HuVarBase: A human variant database with comprehensive information at gene and protein levels

ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models

Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs. The representations derived from protein language models (PLMs) from the structural neighbourhood of mutated sites were utilized to train ZFP-CanPred for differentiating between cancer-causing and neutral mutations. ZFP-CanPred, achieved a superior performance with an accuracy of 0.72, F1-score of 0.79, and area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.74, on an independent test set. In a comparative analysis against 11 existing prediction tools using a curated dataset of 331 mutations, ZFP-CanPred demonstrated the highest AU-ROC of 0.74, outperforming both generic and cancer-specific methods. The model's balanced performance across specificity and sensitivity addresses a significant limitation of current methodologies. The source code and other related files are available on GitHub at https://github.com/amitphogat/ZFP-CanPred.git. We envisage that the present study contributes to understand the oncogenic processes and developing targeted therapeutic strategies.

Molecular Interactions of the Pioneer Transcription Factor GATA3 With DNA

The GATA3 transcription factor is a pioneer transcription factor that is critical in the development, proliferation, and maintenance of several immune cell types. Identifying the detailed conformational dynamics and interactions of this transcription factor, as well as its clinically important population variants will allow us to unravel its mode of action. In this study, we analyze the molecular interactions of the GATA3 transcription factor bound to dsDNA as well as three clinically important population variants by atomistic molecular dynamics simulations. We identify the effect of the variants on the DNA conformational dynamics and delineate the differences compared to the wildtype transcription factor that could be related to impaired function. We highlight the structural plasticity in the binding of the GATA3 transcription factor and identify important DNA-protein contacts. Although the DNA-protein contacts are persistent and appear to be stable, they exhibit nanosecond timescale fluctuations and several binding/unbinding events. Further, we identify differential DNA binding in the three variants and show that the N-terminal binding is reduced in two of the variants. Our results indicate that reduced minor groove width and DNA diameter are important hallmarks for the binding of GATA3. Our work is an important step towards understanding the functional dynamics of the GATA3 protein and its clinically significant population variants.

Unlocking the secrets of the immunopeptidome: MHC molecules, ncRNA peptides, and vesicles in immune response

The immunopeptidome, a diverse set of peptides presented by Major Histocompatibility Complex (MHC) molecules, is a critical component of immune recognition and response. This review article delves into the mechanisms of peptide presentation by MHC molecules, particularly emphasizing the roles of ncRNA-derived peptides and extracellular vesicles (EVs) in shaping the immunopeptidome landscape. We explore established and emerging insights into MHC molecule interactions with peptides, including the dynamics of peptide loading, transport, and the influence of cellular and genetic variations. The article highlights novel research on non-coding RNA (ncRNA)-derived peptides, which challenge conventional views of antigen processing and presentation and the role of EVs in transporting these peptides, thereby modulating immune responses at remote body sites. This novel research not only challenges conventional views but also opens up new avenues for understanding immune responses. Furthermore, we discuss the implications of these mechanisms in developing therapeutic strategies, particularly for cancer immunotherapy. By conducting a comprehensive analysis of current literature and advanced methodologies in immunopeptidomics, this review aims to deepen the understanding of the complex interplay between MHC peptide presentation and the immune system, offering new perspectives on potential diagnostic and therapeutic applications. Additionally, the interactions between ncRNA-derived peptides and EVs provide a mechanism for the enhanced surface presentation of these peptides and highlight a novel pathway for their systemic distribution, potentially altering immune surveillance and therapeutic landscapes.

On the abundance and importance of AXXXA sequence motifs in globular proteins and their involvement in CβCβ interaction

The AXXXA and GXXXG motifs are frequently observed in helices, especially in membrane proteins. The motif GXXXG is known to stabilize helix-helix association in membrane proteins via CαHO bonding. AXXXA sequence motif additionally stabilizes the folded state of proteins. We found 27,000 and 18,000 occurrences of AXXXA and GXXXG motifs in a non-redundant set of 6000 obligate homodimeric (OD) complexes. Interestingly, this is less pronounced in transient homodimers (TD) and heterodimers (HetD). On average each obligate homodimer contains four AXXXA motifs, it is 2 and 3.5 for HetD and TD, respectively. Focusing on the binding surface it is seen that 27 % of the ODs contain at least one AXXXA motif at the interface, whereas it is 17 % and 15 % for HetD and TD respectively. AXXXA predominantly stabilizes the OD quaternary structure via the side chain CβCβ interactions. This interaction is energetically favorable and is found to be a major driving force for OD quaternary structure stability. Cβ-Cβ interactions are observed ∼6 times higher than the known CαHO interaction for helix-helix stabilization. Two additional new interactions of CβO and OO are observed at the AXXXA containing interface regions. The occurrence of the motif gets drastically reduced if any of the terminal Ala residues are replaced by Gly. Our findings show the importance of AXXXA in providing stability to the quaternary structure through specific hydrophobic interactions and the specificity of the Ala residue at motif termini. The knowledge gained can be used for designing synthetic proteins of improved stability and for designing peptide-based therapeutics.

AlzDiscovery: A computational tool to identify Alzheimer's disease-causing missense mutations using protein structure information

Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment. Here, we present a machine learning-based analysis to classify the AD-causing mutations from their benign counterparts in 21 AD-related proteins leveraging both sequence- and structure-based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD-related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state-of-the-art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user-friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD-related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

The human retroviral-like aspartic protease 1 (ASPRV1): From in vitro studies to clinical correlations

The human retroviral-like aspartic protease 1 (ASPRV1) is a retroviral-like protein that was first identified in the skin due to its expression in the stratum granulosum layer of the epidermis. Accordingly, it is also referred to as skin-specific aspartic protease. Similar to the retroviral polyproteins, the full-length ASPRV1 also undergoes self-proteolysis, the processing of the precursor is necessary for the autoactivation of the protease domain. ASPRV1's functions are well-established at the level of the skin: it is part of the epidermal proteolytic network and has a significant contribution to skin moisturization via the limited proteolysis of filaggrin; it is only natural protein substrate identified so far. Filaggrin and ASPRV1 are also specific for mammalians, these proteins provide unique features for the skins of these species, and the importance of filaggrin processing in hydration is proved by the fact that some ASPRV1 mutations are associated with skin diseases such as ichthyosis. ASPRV1 was also found to be expressed in macrophage-like neutrophil cells, indicating that its functions are not limited to the skin. In addition, differential expression of ASPRV1 was detected in many diseases, with yet unknown significance. The currently known enzymatic characteristics-that had been revealed mainly by in vitro studies-and correlations with pathogenic phenotypes imply potentially important functions in multiple cell types, which makes the protein a promising target of functional studies. In this review we describe the currently available knowledge and future perspective in regard to ASPRV1.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Variant Effect Prediction in the Age of Machine Learning

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Precise prediction of phase-separation key residues by machine learning

Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.

In silico and structure-based evaluation of deleterious mutations identified in human Chk1, Chk2, and Wee1 protein kinase

Checkpoint kinases Chk1, Chk2, Wee1 are playing a key role in DNA damage response and genomic integrity. Cancer-associated mutations identified in human Chk1, Chk2, and Wee1 were retrieved to understand the function associated with the mutation and also alterations in the folding pattern. Therefore, an attempt has been made to identify deleterious effect of variants using in silico and structure-based approach. Variants of uncertain significance for Chk1, Chk2, and Wee1 were retrieved from different databases and four prediction servers were employed to predict pathogenicity of mutations. Further, Interpro, I-Mutant 3.0, Consurf, TM-align, and have (y)our protein explained were used for comprehensive study of the deleterious effects of variants. The sequences of Chk1, Chk2, and Wee1 were analyzed using Clustal Omega, and the three-dimensional structures of the proteins were aligned using TM-align. The molecular dynamics simulations were performed to explore the differences in folding pattern between Chk1, Chk2, Wee1 wild-type, and mutant protein and also to evaluate the structural integrity. Thirty-six variants in Chk1, 250 Variants in Chk2, and 29 in Wee1 were categorized as pathogenic using in silico prediction tools. Furthermore, 25 mutations in Chk1, 189 in Chk2, and 14 in Wee1 were highly conserved, possessing deleterious effect and also influencing the protein structure and function. These identified mutations may provide underlying genetic intricacies to serve as potential targets for therapeutic inventions and clinical management.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Mitochondrial carbonic anhydrase VA and VB: properties and roles in health and disease

Carbonic anhydrase V (CA V), a mitochondrial enzyme, was first isolated from guinea-pig liver and subsequently identified in mice and humans. Later, studies revealed that the mouse genome contains two mitochondrial CA sequences, named Car5A and Car5B. The CA VA enzyme is most highly expressed in the liver, whereas CA VB shows a broad tissue distribution. Car5A knockout mice demonstrated a predominant role for CA VA in ammonia detoxification, whereas the roles of CA VB in ureagenesis and gluconeogenesis were evident only in the absence of CA VA. Previous studies have suggested that CA VA is mainly involved in the provision of HCO₃ ^- for biosynthetic processes. In children, mutations in the CA5A gene led to reduced CA activity, and the enzyme was sensitive to increased temperature. The metabolic profiles of these children showed a reduced supply of HCO₃ ^- to the enzymes that take part in intermediary metabolism: carbamoylphosphate synthetase, pyruvate carboxylase, propionyl-CoA carboxylase and 3-methylcrotonyl-CoA carboxylase. Although the role of CA VB is still poorly understood, a recent study reported that it plays an essential role in human Sertoli cells, which sustain spermatogenesis. Metabolic disease associated with CA VA appears to be more common than other inborn errors of metabolism and responds well to treatment with N-carbamyl-l-glutamate. Therefore, early identification of hyperammonaemia will allow specific treatment with N-carbamyl-l-glutamate and prevent neurological sequelae. Carbonic anhydrase VA deficiency should therefore be considered a treatable condition in the differential diagnosis of hyperammonaemia in neonates and young children.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

Novel clinical, molecular and bioinformatics insights into the genetic background of autism

Background

Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes.

Method

Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits.

Results

Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills.

Conclusions

These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.

Prediction of infectivity of SARS-CoV-2 virus based on Spike-hACE-2 interaction

The COVID-19 pandemic caused by SARS-CoV-2 results almost 3 M death worldwide and till continuing in spite of having several vaccine against the virus. One of the main reasons is the mutations occur in the virus to cope with the environment. Detail study of genomics and proteomics level of each components may help to combat the situation. Spike (S) protein that covers the surface of the virus helps in entry by encountering the host receptor Human Angiotensin-Converting Enzyme-2 (hACE-2) with other different roles. In this study, we accomplish our work with the mutations in receptor binding domain (RBD) of Spike (S) protein considering different aspects like the hACE-2 variants in human populations to get an idea about the varying infectivity of different strains for different population. Several other parameters affecting the viral infectivity and in different diseased condition were also studied which may guide to a better insight in developing future therapeutics.

Correction of a Factor VIII genomic inversion with designer-recombinases

Despite advances in nuclease-based genome editing technologies, correcting human disease-causing genomic inversions remains a challenge. Here, we describe the potential use of a recombinase-based system to correct the 140 kb inversion of the F8 gene frequently found in patients diagnosed with severe Hemophilia A. Employing substrate-linked directed molecular evolution, we develop a coupled heterodimeric recombinase system (RecF8) achieving 30% inversion of the target sequence in human tissue culture cells. Transient RecF8 treatment of endothelial cells, differentiated from patient-derived induced pluripotent stem cells (iPSCs) of a hemophilic donor, results in 12% correction of the inversion and restores Factor VIII mRNA expression. In this work, we present designer-recombinases as an efficient and specific means towards treatment of monogenic diseases caused by large gene inversions.

Correction of disease-causing large genomic inversions remains challenging. Here, the authors developed a dual designer-recombinase system (RecF8) that efficiently corrects a 140 kb inversion frequently found in patients with severe Hemophilia A.

TEAD family transcription factors in development and disease

The balance between stem cell potency and lineage specification entails the integration of both extrinsic and intrinsic cues, which ultimately influence gene expression through the activity of transcription factors. One example of this is provided by the Hippo signalling pathway, which plays a central role in regulating organ size during development. Hippo pathway activity is mediated by the transcriptional co-factors Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which interact with TEA domain (TEAD) proteins to regulate gene expression. Although the roles of YAP and TAZ have been intensively studied, the roles played by TEAD proteins are less well understood. Recent studies have begun to address this, revealing that TEADs regulate the balance between progenitor self-renewal and differentiation throughout various stages of development. Furthermore, it is becoming apparent that TEAD proteins interact with other co-factors that influence stem cell biology. This Primer provides an overview of the role of TEAD proteins during development, focusing on their role in Hippo signalling as well as within other developmental, homeostatic and disease contexts.

D3DistalMutation: a Database to Explore the Effect of Distal Mutations on Enzyme Activity

Enzyme activity is affected by amino acid mutations, particularly mutations near the active site. Increasing evidence has shown that distal mutations more than 10 Å away from the active site may significantly affect enzyme activity. However, it is difficult to study the enzyme regulation mechanism of distal mutations due to the lack of a systematic collection of three-dimensional (3D) structures, highlighting distal mutation site and the corresponding enzyme activity change. Therefore, we constructed a distal mutation database, namely, D3DistalMutation, which relates the distal mutation to enzyme activity. As a result, we observed that approximately 80% of distal mutations could affect enzyme activity and 72.7% of distal mutations would decrease or abolish enzyme activity in D3DistalMutation. Only 6.6% of distal mutations in D3DistalMutation could increase enzyme activity, which have great potential to the industrial field. Among these mutations, the Y to F, S to D, and T to D mutations are most likely to increase enzyme activity, which sheds some light on industrial catalysis. Distal mutations decreasing enzyme activity in the allosteric pocket play an indispensable role in allosteric drug design. In addition, the pockets in the enzyme structures are provided to explore the enzyme regulation mechanism of distal mutations. D3DistalMutation is accessible free of charge at https://www.d3pharma.com/D3DistalMutation/index.php.

The Participation of the Intrinsically Disordered Regions of the bHLH-PAS Transcription Factors in Disease Development

The basic helix–loop–helix/Per-ARNT-SIM (bHLH-PAS) proteins are a family of transcription factors regulating expression of a wide range of genes involved in different functions, ranging from differentiation and development control by oxygen and toxins sensing to circadian clock setting. In addition to the well-preserved DNA-binding bHLH and PAS domains, bHLH-PAS proteins contain long intrinsically disordered C-terminal regions, responsible for regulation of their activity. Our aim was to analyze the potential connection between disordered regions of the bHLH-PAS transcription factors, post-transcriptional modifications and liquid-liquid phase separation, in the context of disease-associated missense mutations. Highly flexible disordered regions, enriched in short motives which are more ordered, are responsible for a wide spectrum of interactions with transcriptional co-regulators. Based on our in silico analysis and taking into account the fact that the functions of transcription factors can be modulated by posttranslational modifications and spontaneous phase separation, we assume that the locations of missense mutations inducing disease states are clearly related to sequences directly undergoing these processes or to sequences responsible for their regulation.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

DINAX- a comprehensive database of inherited ataxias

BACKGROUND

Neurodegenerative disorders such as hereditary ataxia often manifest overlapping symptoms and are likely to be misdiagnosed based on clinical phenotypes. To identify the genes associated with such disorders for diagnostic purposes, geneticists often use high throughput technologies which generate an enormous amount of data on variants whose relevance can be unclear. Besides, analysis and interpretation of high throughput data require gleaning of several web-based resources which can be laborious and time-consuming. To overcome these, we have created a Database for Inherited Ataxia (DINAX), a repository of gene variants from publicly available information.

METHODS

DINAX is implemented as a MySQL relational database using the PHP scripting language. Web interfaces were developed using HTML, CSS, and JavaScript. Variant and phenotype information was collected and manually curated from published literature and primary databases such as OMIM and ClinVar. These were further analyzed to decipher expression and pathway analysis.

RESULTS

DINAX is an inventory of 7166 genomic variants (single nucleotide polymorphisms, deletions, insertions, and translocations) reported till date among the 185 genes associated with different subtypes of inherited ataxia. DINAX implements a dual search methodology for genes and phenotypes linking to ataxia associated genes, variants, and their source. Pathway analysis confirmed their association with ataxia.

CONCLUSION

The database is created to provide a single web source for obtaining information about ataxia related genes. Besides, the database facilitates easy identification of known and reported variants as well as the novel or unreported variants. DINAX is freely available at http://slsdb.manipal.edu/dinax.

Identification of Tumor Antigens in the HLA Peptidome of Patient-derived Xenograft Tumors in Mouse

Personalized cancer immunotherapy targeting patient-specific cancer/testis antigens (CTA) and neoantigens may benefit from large-scale tumor human leukocyte antigen (HLA) peptidome (immunopeptidome) analysis, which aims to accurately identify antigens presented by tumor cells. Although significant efforts have been invested in analyzing the HLA peptidomes of fresh tumors, it is often impossible to obtain sufficient volumes of tumor tissues for comprehensive HLA peptidome characterization. This work attempted to overcome some of these obstacles by using patient-derived xenograft tumors (PDX) in mice as the tissue sources for HLA peptidome analysis. PDX tumors provide a proxy for the expansion of the patient tumor by re-grafting them through several passages to immune-compromised mice. The HLA peptidomes of human biopsies were compared with those derived from PDX tumors. Larger HLA peptidomes were obtained from the significantly larger PDX tumors as compared with the patient biopsies. The HLA peptidomes of different PDX tumors derived from the same source tumor biopsy were very reproducible, even following subsequent passages to new naïve mice. Many CTA-derived HLA peptides were discovered, as well as several potential neoantigens/variant sequences. Taken together, the use of PDX tumors for HLA peptidome analysis serves as a highly expandable and stable source of reproducible and authentic peptidomes, opening up new opportunities for defining large HLA peptidomes when only small tumor biopsies are available. This approach provides a large source for tumor antigens identification, potentially useful for personalized immunotherapy.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

VarSite: Disease variants and protein structure

VarSite is a web server mapping known disease-associated variants from UniProt and ClinVar, together with natural variants from gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image-based and provide both an overview for each human protein, as well as a report for any specific variant of interest. The information can be useful in assessing whether a given variant might be pathogenic or benign. The structural annotations for each position in the protein include protein secondary structure, interactions with ligand, metal, DNA/RNA, or other protein, and various measures of a given variant's possible impact on the protein's function. The 3D locations of the disease-associated variants can be viewed interactively via the 3dmol.js JavaScript viewer, as well as in RasMol and PyMOL. Users can search for specific variants, or sets of variants, by providing the DNA coordinates of the base change(s) of interest. Additionally, various agglomerative analyses are given, such as the mapping of disease and natural variants onto specific Pfam or CATH domains. The server is freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite.

A transient reporter for editing enrichment (TREE) in human cells

Current approaches to identify cell populations that have been modified with deaminase base editing technologies are inefficient and rely on downstream sequencing techniques. In this study, we utilized a blue fluorescent protein (BFP) that converts to green fluorescent protein (GFP) upon a C-to-T substitution as an assay to report directly on base editing activity within a cell. Using this assay, we optimize various base editing transfection parameters and delivery strategies. Moreover, we utilize this assay in conjunction with flow cytometry to develop a transient reporter for editing enrichment (TREE) to efficiently purify base-edited cell populations. Compared to conventional cell enrichment strategies that employ reporters of transfection (RoT), TREE significantly improved the editing efficiency at multiple independent loci, with efficiencies approaching 80%. We also employed the BFP-to-GFP conversion assay to optimize base editor vector design in human pluripotent stem cells (hPSCs), a cell type that is resistant to genome editing and in which modification via base editors has not been previously reported. At last, using these optimized vectors in the context of TREE allowed for the highly efficient editing of hPSCs. We envision TREE as a readily adoptable method to facilitate base editing applications in synthetic biology, disease modeling, and regenerative medicine.

XMAn v2-a database of Homo sapiens mutated peptides

SUMMARY

The 'Unknown Mutation Analysis (XMAn)' database is a compilation of Homo sapiens mutated peptides in FASTA format, that was constructed for facilitating the identification of protein sequence alterations by tandem mass spectrometry detection. The database comprises 2 539 031 non-redundant mutated entries from 17 599 proteins, of which 2 377 103 are missense and 161 928 are nonsense mutations. It can be used in conjunction with search engines that seek the identification of peptide amino acid sequences by matching experimental tandem mass spectrometry data to theoretical sequences from a database.

AVAILABILITY AND IMPLEMENTATION

XMAn v2 can be accessed from github.com/lazarlab/XMAnv2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.