Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Proteome-scale characterisation of motif-based interactome rewiring by disease mutations

Whole genome and exome sequencing are reporting on hundreds of thousands of missense mutations. Taking a pan-disease approach, we explored how mutations in intrinsically disordered regions (IDRs) break or generate protein interactions mediated by short linear motifs. We created a peptide-phage display library tiling ~57,000 peptides from the IDRs of the human proteome overlapping 12,301 single nucleotide variants associated with diverse phenotypes including cancer, metabolic diseases and neurological diseases. By screening 80 human proteins, we identified 366 mutation-modulated interactions, with half of the mutations diminishing binding, and half enhancing binding or creating novel interaction interfaces. The effects of the mutations were confirmed by affinity measurements. In cellular assays, the effects of motif-disruptive mutations were validated, including loss of a nuclear localisation signal in the cell division control protein CDC45 by a mutation associated with Meier-Gorlin syndrome. The study provides insights into how disease-associated mutations may perturb and rewire the motif-based interactome.

MediatorWeb: a protein-protein interaction network database for the RNA polymerase II Mediator complex

The protein-protein interaction (PPI) network of the Mediator complex is very tightly regulated and depends on different developmental and environmental cues. Here, we present an interactive platform for comparative analysis of the Mediator subunits from humans, baker's yeast Saccharomyces cerevisiae, and model plant Arabidopsis thaliana in a user-friendly web-interface database called MediatorWeb. MediatorWeb provides an interface to visualize and analyze the PPI network of Mediator subunits. The database facilitates downloading the untargeted and unweighted network of Mediator complex, its submodules, and individual Mediator subunits to better visualize the importance of individual Mediator subunits or their submodules. Further, MediatorWeb offers network visualization of the Mediator complex and interacting proteins that are functionally annotated. This feature provides clues to understand functions of Mediator subunits in different processes. In an additional tab, MediatorWeb provides quick access to secondary and tertiary structures, as well as residue-level contact information for Mediator subunits in each of the three model organisms. Another useful feature of MediatorWeb is detection of interologs based on orthologous analyses, which can provide clues to understand the functions of Mediator complex in less explored kingdoms. Thus, MediatorWeb and its features can help the user to understand the role of Mediator complex and its subunits in the transcription regulation of gene expression.

Single-cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells

Zika virus (ZIKV) is a mosquito-borne flavivirus that caused an epidemic in the Americas in 2016 and is linked to severe neonatal birth defects, including microcephaly and spontaneous abortion. To better understand the host response to ZIKV infection, we adapted the 10× Genomics Chromium single-cell RNA sequencing (scRNA-seq) assay to simultaneously capture viral RNA and host mRNA. Using this assay, we profiled the antiviral landscape in a population of human monocyte-derived dendritic cells infected with ZIKV at the single-cell level. The bystander cells, which lacked detectable viral RNA, expressed an antiviral state that was enriched for genes coinciding predominantly with a type I interferon (IFN) response. Within the infected cells, viral RNA negatively correlated with type I IFN-dependent and -independent genes (the antiviral module). We modeled the ZIKV-specific antiviral state at the protein level, leveraging experimentally derived protein interaction data. We identified a highly interconnected network between the antiviral module and other host proteins. In this work, we propose a new paradigm for evaluating the antiviral response to a specific virus, combining an unbiased list of genes that highly correlate with viral RNA on a per-cell basis with experimental protein interaction data.

IMPORTANCE

Zika virus (ZIKV) remains a public health threat given its potential for re-emergence and the detrimental fetal outcomes associated with infection during pregnancy. Understanding the dynamics between ZIKV and its host is critical to understanding ZIKV pathogenesis. Through ZIKV-inclusive single-cell RNA sequencing (scRNA-seq), we demonstrate on the single-cell level the dynamic interplay between ZIKV and the host: the transcriptional program that restricts viral infection and ZIKV-mediated inhibition of that response. Our ZIKV-inclusive scRNA-seq assay will serve as a useful tool for gaining greater insight into the host response to ZIKV and can be applied more broadly to the flavivirus field.

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function 1 . As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB) 2 , the most common embryonal brain tumor in children, and pineoblastoma 3 . These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST 4 . However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. Sliding Window Interaction Grammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy

Revealing the molecular effect that pathogenic missense mutations have on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast targeted drug development at a low cost. Here, we report an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG, but do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp, and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

The Ubiquitin-Associated and SH3 Domain-Containing Proteins (UBASH3) Family in Mammalian Development and Immune Response

UBASH3A and UBASH3B are protein families of atypical protein tyrosine phosphatases that function as regulators of various cellular processes during mammalian development. As UBASH3A has only mild phosphatase activity, its regulatory effects are based on the phosphatase-independent mechanisms. On the contrary, UBASH3B has strong phosphatase activity, and the suppression of its receptor signalling is mediated by Syk and Zap-70 kinases. The regulatory functions of UBASH3A and UBASH3B are particularly evident in the lymphoid tissues and kidney development. These tyrosine phosphatases are also known to play key roles in autoimmunity and neoplasms. However, their involvement in mammalian development and its regulatory functions are largely unknown and are discussed in this review.

Single cell analysis reveals an antiviral network that controls Zika virus infection in human dendritic cells

Zika virus (ZIKV) is a mosquito-borne flavivirus that caused an epidemic in the Americas in 2016 and is linked to severe neonatal birth defects, including microcephaly and spontaneous abortion. To better understand the host response to ZIKV infection, we adapted the 10x Genomics Chromium single cell RNA sequencing (scRNA-seq) assay to simultaneously capture viral RNA and host mRNA. Using this assay, we profiled the antiviral landscape in a population of human moDCs infected with ZIKV at the single cell level. The bystander cells, which lacked detectable viral RNA, expressed an antiviral state that was enriched for genes coinciding predominantly with a type I interferon (IFN) response. Within the infected cells, viral RNA negatively correlated with type I IFN dependent and independent genes (antiviral module). We modeled the ZIKV specific antiviral state at the protein level leveraging experimentally derived protein-interaction data. We identified a highly interconnected network between the antiviral module and other host proteins. In this work, we propose a new paradigm for evaluating the antiviral response to a specific virus, combining an unbiased list of genes that highly correlate with viral RNA on a per cell basis with experimental protein interaction data. Our ZIKV-inclusive scRNA-seq assay will serve as a useful tool to gaining greater insight into the host response to ZIKV and can be applied more broadly to the flavivirus field.

Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Unraveling Extremely Damaging IRAK4 Variants and Their Potential Implications for IRAK4 Inhibitor Efficacy

Interleukin-1-receptor-associated kinase 4 (IRAK4) possesses a crucial function in the toll-like receptor (TLR) signaling pathway, and the dysfunction of this molecule could lead to various infectious and immune-related diseases in addition to cancers. IRAK4 genetic variants have been linked to various types of diseases. Therefore, we conducted a comprehensive analysis to recognize the missense variants with the most damaging impacts on IRAK4 with the employment of diverse bioinformatics tools to study single-nucleotide polymorphisms' effects on function, stability, secondary structures, and 3D structure. The residues' location on the protein domain and their conservation status were investigated as well. Moreover, docking tools along with structural biology were engaged in analyzing the SNPs' effects on one of the developed IRAK4 inhibitors. By analyzing IRAK4 gene SNPs, the analysis distinguished ten variants as the most detrimental missense variants. All variants were situated in highly conserved positions on an important protein domain. L318S and L318F mutations were linked to changes in IRAK4 secondary structures. Eight SNPs were revealed to have a decreasing effect on the stability of IRAK4 via both I-Mutant 2.0 and Mu-Pro tools, while Mu-Pro tool identified a decreasing effect for the G198E SNP. In addition, detrimental effects on the 3D structure of IRAK4 were also discovered for the selected variants. Molecular modeling studies highlighted the detrimental impact of these identified SNP mutant residues on the druggability of the IRAK4 ATP-binding site towards the known target inhibitor, HG-12-6, as compared to the native protein. The loss of important ligand residue-wise contacts, altered protein global flexibility, increased steric clashes, and even electronic penalties at the ligand-binding site interfaces were all suggested to be associated with SNP models for hampering the HG-12-6 affinity towards IRAK4 target protein. This given model lays the foundation for the better prediction of various disorders relevant to IRAK4 malfunction and sheds light on the impact of deleterious IRAK4 variants on IRAK4 inhibitor efficacy.

Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties

DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.

Association of NOTCH4 and ACHE gene polymorphism in Alzheimer's disease of Gujarat cohort

BACKGROUND

Alzheimer's Disease (AD) is the most common form of dementia, affecting cognitive and behavioral functions. AD is a complex disease resulting from the modest effect of gene interaction and environmental factors, as a result of which the exact pathogenesis is still unknown.

AIM

The aim of the present study was to investigate the association between variants of 98 targeted genes with Alzheimer's disease phenotype.

METHOD

A total of 98 genes from 32 AD cases and 11 controls were genotyped using the Haloplex target enrichment method and the PCR-RFLP approach.Association analysis was performed using the PLINK tool to identify the variant significantly associated with AD. Functional enrichment analysis and network analysis was performed using ClueGo and String database respectively. The Expression Quantitative Trait Loci (eQTL) analysis using the Genotype Tissue Expression (GTEx) dataset to explore the possible implication of the variant on the expression of one or more genes in different brain regions and whole blood.

RESULT

Association analysis showed significant association of 19 variant assigned to 16 genes with Alzheimer's with p-value < 0.05 with rs367398/NOTCH4 only variant that passed multiple test corrections. Functional enrichment analysis showed association of these genes with AD. ClueGo and network analysis utilizing the String database suggested that genes are directly and indirectly linked to the AD pathogenesis. eQTL analysis revealed that the rs367398/NOTCH4 and rs1799806/ACHE variant showed significant eQTL for the neighbouring genes.

CONCLUSION

The present study showed the possible role of 16 genes in AD pathogenesis, especially highlighting the role of rs367398/NOTCH4 and rs1799806/ACHE. However further investigation with large cohort is required to study and validate the implication of these variants in the AD pathogenesis.

Pervasive mislocalization of pathogenic coding variants underlying human disorders

Widespread sequencing has yielded thousands of missense variants predicted or confirmed as disease-causing. This creates a new bottleneck: determining the functional impact of each variant - largely a painstaking, customized process undertaken one or a few genes or variants at a time. Here, we established a high-throughput imaging platform to assay the impact of coding variation on protein localization, evaluating 3,547 missense variants of over 1,000 genes and phenotypes. We discovered that mislocalization is a common consequence of coding variation, affecting about one-sixth of all pathogenic missense variants, all cellular compartments, and recessive and dominant disorders alike. Mislocalization is primarily driven by effects on protein stability and membrane insertion rather than disruptions of trafficking signals or specific interactions. Furthermore, mislocalization patterns help explain pleiotropy and disease severity and provide insights on variants of unknown significance. Our publicly available resource will likely accelerate the understanding of coding variation in human diseases.

In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes

Infertility is a heterogeneous condition, with genetic causes thought to underlie a substantial fraction of cases. Genome sequencing is becoming increasingly important for genetic diagnosis of diseases including idiopathic infertility; however, most rare or minor alleles identified in patients are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling. To determine the consequences of these variants in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXO1, FKBP6, MCM9, M1AP, MEI1, MSH4 and SEPT12 by generating genome-edited mouse models. Nine variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction (PPI) in the yeast two hybrid (Y2H) assay. Though these genes are essential for normal meiosis or spermiogenesis in mice, only one variant, observed in the MCM9 gene of a male infertility patient, compromised fertility or gametogenesis in the mouse models. To explore the disconnect between predictions and outcomes, we compared pathogenicity calls of missense variants made by ten widely used algorithms to 1) those annotated in ClinVar and 2) those evaluated in mice. All the algorithms performed poorly in terms of predicting the effects of human missense variants modeled in mice. These studies emphasize caution in the genetic diagnoses of infertile patients based primarily on pathogenicity prediction algorithms and emphasize the need for alternative and efficient in vitro or in vivo functional validation models for more effective and accurate VUS description to either pathogenic or benign categories.

Shaping Nanobodies and Intrabodies against Proteoforms

Proteoforms expand genomic diversity and direct developmental processes. While high-resolution mass spectrometry has accelerated characterization of proteoforms, molecular techniques working to bind and disrupt the function of specific proteoforms have lagged behind. In this study, we worked to develop intrabodies capable of binding specific proteoforms. We employed a synthetic camelid nanobody library expressed in yeast to identify nanobody binders of different SARS-CoV-2 receptor binding domain (RBD) proteoforms. Importantly, employment of the positive and negative selection mechanisms inherent to the synthetic system allowed for amplification of nanobody-expressing yeast that bind to the original (Wuhan strain RBD) but not the E484 K (Beta variant) mutation. Nanobodies raised against specific RBD proteoforms were validated by yeast-2-hybrid analysis and sequence comparisons. These results provide a framework for development of nanobodies and intrabodies that target proteoforms.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Genetic Determinants of Cardiovascular Disease: The Endothelial Nitric Oxide Synthase 3 (eNOS3), Krüppel-Like Factor-14 (KLF-14), Methylenetetrahydrofolate Reductase (MTHFR), MiRNAs27a and Their Association with the Predisposition and Susceptibility to Coronary Artery Disease

Coronary artery disease (CAD) is an important cause of death worldwide. CAD is caused by genetic and other factors including hypertension, hyperlipidemia, obesity, stress, unhealthy diet, physical inactively, smoking and Type 2 diabetes (T2D). The genome wide association studies (GWASs) have revealed the association of many loci with risk to diseases such as cancers, T2D and CAD. Nitric oxide (NO) is a potent vasodilator and is required for normal vascular health. It is produced in the endothelial cells in a reaction catalyzed by the endothelial NO synthase (eNOS). Methylenetetrahydrofolate reductase (MTHFR) is a very important enzyme involved in metabolism of folate and homocysteine, and its reduced function leads to cardiovascular disease. The Krüppel-like factor-14 (KLF-14) is an important transcriptional regulator that has been implicated in metabolic syndrome. MicroRNA (MiRNAs) are short non-coding RNAs that regulate the gene expression of proteins involved in important physiological processes including cell cycle and metabolism. In the present study, we have investigated the potential impact of germline pathogenic variants of endothelial eNOS, KLF-14, MTHFR, MiRNA-27a and their association with risk to CAD in the Saudi population. Methods: Amplification Refractory Mutation System (ARMS) PCR was used to detect MTHFR, KLF-14, miRNA-27a and eNOS3 genotyping in CAD patients and healthy controls. About 125 CAD cases and 125 controls were enrolled in this study and statistical associations were calculated including p-value, risk ratio (RR), and odds ratio (OD). Results: There were statistically significant differences (p < 0.05) in genotype distributions of MTHFR 677 C>T, KLF-14 rs972283 G>A, miRNAs27a rs895819 A>G and eNOS3 rs1799983 G>T between CAD patients and controls. In addition, our results indicated that the MTHFR-TT genotype was associated with increased CAD susceptibility with an OR 2.75 (95%) and p < 0.049, and the KLF14-AA genotype was also associated with increased CAD susceptibility with an OR of 2.24 (95%) and p < 0.024. Moreover, the miRNAs27a-GG genotype protects from CAD risk with an OR = 0.31 (0.016), p = 0.016. Our results also indicated that eNOS3 -GT genotype is associated with CAD susceptibility with an OR = 2.65, and p < 0.0003. Conclusion: The MTHFR 677C>T, KLF14 rs972283 G>A, miRNAs27a A>G, and eNOS3 rs1799983 G>T genotypes were associated with CAD susceptibility (p < 0.05). These findings require verification in future large-scale population based studies before these loci are used for the prediction and identification of individuals at risk to CAD. Weight control, physical activity, and smoking cessation are very influential recommendations given by clinicians to the at risk individuals to reduce or delay the development of CAD.

A comprehensive SARS-CoV-2-human protein-protein interactome network identifies pathobiology and host-targeting therapies for COVID-19

Physical interactions between viral and host proteins are responsible for almost all aspects of the viral life cycle and the host's immune response. Studying viral-host protein-protein interactions is thus crucial for identifying strategies for treatment and prevention of viral infection. Here, we use high-throughput yeast two-hybrid and affinity purification followed by mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of both binary and co-complex interactions. We report a total of 739 high-confidence interactions, showing the highest overlap of interaction partners among published datasets as well as the highest overlap with genes differentially expressed in samples (such as upper airway and bronchial epithelial cells) from patients with SARS-CoV-2 infection. Showcasing the utility of our network, we describe a novel interaction between the viral accessory protein ORF3a and the host zinc finger transcription factor ZNF579 to illustrate a SARS-CoV-2 factor mediating a direct impact on host transcription. Leveraging our interactome, we performed network-based drug screens for over 2,900 FDA-approved/investigational drugs and obtained a curated list of 23 drugs that had significant network proximities to SARS-CoV-2 host factors, one of which, carvedilol, showed promising antiviral properties. We performed electronic health record-based validation using two independent large-scale, longitudinal COVID-19 patient databases and found that carvedilol usage was associated with a significantly lowered probability (17%-20%, P < 0.001) of obtaining a SARS-CoV-2 positive test after adjusting various confounding factors. Carvedilol additionally showed anti-viral activity against SARS-CoV-2 in a human lung epithelial cell line [half maximal effective concentration (EC ₅₀ ) value of 4.1 µM], suggesting a mechanism for its beneficial effect in COVID-19. Our study demonstrates the value of large-scale network systems biology approaches for extracting biological insight from complex biological processes.

Non-adaptive complexity and biochemical function

Intricate biochemical structures are usually thought to be useful, because natural selection preserves them from degradation by a constant hail of destructive mutations. Biochemists therefore often deliberately disrupt them to understand how complexity improves protein function or fitness. However, evolutionary theory suggests that even useless complexity that never improved fitness can become completely essential if a simple set of evolutionary conditions is fulfilled. We review evidence that stable protein complexes, protein-chaperone interactions, and complexes consisting of several paralogs all fulfill these conditions. This makes reverse genetics or destructive mutagenesis unsuitable for assigning functions to these kinds of complexity. Instead, we advocate that incorporating evolutionary approaches into biochemistry overcomes this difficulty and allows us to distinguish useless from useful biochemical complexity.

Implications of disease-related mutations at protein-protein interfaces

Protein-protein interfaces have been attracting great attention owing to their critical roles in protein-protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein-protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.

Decoding the cellular effects of genetic variation through interaction proteomics

It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

A computational exploration of resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

Effect of Lithium Drug on Binding Affinities of Glycogen Synthase Kinase-3 β to Its Network Partners: A New Computational Approach

Finding new methods to study the effect of small molecules on protein interaction networks provides us with invaluable tools in the fields of pharmacodynamics and drug design. Lithium is an antimanic drug that has been used for the treatment of bipolar disorder for more than 60 years. Here, we utilized a new approach to study the effect of lithium as a drug on the protein interaction network of GSK-3β as a hub protein and computed the affinities of GSK-3β to its partners in the presence of lithium or sodium ions. For this purpose, ensembles of GSK-3β protein structures were created in the presence of either lithium or sodium ions using adaptive tempering molecular dynamics simulations. The protein binding patches of GSK-3β for its partners were determined, and finally, the affinity of each binding patch to the related partner was computed for structures of ensembles using a monomer-based approach. Besides, by comparing structural dynamics of GSK-3β during MD simulations in the presence of LiCl and NaCl, we suggested a new mechanism for the inhibitory effect of lithium on GSK-3β.

A panoramic view of proteomics and multiomics in precision health

Health is often qualitatively defined as a status free from disease and its quantitative definition requires finding the boundary separating health from pathological conditions. Since many complex diseases have a strong genetic component, substantial efforts have been made to sequence large-scale personal genomes; however, we are not yet able to effectively quantify health status from personal genomes. Since mutational impacts are ultimately manifested at the protein level, we envision that introducing a panoramic proteomic view of complex diseases will allow us to mechanistically understand the molecular etiologies of human diseases. In this perspective article, we will highlight key proteomic approaches to identify pathogenic mutations and map their convergent pathways underlying disease pathogenesis and the integration of omics data at multiple levels to define the borderline between health and disease.

Low concentration Tetrabromobisphenol A (TBBPA) elevating overall metabolism by inducing activation of the Ras signaling pathway

Tetrabromobisphenol A (TBBPA), one of the most common flame retardants, affects neurodevelopment, disrupts the endocrine system, and increases the possibility of tumorigenesis. This study investigates the cytotoxic effects, genetic effects, and metabolic effects from exposure to low concentration TBBPA. The cell exposure was measured by mimicking the residual TBBPA concentrations in human plasma, specifically in occupational populations. Our results revealed that long-term TBBPA exposure, especially at 1 nM concentration, significantly promoted the proliferation of HepG2 cells. Furthermore, long-term TBBPA exposure can double the levels of reactive oxygen species (ROS) released from mitochondria, thereby increasing Adenosine Monophosphate activated Protein kinase (AMPK) gene expression level to promote cellular proliferation. However, ROS can also mediate the apoptosis process through the mitochondrial membrane potential (MMP). The RNA-seq analysis confirmed that the Ras signaling pathway was activated by the growth factor to mediate cell detoxification mechanism, increasing lipid and vitamin metabolic rate. Our work uncovers a cellular mechanism by which long-term exposure to low concentration TBBPA can induce the activation of the Ras signaling pathway and demonstrates potential metabolic disorder in the human hepatic cells upon plasma TBBPA exposure.

Chemerin levels and its genetic variants are associated with Gestational Diabetes Mellitus: a hospital-based study in a Chinese cohort

BACKGROUND

Gestational diabetes mellitus (GDM) is a glucose intolerance condition encounters for the first time in a fraction of pregnant women. The role of different host inflammatory molecules in GDM etiology has been deciphered. Chemerin is a chemoattractant protein primarily associated with the pathogenesis of type 2 diabetes, obesity, and metabolic syndrome. However, the association of chemerin and its genetic variants with the predisposition of GDM is not clear, and our present study is aimed to address the issue.

MATERIALS AND METHODS

A total of 703 Chinese women comprising of GDM (n=303), glucose tolerant pregnant women (n=211), and non-pregnant glucose tolerant controls (n=189) were recruited in the present investigation. GDM was diagnosed according to the World Health Organization recommendation for diagnosis of gestational diabetes during pregnancy. Plasma levels of chemerin were quantified by an Enzyme-linked Immunosorbent Assay (ELISA). Common variants in the chemerin gene (rs4721, rs17173617, rs7806429, and rs17173608) were genotyped by using TaqMan assay.

RESULTS

Plasma chemerin level was found higher in subjects with GDM as compared to glucose tolerant pregnant and non-pregnant women. Further, a positive correlation between plasma chemerin and HOMA-IR index suggesting an essential role of chemerin in mediating insulin resistance. Variants of rs4721 and rs17173608 polymorphisms were associated with lower levels of plasma chemerin and low HOMA-IR index. Furthermore, mutants of rs4721 and rs17173608 polymorphisms were associated with protection against the development of GDM in the Chinese cohort.

CONCLUSIONS

Plasma chemerin is elevated in GDM patients. Genetic variation in chemerin gene associated with lower plasma levels of chemerin, HOMA-IR index and protects against the development of GDM in Chinese.

Exploring absent protein function in yeast: assaying post translational modification and human genetic variation

Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are absent. It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.

SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity

MOTIVATION

Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available.

RESULTS

Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37-0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein-protein interactions.

AVAILABILITY AND IMPLEMENTATION

SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Boosting Detection of Low-Abundance Proteins in Thermal Proteome Profiling Experiments by Addition of an Isobaric Trigger Channel to TMT Multiplexes

The study of low-abundance proteins is a challenge to discovery-based proteomics. Mass spectrometry (MS) applications, such as thermal proteome profiling (TPP), face specific challenges in the detection of the whole proteome as a consequence of the use of nondenaturing extraction buffers. TPP is a powerful method for the study of protein thermal stability, but quantitative accuracy is highly dependent on consistent detection. Therefore, TPP can be limited in its amenability to study low-abundance proteins that tend to have stochastic or poor detection by MS. To address this challenge, we incorporated an affinity-purified protein complex sample at submolar concentrations as an isobaric trigger channel into a mutant TPP (mTPP) workflow to provide reproducible detection and quantitation of the low-abundance subunits of the cleavage and polyadenylation factor (CPF) complex. The inclusion of an isobaric protein complex trigger channel increased detection an average of 40× for previously detected subunits and facilitated detection of CPF subunits that were previously below the limit of detection. Importantly, these gains in CPF detection did not cause large changes in melt temperature (T_m) calculations for other unrelated proteins in the samples, with a high positive correlation between T_m estimates in samples with and without isobaric trigger channel addition. Overall, the incorporation of an affinity-purified protein complex as an isobaric trigger channel within a tandem mass tag (TMT) multiplex for mTPP experiments is an effective and reproducible way to gather thermal profiling data on proteins that are not readily detected using the original TPP or mTPP protocols.

Genetic basis of mitochondrial diseases

Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

Strategies to Identify Genetic Variants Causing Infertility

Genetic causes are thought to underlie about half of infertility cases, but understanding the genetic bases has been a major challenge. Modern genomics tools allow more sophisticated exploration of genetic causes of infertility through population, family-based, and individual studies. Nevertheless, potential therapies based on genetic diagnostics will be limited until there is certainty regarding the causality of genetic variants identified in an individual. Genome modulation and editing technologies have revolutionized our ability to functionally test such variants, and also provide a potential means for clinical correction of infertility variants. This review addresses strategies being used to identify causative variants of infertility.

Mass spectrometry-based protein-protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.

Variants in RABL2A causing male infertility and ciliopathy

Approximately 7% of men worldwide suffer from infertility, with sperm abnormalities being the most common defect. Though genetic causes are thought to underlie a substantial fraction of idiopathic cases, the actual molecular bases are usually undetermined. Because the consequences of most genetic variants in populations are unknown, this complicates genetic diagnosis even after genome sequencing of patients. Some patients with ciliopathies, including primary ciliary dyskinesia and Bardet-Biedl syndrome, also suffer from infertility because cilia and sperm flagella share several characteristics. Here, we identified two deleterious alleles of RABL2A, a gene essential for normal function of cilia and flagella. Our in silico predictions and in vitro assays suggest that both alleles destabilize the protein. We constructed and analyzed mice homozygous for these two single-nucleotide polymorphisms, Rabl2L119F (rs80006029) and Rabl2V158F (rs200121688), and found that they exhibit ciliopathy-associated disorders including male infertility, early growth retardation, excessive weight gain in adulthood, heterotaxia, pre-axial polydactyly, neural tube defects and hydrocephalus. Our study provides a paradigm for triaging candidate infertility variants in the population for in vivo functional validation, using computational, in vitro and in vivo approaches.

The ssDNA-binding protein MEIOB acts as a dosage-sensitive regulator of meiotic recombination

Meiotic recombination enables reciprocal exchange of genetic information between parental chromosomes and is essential for fertility. MEIOB, a meiosis-specific ssDNA-binding protein, regulates early meiotic recombination. Here we report that the human infertility-associated missense mutation (N64I) in MEIOB causes protein degradation and reduced crossover formation in mouse testes. Although the MEIOB N64I substitution is associated with human infertility, the point mutant mice are fertile despite meiotic defects. Meiob mutagenesis identifies serine 67 as a critical residue for MEIOB. Biochemically, these two mutations (N64I and S67 deletion) cause self-aggregation of MEIOB and sharply reduced protein half-life. Molecular genetic analyses of both point mutants reveal an important role for MEIOB in crossover formation in late meiotic recombination. Furthermore, we find that the MEIOB protein levels directly correlate with the severity of meiotic defects. Our results demonstrate that MEIOB regulates meiotic recombination in a dosage-dependent manner.

Precision medicine - networks to the rescue

Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

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

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions

Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.