology-supported genetic approach implemented for the first time in this context to integrate research with service delivery.

Enrichment patterns of intrinsic disorder in proteins

Intrinsically disordered regions in proteins have been shown to be important in protein function. However, not all proteins contain the same amount of intrinsic disorder. The variation in the levels of intrinsic disorder in different types of proteins has been extensively studied over the last two decades. It is now known that the levels of intrinsic disorder vary in proteins across organisms, functions, diseases, and cellular locations. This review consolidates the known trends in the abundance of intrinsic disorder identified in groups of proteins across varying conditions and functions. It also presents new data towards the understanding of intrinsic disorder in cell type-specific proteins.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01016-7.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.