Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

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.

Membrane anchorage-induced (MAGIC) knock-down of non-synonymous point mutations

The promise of personalized medicine for monogenic and complex polygenic diseases depends on the availability of strategies for targeted inhibition of disease-associated polymorphic protein variants. A large majority of disease-causing genetic alterations are non-synonymous single nucleotide genetic variations (nsSNVs). Yet a general strategy for inhibiting the expression of nsSNVs without editing the human genome is currently lacking. Here, we reveal that upon intracellular delivery of lipid conjugated point mutation-specific monoclonal antibodies, a target-specific knockdown of gene expression at both mRNA and protein levels is observed. By harnessing the phenomenon of m embrane a nchorage i ndu c ed (MAGIC) knock-down of epitope-containing protein targets, we reveal a novel approach for inhibiting the expression of amino acid-altering point mutations. This approach opens up a new opportunity for the therapeutic inhibition of undruggable protein variants as well as paves the way for interrogating the nsSNVs in the human genome.

Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization

Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleterious and benign missense variants by means of a large number of features, including structure, sequence, interaction networks, gene disease associations as well as phenotypes. However, development of a reliable and accurate algorithm for merging heterogeneous information is highly needed as it could be captured all information of complex interactions on network that genes participate in. In this study we proposed a new method based on the non-negative matrix tri-factorization clustering method. We outlined two versions of the proposed method: two-source and three-source algorithms. Two-source algorithm aggregates individual deleteriousness prediction methods and PPI network, and three-source algorithm incorporates gene disease associations into the other sources already mentioned. Four benchmark datasets were employed for internally and externally validation of both algorithms of our predictor. The results at all datasets confirmed that, our method outperforms most state of the art variant prediction tools. Two key features of our variant effect prediction method are worth mentioning. Firstly, despite the fact that the incorporation of gene disease information at three-source algorithm can improve prediction performance by comparison with two-source algorithm, our method did not hinder by type 2 circularity error unlike some recent ensemble-based prediction methods. Type 2 circularity error occurs when the predictor annotates variants on the basis of the genes located on. Secondly, the performance of our predictor is superior over other ensemble-based methods for variants positioned on genes in which we do not have enough information about their pathogenicity.

Molecular and in vivo phenotyping of missense variants of the human glucagon receptor

Naturally occurring missense variants of G protein–coupled receptors with loss of function have been linked to metabolic disease in case studies and in animal experiments. The glucagon receptor, one such G protein–coupled receptor, is involved in maintaining blood glucose and amino acid homeostasis; however, loss-of-function mutations of this receptor have not been systematically characterized. Here, we observed fewer glucagon receptor missense variants than expected, as well as lower allele diversity and fewer variants with trait associations as compared with other class B1 receptors. We performed molecular pharmacological phenotyping of 38 missense variants located in the receptor extracellular domain, at the glucagon interface, or with previously suggested clinical implications. These variants were characterized in terms of cAMP accumulation to assess glucagon-induced Gα_s coupling, and of recruitment of β-arrestin-1/2. Fifteen variants were impaired in at least one of these downstream functions, with six variants affected in both cAMP accumulation and β-arrestin-1/2 recruitment. For the eight variants with decreased Gα_s signaling (D63^ECDN, P86^ECDS, V96^ECDE, G125^ECDC, R225^3.30H, R308^5.40W, V368^6.59M, and R378^7.35C) binding experiments revealed preserved glucagon affinity, although with significantly reduced binding capacity. Finally, using the UK Biobank, we found that variants with wildtype-like Gα_s signaling did not associate with metabolic phenotypes, whereas carriers of cAMP accumulation-impairing variants displayed a tendency toward increased risk of obesity and increased body mass and blood pressure. These observations are in line with the essential role of the glucagon system in metabolism and support that Gα_s is the main signaling pathway effecting the physiological roles of the glucagon receptor.

Loss of Function of Mutant IDS Due to Endoplasmic Reticulum-Associated Degradation: New Therapeutic Opportunities for Mucopolysaccharidosis Type II

Mucopolysaccharidosis type II (MPS II) results from the dysfunction of a lysosomal enzyme, iduronate-2-sulfatase (IDS). Dysfunction of IDS triggers the lysosomal accumulation of its substrates, glycosaminoglycans, leading to mental retardation and systemic symptoms including skeletal deformities and valvular heart disease. Most patients with severe types of MPS II die before the age of 20. The administration of recombinant IDS and transplantation of hematopoietic stem cells are performed as therapies for MPS II. However, these therapies either cannot improve functions of the central nervous system or cause severe side effects, respectively. To date, 729 pathogenetic variants in the IDS gene have been reported. Most of these potentially cause misfolding of the encoded IDS protein. The misfolded IDS mutants accumulate in the endoplasmic reticulum (ER), followed by degradation via ER-associated degradation (ERAD). Inhibition of the ERAD pathway or refolding of IDS mutants by a molecular chaperone enables recovery of the lysosomal localization and enzyme activity of IDS mutants. In this review, we explain the IDS structure and mechanism of activation, and current findings about the mechanism of degradation-dependent loss of function caused by pathogenetic IDS mutation. We also provide a potential therapeutic approach for MPS II based on this loss-of-function mechanism.

The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome

Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.

A Fifth of the Protein World: Rossmann-like Proteins as an Evolutionarily Successful Structural unit

The Rossmann-like fold is the most prevalent and diversified doubly-wound superfold of ancient evolutionary origin. Rossmann-like domains are present in a variety of metabolic enzymes and are capable of binding diverse ligands. Discerning evolutionary relationships among these domains is challenging because of their diverse functions and ancient origin. We defined a minimal Rossmann-like structural motif (RLM), identified RLM-containing domains among known 3D structures (20%) and classified them according to their homologous relationships. New classifications were incorporated into our Evolutionary Classification of protein Domains (ECOD) database. We defined 156 homology groups (H-groups), which were further clustered into 123 possible homology groups (X-groups). Our analysis revealed that RLM-containing proteins constitute approximately 15% of the human proteome. We found that disease-causing mutations are more frequent within RLM domains than within non-RLM domains of these proteins, highlighting the importance of RLM-containing proteins for human health.