Pathogenicity Prediction Research Articles

Cultured and molecular measures of fecal indicator microbes in Gulf of Mexico recreational waters.

Recreational waters are routinely assessed by enumeration of fecal indicator bacteria, such as enterococci, but research suggests viral indicators may be better suited for recreational water quality applications. Somatic and F+ coliphage were proposed as predictors of viral pathogens in recreational waters, but concurrent measurements of enterococci and coliphages and comparisons across different risk-based beach action values (BAVs) are limited. We collected paired measurements of enterococci, E. coli, somatic and F+ coliphage from three Gulf of Mexico beaches. Enterococci (colony forming units: CFU) were enumerated on mEI agar and by qPCR (Entero1a: target sequence [TS]). E. coli was enumerated by qPCR (EC23S587, TS). Coliphages (plaque forming units: PFU) were enumerated using dead-end hollowfiber ultrafiltration and single agar layer assay. The largest correlation observed was between Entero1a and cultured enterococci (r≥0.51, p≤0.0001) and the smallest (r≤0.01, p=1.0) between F+ coliphage and cultured enterococci. Applying equivalent BAVs for culturable enterococci (60CFU/100mL), Entero1a (9658 TS per 100mL) and suggested somatic (14 PFU/100mL) and F+ (3 PFU/100mL) thresholds resulted in the overall greatest agreement in beach advisory status between coliphages and Entero1a (74-82%) and lowest between cultured enterococci and somatic coliphage (65%). This rich data set not only provides valuable insights on the incidence of coliphage and FIB in the Gulf of Mexico but will also provide a foundation for future research on fecal source identification and water quality forecast modeling in sub-tropical marine waters.

Sequence-structure based prediction of pathogenicity for amino acid substitutions in proteins associated with primary immunodeficiencies

IntroductionPrimary immunodeficiencies (PIDs) are a group of rare genetic disorders characterized by dysfunction of the immune system components. Early diagnosis and treatment are essential to prevent severe or life-threatening complications. PIDs are manifested by diverse clinical symptoms, posing challenges for accurate diagnosis. A key aspect of PID diagnosis is identifying specific amino acid substitutions in the proteins related with heritable diseases. In this study, we have developed classification sequence-structure-property relationships (SSPR) models for predicting the pathogenicity of amino acid substitutions (AAS) in 25 proteins associated with the most important and genetically studied PIDs and encoded genes: IL2RG, JAK3, RAG1, RAG2, ADA, DCLRE1C, CD40LG, WAS, ATM, STAT3, KMT2D, BTK, FOXP3, AIRE, FAS, ELANE, ITGB2, CYBB, G6PD, GATA2, STAT1, IFIH1, NLRP3, MEFV, and SERPING1.MethodsThe data on 4825 pathogenic and benign AASs in the selected proteins were extracted from ClinVar and gnomAD. SSPR models were created for each protein using the MultiPASS software based on the Bayesian algorithm and different levels of MNA (Multilevel Neighborhoods of Atoms) descriptors for the representation of structural formulas of protein fragments including AAS.ResultsThe accuracy of prediction was assessed through a 5-fold cross-validation and compared to other bioinformatics tools, such as SIFT4G, Polyphen2 HDIV, FATHMM, MetaSVM, PROVEAN, ClinPred, and Alpha Missense. The best SSPR models demonstrated high accuracy, with an average ROC AUC of 0.831 ± 0.037, a Balanced accuracy of (0.763 ± 0.034), MCC (0.457 ± 0.06), and F-measure (0.623 ± 0.07) across all genes, outperforming the most popular bioinformatics tools.ConclusionsThe best created SSPR models for the prediction of pathogenicity of amino acid substitutions related with PIDs have been implemented in a freely available web application SAV-Pred (Single Amino acid Variants Predictor, http://www.way2drug.com/SAV-Pred/), which may be a useful tool for medical geneticists and clinicians. The use of SAV-Pred for some clinical cases of PIDs are provided.

An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.

Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy. Our innovative framework combines zero-shot log odds scores and embeddings from the ESM, an evolutionary scale model as features for training a supervised model on gene variants functionally related to the ARSA gene. The zero-shot log odds score feature captures the generic properties of the proteins learned due to its pre-training on millions of sequences in the UniProt data, while the ESM embeddings for the proteins in the ARSA family capture features specific to the family. We also tested our approach on another enzyme, N-acetyl-glucosaminidase (NAGLU), that belongs to the same superfamily as ARSA. Our results demonstrate that the performance of our family models (augmented ESM models) is either comparable or better than the ESM models. The ARSA model compares favorably with the majority of state-of-the-art predictors on area under precision and recall curve (AUPRC) performance metric. However, the NAGLU model outperforms all pathogenicity predictors evaluated in this study on AUPRC metric. The improved AUPRC has relevance in a diagnostic setting where variant prioritization generally entails identifying a small number of pathogenic variants from a larger number of benign variants. Our results also indicate that genes that have sparse or no experimental variant impact data, the family variant data can serve as a proxy training data for making accurate predictions. Attention analysis of active sites and binding sites in ARSA and NAGLU proteins shed light on probable mechanisms of pathogenicity for positions that are highly attended.

Unified meta regression models for rare variant association studies.

Rare variant association studies (RVAS) of complex traits have emerged as a powerful approach to advance drug discovery and diagnostics. Missense pathogenicity predictions from AlphaMissense based on structural context and protein language models improve the differentiation between benign and deleterious variants. Constraint metrics, on the other hand, allow researchers to pinpoint genomic regions under selective pressure that may not directly impact protein structure, but are more likely to contain functionally important mutations. Loss-of-function (LoF) variants, which result in the complete or partial loss of protein function, are particularly informative, as it is more straightforward to assess their downstream functional consequences. In this study, we present a unified meta regression model approach that incorporates the probability of pathogenicity, probability of constraint, and indicator whether a variant is a predicted loss-of-function or missense variant as features to model the observed effect size and uncertainty of effect size obtained from single-variant genetic analysis. We applied the unified meta regression model to 1,144 continuous phenotypes from UK Biobank using single variant summary statistics obtained from Genebass. We replicated our findings using the AllofUS cohort. For each gene discovery, we make available a characterization of whether constrained sites are associated with the phenotype, whether pathogenic sites determined by structural based predictions are associated with phenotype, and whether broader loss-of-function or missense variant annotation better explains the summary statistics observed. Our results are publicly available at Global Biobank Engine ( https://biobankengine.shinyapps.io/phenome-wide-unified-model/ ).

AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences and relatively limited number of structures. Leveraging the highly accurate protein structures predicted by AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines sequence and AF2-based structural characteristics to predict missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense. We also showed that the recruitment of AF2 structures that are full-length and represent the unbound states ensures more precise SASA calculations compared to the recruitment of experimental structures. In line with the completeness of the AF2 structures, their use provide a more comprehensive view of the structural characteristics of the missense variation datasets by capturing all variants. AFFIPred maintains high-level accuracy without the limitations of PDB-based classifiers. AFFIPred has predicted over 210 million variations of the human proteome, which are accessible at https://affipred.timucinlab.com/.

An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

Background and objectiveAccurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).MethodsThe study used a dataset from 502 HNSC patients, classifying mutations based on 299 known high-confidence cancer driver genes. Missense somatic mutations in driver genes were treated as driver mutations, while non-driver mutations were randomly selected from other genes. Each mutation was annotated with 41 PCSAs. Three machine learning algorithms—logistic regression, random forest, and support vector machine—along with recursive feature elimination, were used to rank these PCSAs. The final ranking of the PCSAs was determined using rank-average-sort and rank-sum-sort methods.ResultsThe random forest algorithm emerged as the top performer among the three tested ML algorithms, with an AUC-ROC of 0.89, compared to 0.83 for the other two, in distinguishing pathogenic driver mutations from benign passenger mutations using all 41 PCSAs. The top 11 PCSAs were selected based on the first quintile cut-off from the final rank-sum distribution. Classifiers built using these top 11 PCSAs (DEOGEN2, Integrated_fitCons, MVP, etc.) demonstrated significantly higher performance (p-value < 2.22e-16) compared to those using the remaining 30 PCSAs across all three ML algorithms, in separating pathogenic driver from benign passenger mutations. The top PCSAs demonstrated strong performance on a validation cohort including independent HNSC and other cancer types: breast, lung, and colorectal - reflecting its consistency, robustness and generalizability.ConclusionsThe ensemble machine learning approach effectively evaluates the performance of PCSAs based on their ability to differentiate pathogenic drivers from benign passenger mutations in HNSC and other cancer types. Notably, some well-known PCSAs performed poorly, underscoring the importance of data-driven selection over relying solely on popularity.

Whole-exome sequencing identifies novel mutation in intrahepatic cholestasis of pregnancy: A case report and literature review.

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disease characterized by pruritus and elevated total bile acid (TBA) levels. The most serious impact of ICP is sudden unexplained intrauterine fetal death, especially when an associated TBA ≥ 100 µmol/L is confirmed.We report a case of a 27-year-old female patient with early-onset severe refractory ICP. Whole-exome sequencing and mutation analyses were performed to obtain genetic data on the patient and her mother. Sanger sequencing was performed to screen the mutation site. Computer-based algorithms were applied to predict the pathogenesis of the identified mutation. Subsequently, we conducted a literature review to characterize the pathological features and perinatal management of severe refractory ICP, especially ICP with genetic susceptibility.A heterozygous mutation in the ABCD3 gene: c.130C > T/p.Pro44Ser was detected in this patient. Through the analysis of pathogenicity prediction software, the mutations were disease-causing. This is the first report to identify the novel p.Pro44Ser mutations of ABCD3 gene in ICP patients.Our report provides new insights into the genetic architecture of ICP involving ABCD3 variants. Early-onset severe refractory ICP is rare and mutations in bile acid metabolism genes might accentuate the phenotype. Emphasized perinatal management and screening for potential pathogenicity sites of variants that drive specific recognition of ICP is necessary.

Functional Characterization and In Silico Prediction Tools Improve the Pathogenicity Prediction of Novel Bile Acid Transporter Variants.

The pathogenicity of cholestatic liver diseases (CLDs) remains insufficiently characterized, hindering definitive diagnosis and timely treatment. The aim of this study was to improve the pathogenicity prediction of novel bile acid (BA) transporter variants in patients with CLDs. We analyzed the clinical characteristics and genetic profiles of a CLD cohort (n = 57) using multiple in silico tools and invitro functional assays. We identified 78 unique variants in four BA transporter genes. The predominant defects were associated with ABCC2 (57/78, 73.1%), with the most frequent being missense variants (39/78, 50.0%). Using in silico tools, we identified 47 novel variants: 12 mis-splicing, 21 deleterious missense, and 23 with altered protein stability. Of the 34 novel variants in ABCC2 identified through invitro functional assays, seven incurred aberrant splicing, 11 missense variants resulted in MRP2 reduction, 9 missense variants resulted in abnormal N-glycosylation, 18 variants altered MRP2 localization, and 26 variants reduced organic anion transport activity. These findings indicate that a multidisciplinary approach, integrating bioinformatics and experimental data, significantly enhances the accuracy of genetic-based CLD diagnosis. It serves as a foundational study for BA transport variants pathogenicity reclassification and expands the mutation spectrum of CLDs in China.

Functional study of three cases with novel TBX19 variants.

Congenital isolated adrenocorticotropic hormone deficiency (CIAD) is an autosomal recessive disorder. This study identifies novel TBX19 variants for CIAD patients, explores its possible effect mechanism at the structural, functional and protein levels, and guides clinicians better understand the condition. The clinical characteristics of three CIAD children were summarized. Multiple sequence alignment was performed and five algorithms, PROVEA, PolyPhen2, Mutation Taster, FATHMM, and I Mutant2.0, were used for the pathogenicity prediction. In addition, the three-dimensional protein structure of wild-type TBX19 was generated by Alphafold 3 and its variants were shown using PyMOL. Furthermore, immunoblotting analysis was applied to examine changes in the protein levels and the luciferase reporter assay was performed to further investigate the effects of TBX19 and its variants on pro-opiomelanocortin (POMC) transcriptional activity. We describe three Chinese patients with CIAD caused by TBX19 variants. The TBX19 variant, c.856C>T (p.R286*) was classified as pathogenic according to ACMG, whereas the other four variants, c.377C>T (p.P126L), c.602A>T (p.E201V), c.401A>G (p.H134R) and c.299G>A (p.R100H) were predicted to be disease-causing. Variants lead to alter interactions, conformational changes in proteins or truncate protein. TBX19 and PITX1 cooperated, resulting in a strong synergistic activation effect on POMC transcriptional expression. A functional study showed that the variants in our study result in a significant suppression of POMC transcriptional activity compared to wild-type TBX19. Our study identifies five TBX19 loss-of-function variants, two of which are novel and that provides new perspectives into the pathophysiological mechanism and expands the variant spectrum in IAD.

Deep learning–based assessment of missense variants in the COG4 gene presented with bilateral congenital cataract

ObjectiveWe compared the protein structure and pathogenicity of clinically relevant variants of the COG4 gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.Methods and analysisThe sequences...

Characterisation of EfbA from the Endodontic Pathogen Enterococcus faecalis and Prediction of Immunodominant EfbA Epitope Peptides: An In-vitro and In-silico Study

Introduction: Enterococcus faecalis (E. faecalis) belongs to Group D Streptococci and causes recalcitrant infections such as urinary tract infections, wound infections, intra-abdominal and pelvic infections, bacteraemia, and endocarditis. The emergence of multiple drug resistance, including to the last-resort drug vancomycin, is a major concern with E. faecalis infections. Therefore, it is necessary to implement alternative strategies to combat E. faecalis infections in dental settings. An immunoinformatics approach is one such strategy that can predict and assess the immunodominant B-cell and T-cell epitopes. Thus, there is a need to identify novel vaccine candidates as immunodominant epitope peptides from the Enterococcal Fibronectin Binding Protein-A (EfbA) protein for E. faecalis. Aim: To predict the immunodominant B-cell and T-cell epitopes from the EfbA protein of E. faecalis. Materials and Methods: An in-vitro and in-silico pilot study was conducted in the Department of Microbiology at Saveetha Dental College and Hospitals, Chennai, Tamil Nadu, India. Total of 20 carious scrapings were collected from patients with root caries and were phenotypically characterised for E. faecalis and the EfbA genetic determinant by Polymerase Chain Reaction (PCR) amplification. The EfbA protein was retrieved from the National Center for Biotechnology Information (NCBI) database, after which prediction of antigenicity and allergenicity was performed via the VaXiJen and Algpred servers, respectively. The prediction of T-cell epitopes was carried out with the help of the EpiDOCK server, and B-cell predictions were made using the Kolaskar and Tongaonkar tool. Docking of the epitopes with reference to Human Leukocyte Antigens (HLA) alleles was assessed using the ClusPro server, and the results were evaluated for the T-cell dominant epitopes. Results: Among the 20 samples, E. faecalis was characterised in 5 (25%) patients, with three strains exhibiting Multidrug Resistant (MDR) traits. Among the three MDR strains, two strains showed the presence of the EfbA gene by PCR. In-silico analysis of the EfbA protein yielded a total of 17 epitopes, and based on the assessments, a final selection of two epitopes (IRAKGKNHK and LLLSAHPSY) was made, showing promising docking scores with HLA alleles and TLR2. Conclusion: Among the two epitopes, LLLSAHPSY was identified as the most significant immune-dominant epitope predicted, which needs to be further evaluated for vaccine synthesis and experimentation.

Exploiting O-GlcNAc dyshomeostasis to screen O-GlcNAc transferase intellectual disability variants.

O-GlcNAcylation is an essential protein modification catalyzed by O-GlcNAc transferase (OGT). Missense variants in OGT are linked to a novel intellectual disability syndrome known as OGT congenital disorder of glycosylation (OGT-CDG). The mechanisms by which OGT missense variants lead to this heterogeneous syndrome are not understood, and no unified method exists for dissecting pathogenic from non-pathogenic variants. Here, we develop a double-fluorescence strategy in mouse embryonic stem cells to measure disruption of O-GlcNAc homeostasis by quantifying the effects of variants on endogenous OGT expression. OGT-CDG variants generally elicited a lower feedback response than wild-type and Genome Aggregation Database (gnomAD) OGT variants. This approach was then used to dissect new putative OGT-CDG variants from pathogenic background variants in other disease-associated genes. Our work enables the prediction of pathogenicity for rapidly emerging de novo OGT-CDG variants and points to reduced disruption of O-GlcNAc homeostasis as a common mechanism underpinning OGT-CDG.

Targeted Next-Generation Sequencing in Rare Diseases.

Targeted next-generation sequencing (NGS) in rare disease focuses on genetic analysis of specific regions in genome that are linked to a rare disease. In addition to library preparation, sequencing, and data analysis, targeted NGS includes an additional step of target enrichment of selected genes and regions. It allows for more sensitive and profound sequencing, as it is a fast and cost-effective approach with less data burden and is therefore often a method of choice for identifying rare variants in known genes, especially in diagnostics of rare diseases. Several in silico tools address the pathogenicity predictions of rare variants of unknown significance (VUS) and can therefore facilitate clinical interpretation.

A TSC2 recurrent variant c.5126C>T in a Han-Chinese family with tuberous sclerosis complex.

To identify the disease-causing variant in a family with tuberous sclerosis complex (TSC). This study including a Han-Chinese pedigree recruited from the Third Xiangya Hospital, Central South University, Changsha, Hunan, China was conducted between February, 2019 and January, 2023. Detailed clinical examinations were performed on the proband and other family members of a Han-Chinese family with TSC. Whole exome sequencing of the proband and Sanger sequencing of all family members were performed, followed by variant pathogenicity prediction and conservation analysis. SWISS-MODEL and PyMOL software were used for protein modelling and creating the three-dimensional structure model illustration of the critical GTPase-activating protein (GAP) domain. The variant was classified following the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. The female proband exhibited typical features of TSC, including hypomelanotic macules, angiofibromas, shagreen patches, seizures, brain lesions, cognitive impairment, renal abnormalities, and cardiovascular abnormalities. A recurrent c.5126C>T variant in the TSC complex subunit 2 gene (TSC2) was identified as the genetic cause of TSC in this family, classified as "pathogenic" according to ACMG standards and guidelines. The c.5126C>T variant leads to an amino acid change from proline to leucine at position 1709 (p.P1709L) in the functional GAP domain of tuberin protein, which may impair tumor growth inhibition of the hamartin-tuberin complex. This study reported a Han-Chinese TSC patient with a recurrent variant TSC2 c.5126C>T (p.P1709L). These findings broaden the phenotypic spectrum of TSC caused by this variant and may contribute to improving TSC genetic diagnoses as well as understanding of its mechanisms.

A novel de novo GABRA2 gene missense variant causing developmental epileptic encephalopathy in a Chinese patient.

Variants in the GABRA2 gene, which encodes the α2 subunit of the γ-aminobutyric acid A receptor, have been linked to a rare form of developmental and epileptic encephalopathy (DEE) referred to as DEE78. Only eight patients have been reported globally. This study presents the clinical presentation and genetic analysis of a Chinese family with a child diagnosed with DEE78, due to a novel GABRA2 variant. Genetic diagnosis was performed using trio-whole exome sequencing, followed by bioinformatics predictions of pathogenicity. Structural modeling assessed the potential impact of the variant. A mutant plasmid was constructed and transfected into 293 Tcells. Western blotting (WB) was used to evaluate mutant protein expression, while co-immunoprecipitation (Co-IP) analyzed interactions with GABRB3 and GABRG2 proteins. Immunofluorescence (IF) assessed the subcellular localization of the mutant protein. The 6-year-old male proband presented with seizures starting at age two, along with global developmental delay and hypotonia. Genetic testing revealed a heterozygous de novo variant in GABRA2 gene (NM_000807: c.923C>T, p.Ala308Val). Structural modeling suggested that this variant is located within the extracellular domain, which may disrupt hydrogen bonding interactions with GABRB3 and GABRG2. WB and Co-IP showed reduced protein expression and impaired interactions, potentially destabilizing the pentamer receptor complex. If analysis revealed that the variant did not affect subcellular localization. This study identified a novel likely pathogenic GABRA2 extracellular domain variant in a Chinese family causing the DEE phenotype. The results expand the genotypic and phenotypic spectrum of GABRA2-related DEE.

Widening the infantile hypotonia with psychomotor retardation and characteristic Facies-1 Syndrome's clinical and molecular spectrum through NALCN in-silico structural analysis.

Infantile hypotonia with psychomotor retardation and characteristic facies-1 (IHPRF1, MIM#615419) is a rare, birth onset, autosomal recessive disorder caused by homozygous or compound heterozygous truncating variants in NALCN gene (MIM#611549) resulting in a loss-of-function effect. We enrolled a new IHPRF1 patients' cohort in the framework of an international multicentric collaboration study. Using specialized in silico pathogenicity predictors and ad hoc structural analyses, we assessed the mechanistic consequences of the deleterious variants retrieved on NALCN structure and function. To date 38 different NALCN variants have been retrieved from 33 different families, 26 from unrelated and 22 from related patients. We report on five new IHPRF1 patients from four different families, harboring four newly identified and one previously retrieved variant that exhibited a markedly significant functional impact, thereby compromising the functionality of the protein complex. By widening the functional spectrum of biallelic variants affecting the NALCN gene, this article broadens the IHPRF1 syndrome's genotype-phenotype correlation and gives new insight into its pathogenic mechanism, diagnosis, and clinical management.

Comprehensive evaluation of AlphaMissense predictions by evidence quantification for variants of uncertain significance

Accurate variant classification is critical for genetic diagnosis. Variants without clear classification, known as “variants of uncertain significance” (VUS), pose a significant diagnostic challenge. This study examines AlphaMissense performance in variant classification, specifically for VUS. A systematic comparison between AlphaMissense predictions and predictions based on curated evidence according to the ACMG/AMP classification guidelines was conducted for 5845 missense variants in 59 genes associated with representative Mendelian disorders. A framework for quantifying and modeling VUS pathogenicity was used to facilitate comparison. Manual reviewing classified 5845 variants as 4085 VUS, 1576 pathogenic/likely pathogenic, and 184 benign/likely benign. Pathogenicity predictions based on AlphaMissense and ACMG guidelines were concordant for 1887 variants (1352 pathogenic, 132 benign, and 403 VUS/ambiguous). The sensitivity and specificity of AlphaMissense predictions for pathogenicity were 92% and 78%. Moreover, the quantification of VUS evidence and heatmaps weakly correlated with the AlphaMissense score. For VUS without computational evidence, incorporating AlphaMissense changed the VUS quantification for 878 variants, while 56 were reclassified as likely pathogenic. When AlphaMissense replaced existing computational evidence for all VUS, 1709 variants changed quantified criteria while 63 were reclassified as likely pathogenic. Our research suggests that the augmentation of AlphaMissense with empirical evidence may improve performance by incorporating a quantitative framework to aid in VUS classification.

Variant pathogenicity prediction based on the ESGMM algorithm

Modeling the functional impact of sequence variation is a critical issue for both understanding and developing proteins. An Evolutionary Sequence and Gaussian Mixture Model (ESGMM) for predicting variant pathogenicity is presented in this paper. The model is trained on 2715 clinical proteins and their homologous sequences, using a Transformer-based protein language model to discover evolutionary patterns of amino acids from multiple sequence alignment (MSA). To fully mine deep information of MSA two-dimensional data, an axial attention mechanism is introduced during training. The model estimates the probability of all variants compared to the wild type and calculates variant scores. To categorize variations as pathogenic or benign, a global–local Gaussian mixture model is then constructed for each variant, and ESGMM scores are produced for each variant employing a combination of global and local information. Particle swarm optimization (PSO) is introduced to optimize the local Gaussian mixture model and further quantify the uncertainty of the classification, which enhances the model prediction precision. Experimental results demonstrate the superiority of the optimized ESGMM algorithm in predicting the pathogenicity of variants.

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

In silico prediction of pathogen's pandemic potential using the viral trait assessment for pandemics (ViTAP) model.

Our world is ever evolving and interconnected, creating constant opportunities for disease outbreaks and pandemics to occur, making pandemic preparedness and pathogen management crucial for global health security. Early pathogen identification and intervention play a key role in mitigating the impacts of disease outbreaks. In this perspective, we present the Viral Trait Assessment for Pandemics (ViTAP) model to aid in the early identification of high-risk viruses that have pandemic potential, which incorporates lessons from past pandemics, including which key viral characteristics are important such as genetic makeup, transmission modes, mutation rates, and symptom severity. This model serves as the foundation for the development of powerful, quantitative tools for the early prediction of pandemic pathogens. The use of such a tool, in conjunction with other pandemic preparedness measures, can allow for early intervention and containment of the virus. This proactive approach could enable timely interventions, guiding public health responses, and resource allocation to prevent widespread outbreaks and mitigate the impact of emerging pathogens.