The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions

Rare genetic coding variants associated with age-related episodic memory decline implicate distinct memory pathologies in the hippocampus

Background

Approximately 40% of people aged 65 or older experience memory loss, particularly in episodic memory. Identifying the genetic basis of episodic memory decline is crucial for uncovering its underlying causes.

Methods

We investigated common and rare genetic variants associated with episodic memory decline in 742 (632 for rare variants) Ashkenazi Jewish individuals (mean age 75) from the LonGenity study. All-atom MD simulations were performed to uncover mechanistic insights underlying rare variants associated with episodic memory decline.

Results

In addition to the common polygenic risk of Alzheimer's Disease (AD), we identified and replicated rare variant association in ITSN1 and CRHR2 . Structural analyses revealed distinct memory pathologies mediated by interfacial rare coding variants such as impaired receptor activation of corticotropin releasing hormone and dysregulated L-serine synthesis.

Discussion

Our study uncovers novel risk loci for episodic memory decline. The identified underlying mechanisms point toward heterogeneous memory pathologies mediated by rare coding variants.

Truncated variants of thyroid hormone receptor beta display disease-inflicting malfunctioning at cellular level

Thyroid hormone receptor β (THRβ) is a member of the nuclear receptor superfamily of ligand-modulated transcription factors. Upon ligand binding, THRβ sequentially recruits the components of transcriptional machinery to modulate target gene expression. In addition to regulating diverse physiological processes, THRβ plays a crucial role in hypothalamus-pituitary-thyroid axis feedback regulation. Anomalies in THRβ gene/protein structure are associated with onset of diverse disease states. In this study, we investigated disease-inflicting truncated variants of THRβ using in-silico analysis and cell-based assays. We examined the THRβ truncated variants on multiple test parameters, including subcellular localization, ligand-receptor interactions, transcriptional functions, interaction with heterodimeric partner RXR, and receptor-chromatin interactions. Moreover, molecular dynamic simulation approaches predicted that shortened THRβ-LBD due to point mutations contributes proportionally to the loss of structural integrity and receptor stability. Deviant subcellular localization and compromised transcriptional function were apparent with these truncated variants. Present study shows that 'mitotic bookmarking' property of some THRβ variants is also affected. The study highlights that structural and conformational attributes of THRβ are necessary for normal receptor functioning, and any deviations may contribute to the underlying cause of the inflicted diseases. We anticipate that insights derived herein may contribute to improved mechanistic understanding to assess disease predisposition.

Genetic variation in the BLM gene and its expression in the ovaries is closely related to kidding number in goats

Bloom (BLM) helicase plays an important role in DNA replication and the maintenance of genome integrity. BLM protein deficiency, which plays a vital role in the sperm-egg union and germ-cell development during reproduction, can lead to severe DNA damage in goats. However, the effect of BLM protein deficiency on goat litter size has not been reported. Herein, we studied the association between the genetic variation in the BLM gene and the number of kids per litter in Guizhou white goats. We explored differences in the expression of the BLM protein in the follicles of single and multi-kid nanny goats. We also analyzed the effects of dysregulated BLM gene expression on the proliferation and apoptosis of ovarian granulosa cells and the expression of genes related to follicle development in goats. Five single nucleotide polymorphism (SNP) loci, including the non-synonymous mutations g.38179 A > G, g.40626 G > C and g.89621 T > G; the intron synonymous mutation g.56961 G > A and the exon synonymous mutation g.65796 C > T were found in the BLM gene. All SNPs loci were in Hardy-Weinberg equilibrium, and correlation analysis showed that the g.65796 C > T and g.89621 T > G loci polymorphism was strongly associated with litter size in the first three litters (P < 0.05). The diplogenotype Hap 2/2 (AAGGAACCTT) showed no significant difference in litter size between different births, indicating that the diploid genotype is stable in different litter sizes. Bioinformatics analysis showed that three non-synonymous mutation loci (p.T488A, p.A662S, and p.S1373A) could affect BLM protein stability, and mutations in p.T488A and p.S1373A led to changes in amino acid polarity and associated interactions. qPCR results showed that the expression level of the BLM gene in the uterus and ovaries of TT genotype nanny goats was significantly higher than that of GG genotype nanny goats. Indirect immunofluorescence assay (IF) showed that the BLM protein was significantly overexpressed in both the primordial and growing follicles of nanny goats with multiple kids (P < 0.01). Disrupting BLM gene expression in the ovarian granulosa cells down-regulated the expression of the Cyp19A1 gene. It also significantly inhibited the proliferation of follicles and induces early apoptosis of the granulosa cells. These findings confirm that polymorphism in the BLM gene is closely related to the littering traits of Guizhou white goats, and it affects the reproductive performance of nanny goats by regulating the development of the oocytes and granulosa cells. This work provides new evidence on the regulatory effect of the BLM gene on the litter size of nanny goats.

Role of toll-like receptor in the pathogenesis of oral cancer

Toll-like receptors are important molecules of innate immunity. They are known as pattern recognition receptors. They recognise certain molecules known as pathogen-associated molecular pattern on a pathogen and release chemicals that causes inflammation. Toll-like receptors (TLR) help in the removal of the infected cell and thus stop the spread of infection and are being studied for their association with cancer. Oral carcinoma has emerged as a major problem of our country today; it is found ranks first in men and third in women. Toll-like receptors have been implicated in the development of cancer. Certain polymorphisms in toll-like receptor can make a cell more susceptible to develop oral cancer. The identification of toll-like receptors and the different genotypes that are involved in the development of cancer can be utilised for using them as biomarkers of the disease. The study revealed that toll-like receptors like TLR7 and TLR5 are found to have a role in suppression of oral cancer while toll-like receptors like TLR4 and TLR2 are found to be associated with the progression of oral cancer. Toll-like receptors can turn out as important target molecules in the future in designing therapeutic strategies for oral cancer.

Molecular genetic variability of Cryphonectria hypovirus 1 associated with Cryphonectria parasitica in South Tyrol (northern Italy)

Cryphonectria hypovirus 1 (CHV-1) has been widely studied and used as a biocontrol agent because of its ability to infect the chestnut blight fungus, Cryphonectria parasitica, and to reduce its virulence. Knowledge about the hypovirus, its presence, and diversity is completely lacking in South Tyrol (northern Italy), which may obstruct biocontrol measures for chestnut blight based on CHV-1. This work aimed to study the occurrence of CHV-1 infecting C. parasitica in South Tyrol and to perform a genetic characterization of the hypovirus. In South Tyrol, CHV-1 was found to occur in 29.2% of the fungal isolates investigated, varying in frequency between different regions and chestnut stands. Twenty-three haplotypes based on partial cDNA (complementary DNA) sequences of open reading frame (ORF)-A and 30 haplotypes based on partial cDNA sequences of ORF-B were identified among 47 and 56 hypovirulent fungal isolates, respectively. Phylogenetic analysis showed that all the haplotypes belonged to the Italian subtype of CHV-1 and that they were closely related to the populations of Italy, Switzerland, Croatia and Slovenia. Evidence of recombination was not found in the sequences and point mutations were the main source of diversity. Overall, this study indicated that the prevalence of CHV-1 in South Tyrol is low compared to many other central and western European populations and determined a need to actively impose biocontrol measures. Using sequence analysis, we identified some variants of interest of CHV-1 that should be studied in detail for their potential use in biocontrol.

Investigation of pathogenic germline variants in gastric cancer and development of "GasCanBase" database

BACKGROUND

Gastric cancer, which is also known as stomach cancer, can be influenced by both germline and somatic mutations. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in germline have long been reported to play a pivotal role in cancer progression.

AIM

The aim of this study is to examine the nsSNP in GC-associated genes. The study also aims to develop a database with extensive information regarding the nsSNPs in the GC-associated genes and their impacts.

METHODS AND RESULTS

A total of 34,588 nsSNPs from 1,493,460 SNPs of the 40 genes were extracted from the available SNP database. Drug binding and energy minimization were examined by molecular docking and YASARA. To validate the existence of the germline CDH1 gene mutation (rs34466743) in the isolated blood DNA of gastric cancer (GC) patients, polymerase chain reaction (PCR) and DNA sequencing were performed. According to the results of the gene network analysis, 17 genes may interact with other types of cancer. A total of 11,363 nsSNPs were detected within the 40 GC genes. Among these, 474 nsSNPs were predicted to be damaging and 40 to be the most damaging. The SNPs in domain regions were thought to be strong candidates that alter protein functions. Our findings proposed that most of the selected nsSNPs were within the domains or motif regions. Free Energy Deviation calculation of protein structure pointed toward noteworthy changes in the structure of each protein that can demolish its natural function. Subsequently, drug binding confirmed the structural variation and the ineffectiveness of the drug against the mutant model in individuals with these germline variants. Furthermore, in vitro analysis of the rs34466743 germline variant from the CDH1 gene confirmed the strength and robustness of the pipeline that could expand the somatic alteration for causing cancer. In addition, a comprehensive gastric cancer polymorphism database named "GasCanBase" was developed to make data available to researchers.

CONCLUSION

The findings of this study and the "GasCanBase" database may greatly contribute to our understanding of molecular epidemiology and the development of precise therapeutics for gastric cancer. GasCanBase is available at: https://www.gascanbase.com/.

Genetic diversity of Coffea arabica L. mitochondrial genomes caused by repeat- mediated recombination and RNA editing

Background

Coffea arabica L. is one of the most important crops widely cultivated in 70 countries across Asia, Africa, and Latin America. Mitochondria are essential organelles that play critical roles in cellular respiration, metabolism, and differentiation. C. arabica's nuclear and chloroplast genomes have been reported. However, its mitochondrial genome remained unreported. Here, we intended to sequence and characterize its mitochondrial genome to maximize the potential of its genomes for evolutionary studies, molecular breeding, and molecular marker developments.

Results

We sequenced the total DNA of C. arabica using Illumina and Nanopore platforms. We then assembled the mitochondrial genome with a hybrid strategy using Unicycler software. We found that the mitochondrial genome comprised two circular chromosomes with lengths of 867,678 bp and 153,529 bp, encoding 40 protein-coding genes, 26 tRNA genes, and three rRNA genes. We also detected 270 Simple Sequence Repeats and 34 tandem repeats in the mitochondrial genome. We found 515 high-scoring sequence pairs (HSPs) for a self-to-self similarity comparison using BLASTn. Three HSPs were found to mediate recombination by the mapping of long reads. Furthermore, we predicted 472 using deep-mt with the convolutional neural network model. Then we randomly validated 90 RNA editing events by PCR amplification and Sanger sequencing, with the majority being non-synonymous substitutions and only three being synonymous substitutions. These findings provide valuable insights into the genetic characteristics of the C. arabica mitochondrial genome, which can be helpful for future study on coffee breeding and mitochondrial genome evolution.

Conclusion

Our study sheds new light on the evolution of C. arabica organelle genomes and their potential use in genetic breeding, providing valuable data for developing molecular markers that can improve crop productivity and quality. Furthermore, the discovery of RNA editing events in the mitochondrial genome of C. arabica offers insights into the regulation of gene expression in this species, contributing to a better understanding of coffee genetics and evolution.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Exploring the rare variants associated with Type 2 Diabetes Mellitus in Indian population and its disease-drug association studies: an in-silico approach

The diversified eating habits and religious culture of Indian population may be one of the reasons they largely contribute to the global diabetes burden. In the present investigation, an in-silico approach was carried out to explore hub genes in the Indian population with Type 2 Diabetes Mellitus (T2DM) that are scantily reported in the GWAS catalogue and probable potential anti-diabetic drugs from plants. This computational approach unwrapped LEP (leptin) as the hub gene among 170 genes analyzed with 14 non-synonymous single nucleotide polymorphisms (nsSNPs) with MAF < 0.01. The mutation of the LEP gene leads to a decrease in leptin concentration, which increases the risk of obesity and T2DM. According to the DUET webserver, 11 of 14 mutations examined were found to destabilize the LEP protein. Among 14, four barely reported LEP variants rs781301976 (I45N), rs776443424 (S52F), rs200915360 (D76Y), and rs1191666811 (D162N) were unzipped to be associated with T2DM, which may be the probable potential drug targets. The virtual screening revealed Vescalagin as having the highest binding energy among 336 natural compounds. Molecular docking of Vescalagin depicted higher binding energy (-9.0 kcal/mol) against mutated LEP [rs200915360 (D76Y)] compared to wild (-8.9 kcal/mol) and LEP-Metformin complexes. The trajectory analysis of MD simulations revealed that Vescalagin was more effective than Metformin in stabilizing the system. The present study suggests that the associations of the investigated nsSNPs in LEP [rs200915360 (D76Y)] and others can be key factors in the predominant role of T2DM morbidity in the Indian population that can be used as potential markers and drug targets for T2DM therapeutics.Communicated by Ramaswamy H. Sarma.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

On the linkage of thermodynamics and pathogenicity

This review outlines the effect of disease-causing mutations on proteins' thermodynamics. Two major thermodynamics quantities, which are essential for structural integrity, the folding and binding free energy changes caused by missense mutations, are considered. It is emphasized that disease effects in case of complex diseases may originate from several mutations over several genes, while monogenic diseases are caused by mutation is a single gene. Nevertheless, in both cases it is shown that pathogenic mutations cause larger perturbations of the above-mentioned thermodynamics quantities as compared with the benign mutations. Recent works demonstrating the effect of pathogenic mutations on the above-mentioned thermodynamics quantities, as well as on structural dynamics and allosteric pathways, are reviewed.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

In Silico Characterization and Analysis of Clinically Significant Variants of Lipase-H (LIPH Gene) Protein Associated with Hypotrichosis

Hypotrichosis is an uncommon type of alopecia (hair loss) characterized by coarse scalp hair caused by the reduced or fully terminated activity of the Lipase-H (LIPH) enzyme. LIPH gene mutations contribute to the development of irregular or non-functional proteins. Because several cellular processes, including cell maturation and proliferation, are inhibited when this enzyme is inactive, the hair follicles become structurally unreliable, undeveloped, and immature. This results in brittle hair, as well as altered hair shaft development and structure. Because of these nsSNPs, the protein's structure and/or function may be altered. Given the difficulty in discovering functional SNPs in genes associated with disease, it is possible to assess potential functional SNPs before conducting broader population investigations. As a result, in our in silico analysis, we separated potentially hazardous nsSNPs of the LIPH gene from benign representatives using a variety of sequencing and architecture-based bioinformatics approaches. Using seven prediction algorithms, 9 out of a total of 215 nsSNPs were shown to be the most likely to cause harm. In order to distinguish between potentially harmful and benign nsSNPs of the LIPH gene, in our in silico investigation, we employed a range of sequence- and architecture-based bioinformatics techniques. Three nsSNPs (W108R, C246S, and H248N) were chosen as potentially harmful. The present findings will likely be helpful in future large population-based studies, as well as in drug discovery, particularly in the creation of personalized medicine, since this study provides an initial thorough investigation of the functional nsSNPs of LIPH.

In Silico Analysis of nsSNPs of Human KRAS Gene and Protein Modeling Using Bioinformatic Tools

The KRAS gene belongs to the RAS family and codes for 188 amino acid residues of KRAS protein, with a molecular mass of 21.6 kD. Non-synonymous single-nucleotide polymorphisms (nsSNPs) have been identified within the coding region in which some are associated with different diseases. However, structural changes are not well defined yet. In this study, we first categorized SNPs in the KRAS coding area and then used computational methods to determine their impact on the protein structure and stability. In addition, the three-dimensional model of KRAS was taken from the Protein Data Bank for structural modeling. Furthermore, genomic data were extracted from a variety of sources, including the 1000 Genome Project, dbSNPs, and ENSEMBLE, and assessed through in silico methods. Based on various tools used in this study, 10 out of 48 missense SNPs with rsIDs were found deleterious. The substitution of alanine for proline at position 146 pushed several residues toward the center of the protein. Arginine instead of leucine has a minor effect on protein structure and stability. In addition, the substitution of proline for leucine at the 34th position disrupted the structure and led to a bigger size than the wild-type protein, hence interrupting the protein interaction. Using the well-intended computational approach and applying several bioinformatic tools, we characterized and identified most damaging nsSNPs and further explored the structural dynamics and stability of KRAS protein.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

Computational insight into in silico analysis and molecular dynamics simulation of the dimer interface residues of ALS-linked hSOD1 forms in apo/holo states: a combined experimental and bioinformatic perspective

The aggregation of misfolded SOD1 proteins in neurodegenerative illnesses is a key pathological hallmark in amyotrophic lateral sclerosis (ALS). SOD1 is stabilized and enzymatically activated after binding to Cu/Zn and forming intramolecular disulfide. SOD1 aggregation/oligomerization is triggered by the dissociation of Cu and/or Zn ions. Therefore, we compared the possible effects of ALS-associated point mutations of the holo/apo forms of WT/I149T/V148G SOD1 variants located at the dimer interface to determine structural characterization using spectroscopic methods, computational approaches as well as molecular dynamics (MD) simulations. Predictive results of computational analysis of single-nucleotide polymorphisms (SNPs) suggested that mutant SOD1 has a deleterious effect on activity and structure destabilization. MD data analysis indicated that changes in flexibility, stability, hydrophobicity of the protein as well as increased intramolecular interactions of apo-SOD1 were more than holo-SOD1. Furthermore, a decrease in enzymatic activity in apo-SOD1 was observed compared to holo-SOD1. Comparative intrinsic and ANS fluorescence results of holo/apo-WT-hSOD1 and mutants indicated structural alterations in the local environment of tryptophan residue and hydrophobic patches, respectively. Experimental and MD data supported that substitution effect and metal deficiency of mutants (apo forms) in the dimer interface may promote the tendency to protein mis-folding and aggregation, consequently disrupting the dimer-monomer equilibrium and increased propensity to dissociation dimer into SOD-monomer ultimately leading to loss of stability and function. Overall, data analysis of apo/holo SOD1 forms on protein structure and function using computational and experimental studies will contribute to a better understanding of ALS pathogenicity.

A protein enzyme-free strategy for fluorescence detection of single nucleotide polymorphisms using asymmetric MNAzymes

To establish protein enzyme-free and simple approach for sensitive detection of single nucleotide polymorphisms (SNPs), the nucleic acid amplification reactions were developed to reduce the dependence on protein enzymes (polymerase, endonuclease, ligase). These methods, while enabling highly amplified analysis for the short sequences, cannot be generalized to long genomic sequences. Herein, we develop a protein enzyme-free and general SNPs assay based on asymmetric MNAzyme probes. The multi-arm probe (MNAzyme-9_M-13) with two asymmetric recognition arms, containing a short (9 nt) and a long (13 nt) arm, is designed to detect EGFR T790 M mutation (MT). Owing to the excellent selectivity of short recognition arm, MNAzyme-9_M-13 probe can efficiently avoid interferences from wild-type target (WT) and various single-base mutations. Through a one-pot mixing, MNAzyme-9_M-13 probe enables the sensitive detection of MT, without protein enzyme or multi-step operation. The calculated detection limit for MT is 0.59 nM and 0.83%. Moreover, this asymmetric MNAzyme strategy can be applied for SNPs detection in long genomic sequences as well as short microRNAs (miRNAs) only by changing the low-cost unlabeled recognition arms. Therefore, along with simple operation, low-cost, protein enzyme-free and strong versatility, our asymmetric MNAzyme strategy provides a novel solution for SNPs detection and genes analysis.

Functional Analyses of Rare Germline Missense BRCA1 Variants Located within and outside Protein Domains with Known Functions

The BRCA1 protein is implicated in numerous important cellular processes to prevent genomic instability and tumorigenesis, and pathogenic germline variants predispose carriers to hereditary breast and ovarian cancer (HBOC). Most functional studies of missense variants in BRCA1 focus on variants located within the Really Interesting New Gene (RING), coiled-coil and BRCA1 C-terminal (BRCT) domains, and several missense variants in these regions have been shown to be pathogenic. However, the majority of these studies focus on domain specific assays, and have been performed using isolated protein domains and not the full-length BRCA1 protein. Furthermore, it has been suggested that BRCA1 missense variants located outside domains with known function are of no functional importance, and could be classified as (likely) benign. However, very little is known about the role of the regions outside the well-established domains of BRCA1, and only a few functional studies of missense variants located within these regions have been published. In this study, we have, therefore, functionally evaluated the effect of 14 rare BRCA1 missense variants considered to be of uncertain clinical significance, of which 13 are located outside the well-established domains and one within the RING domain. In order to investigate the hypothesis stating that most BRCA1 variants located outside the known protein domains are benign and of no functional importance, multiple protein assays including protein expression and stability, subcellular localisation and protein interactions have been performed, utilising the full-length protein to better mimic the native state of the protein. Two variants located outside the known domains (p.Met297Val and p.Asp1152Asn) and one variant within the RING domain (p.Leu52Phe) were found to make the BRCA1 protein more prone to proteasome-mediated degradation. In addition, two variants (p.Leu1439Phe and p.Gly890Arg) also located outside known domains were found to have reduced protein stability compared to the wild type protein. These findings indicate that variants located outside the RING, BRCT and coiled-coiled domains could also affect the BRCA1 protein function. For the nine remaining variants, no significant effects on BRCA1 protein functions were observed. Based on this, a reclassification of seven variants from VUS to likely benign could be suggested.

Non-synonymous Single Nucleotide Polymorphisms in Human ACE2 Gene May Affect the Infectivity of SARS-CoV-2 Omicron Subvariants

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19), which first appeared in December 2019. Angiotensin I converting enzyme 2 (ACE2) receptor, present on the host cells, interacts with the receptor binding domain (RBD) of spike (S) protein of SARS-CoV-2 and facilitates the viral entry into host cells.

METHODS

Non-synonymous single nucleotide polymorphisms (nsSNPs) in the ACE2 gene may have an impact on the protein's stability and its function. The deleterious or harmful nsSNPs of the ACE2 gene that can change the strength as well as the pattern of interaction with the RBD of S protein were selected for this study.

RESULTS

The ACE2:RBD interactions were analyzed by protein-protein docking study. The missense mutations A242V, R708W, G405E, D292N, Y633C, F308L, and G405E in ACE2 receptor were found to interact with RBD of Omicron subvariants with stronger binding affinity. Among the other selected nsSNPs of human ACE2 (hACE2), R768W, Y654S, F588S, R710C, R710C, A191P, and R710C were found to have lower binding affinity for RBD of Omicron subvariants.

CONCLUSION

The findings of this study suggest that the nsSNPs present in the human ACE2 gene alter the structure and function of the protein and, consequently, the susceptibility to Omicron subvariants.

PROT-ON: A structure-based detection of designer PROTein interface MutatiONs

The mutation-induced changes across protein-protein interfaces have often been observed to lead to severe diseases. Therefore, several computational tools have been developed to predict the impact of such mutations. Among these tools, FoldX and EvoEF1 stand out as fast and accurate alternatives. Expanding on the capabilities of these tools, we have developed the PROT-ON (PROTein-protein interface mutatiONs) framework, which aims at delivering the most critical protein interface mutations that can be used to design new protein binders. To realize this aim, PROT-ON takes the 3D coordinates of a protein dimer as an input. Then, it probes all possible interface mutations on the selected protein partner with EvoEF1 or FoldX. The calculated mutational energy landscape is statistically analyzed to find the most enriching and depleting mutations. Afterward, these extreme mutations are filtered out according to stability and optionally according to evolutionary criteria. The final remaining mutation list is presented to the user as the designer mutation set. Together with this set, PROT-ON provides several residue- and energy-based plots, portraying the synthetic energy landscape of the probed mutations. The stand-alone version of PROT-ON is deposited at https://github.com/CSB-KaracaLab/prot-on. The users can also use PROT-ON through our user-friendly web service http://proton.tools.ibg.edu.tr:8001/ (runs with EvoEF1 only). Considering its speed and the range of analysis provided, we believe that PROT-ON presents a promising means to estimate designer mutations.

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model

Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids, leading to increased or decreased stability of the encoded proteins. In this study, we describe a novel approach—Protein Stability Prediction with a Gaussian Network Model (PSP-GNM)—to measure the unfolding Gibbs free energy change (ΔΔG) and evaluate the effects of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa–Jernigan statistical potential. We used PSP-GNM to simulate partial unfolding of the wildtype and mutant protein structures, and then used the difference in the energies and entropies of the unfolded wildtype and mutant proteins to calculate ΔΔG. The extent of the agreement between the ΔΔG calculated by PSP-GNM and the experimental ΔΔG was evaluated on three benchmark datasets: 350 forward mutations (S350 dataset), 669 forward and reverse mutations (S669 dataset) and 611 forward and reverse mutations (S611 dataset). We observed a Pearson correlation coefficient as high as 0.61, which is comparable to many of the existing state-of-the-art methods. The agreement with experimental ΔΔG further increased when we considered only those measurements made close to 25 °C and neutral pH, suggesting dependence on experimental conditions. We also assessed for the antisymmetry (ΔΔG_reverse = −ΔΔG_forward) between the forward and reverse mutations on the Ssym+ dataset, which has 352 forward and reverse mutations. While most available methods do not display significant antisymmetry, PSP-GNM demonstrated near-perfect antisymmetry, with a Pearson correlation of −0.97. PSP-GNM is written in Python and can be downloaded as a stand-alone code.

Computational Biology of BRCA2 in Male Breast Cancer, through Prediction of Probable nsSNPs, and Hit Identification

Male breast cancer (MBC) is a relatively rare disease, but emerging data recommend the development of novel therapeutics considering its alarming threats. Compared to female breast cancer (FBC), MBC is reportedly associated with inferior outcomes (poor survival) owing to their late diagnosis and lack of adequate treatment. Treatment typically correlates with FBC, involving surgical removal of the breast tissue along with chemo/hormonal/radiation therapy, the tamoxifen being a standard adjuvant. Considering the distinct immunophenotypic (implying different pathogenesis and progression) differences from FBC, the identification of diagnostics, prognostics, and therapeutics for MBC is highly desirable. In this context, we have analyzed the most deleterious nsSNPs of BRCA2, a human tumor suppressor gene constituting the potential biomarker for tumors including MBC, to predict the structural changes associated with the mutants hampering the normal protein-protein and protein-ligand interactions, resulting in MBC progression. Among 27 nsSNPs confined to 21 rsIDs pertaining to MBC, the 19 nsSNPs constituting 14 rsIDs have been predicted as highly deleterious. We believe that these nsSNPs could serve as potential biomarkers for diagnostic and prognostic purposes and could be the pivotal target for MBC drug discovery. Subsequently, the study highlights the exploration of the key nsSNPs (of BRCA2 associated with the MBC) and its applications toward the identification of therapeutic hit TIP006136 following the homology modeling, virtual screening of 5284 phytochemicals retrieved from the TIPdb (a database of phytochemicals from indigenous plants in Taiwan) database, molecular docking (against native and mutant BRCA2), dynamic simulations (against native and mutant BRCA2), density functional theory (DFT), and molecular electrostatic potential. To the best of our knowledge, this is the first report to use diverse computational modules to investigate the important nsSNPs of BRCA2 related to MBC, implying that TIP006136 could be a potential hit and must be studied further (in vitro and in vivo) to establish its anticancer property and efficacy against MBC.

Phenotype-Genotype analysis of caucasian patients with high risk of osteoarthritis

Background: Osteoarthritis (OA) is a common cause of disability and pain around the world. Epidemiologic studies of family history have revealed evidence of genetic influence on OA. Although many efforts have been devoted to exploring genetic biomarkers, the mechanism behind this complex disease remains unclear. The identified genetic risk variants only explain a small proportion of the disease phenotype. Traditional genome-wide association study (GWAS) focuses on radiographic evidence of OA and excludes sex chromosome information in the analysis. However, gender differences in OA are multifactorial, with a higher frequency in women, indicating that the chromosome X plays an essential role in OA pathology. Furthermore, the prevalence of comorbidities among patients with OA is high, indicating multiple diseases share a similar genetic susceptibility to OA. Methods: In this study, we performed GWAS of OA and OA-associated key comorbidities on 3366 OA patient data obtained from the Osteoarthritis Initiative (OAI). We performed Mendelian randomization to identify the possible causal relationship between OA and OA-related clinical features. Results: One significant OA-associated locus rs2305570 was identified through sex-specific genome-wide association. By calculating the LD score, we found OA is positively correlated with heart disease and stroke. A strong genetic correlation was observed between knee OA and inflammatory disease, including eczema, multiple sclerosis, and Crohn's disease. Our study also found that knee alignment is one of the major risk factors in OA development, and we surprisingly found knee pain is not a causative factor of OA, although it was the most common symptom of OA. Conclusion: We investigated several significant positive/negative genetic correlations between OA and common chronic diseases, suggesting substantial genetic overlaps between OA and these traits. The sex-specific association analysis supports the critical role of chromosome X in OA development in females.

Association of CARD14 Single-Nucleotide Polymorphisms with Psoriasis

Psoriasis is an immune-mediated chronic and painful disease characterized by red raised patches of inflamed skin that may have desquamation, silvery-white scales, itching and cracks. The susceptibility of developing psoriasis depends on multiple factors, with a complex interplay between genetic and environmental factors. Studies have suggested an association between autosomal dominant CARD14 (caspase recruitment domain-containing protein 14) gain-of-function mutations with the pathophysiology of psoriasis. In this study, non-synonymous single-nucleotide polymorphisms (nsSNPs) of CARD14 gene were assessed to determine their association with psoriasis in Pakistani population. A total of 123 subjects (63 patients with psoriasis and 60 normal controls) were included in this study. DNA was extracted from blood, and PCR analysis was performed followed by Sanger sequencing for 18 CARD14 specific nsSNPs (14 previously reported and the 4 most pathogenic nsSNPs identified using bioinformatics analysis). Among the 18 tested SNPs, only 2 nsSNP, rs2066965 (R547S) and rs34367357 (V585I), were found to be associated with psoriasis. Furthermore, rs2066965 heterozygous genotype was found to be more prevalent in patients with joint pain. Additionally, the 3D structure of CARD14 protein was predicted using alpha-fold2. NMSim web server was used to perform coarse grind simulations of wild-type CARD14 and two mutated structures. R547S increases protein flexibility, whereas V353I is shown to promote CARD14-induced NF-kappa B activation. This study confirms the association between two CARD14 nsSNPs, rs2066965 and rs34367357 with psoriasis in a Pakistani population, and could be helpful in identifying the role of CARD14 gene variants as potential genetic markers in patients with psoriasis.

Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology

BACKGROUND AND OBJECTIVES

Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype.

METHODS

An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution.

RESULTS

About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains.

CONCLUSIONS

The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity.

Diverse Molecular Mechanisms Underlying Pathogenic Protein Mutations: Beyond the Loss-of-Function Paradigm

Most known disease-causing mutations occur in protein-coding regions of DNA. While some of these involve a loss of protein function (e.g., through premature stop codons or missense changes that destabilize protein folding), many act via alternative molecular mechanisms and have dominant-negative or gain-of-function effects. In nearly all cases, these non-loss-of-function mutations can be understood by considering interactions of the wild-type and mutant protein with other molecules, such as proteins, nucleic acids, or small ligands and substrates. Here, we review the diverse molecular mechanisms by which pathogenic mutations can have non-loss-of-function effects, including by disrupting interactions, increasing binding affinity, changing binding specificity, causing assembly-mediated dominant-negative and dominant-positive effects, creating novel interactions, and promoting aggregation and phase separation. We believe that increased awareness of these diverse molecular disease mechanisms will lead to improved diagnosis (and ultimately treatment) of human genetic disorders. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Roles of Eph-Ephrin Signaling in the Eye Lens Cataractogenesis, Biomechanics, and Homeostasis

The eye lens is responsible for fine focusing of light onto the retina, and its function relies on tissue transparency and biomechanical properties. Recent studies have demonstrated the importance of Eph-ephrin signaling for the maintenance of life-long lens homeostasis. The binding of Eph receptor tyrosine kinases to ephrin ligands leads to a bidirectional signaling pathway that controls many cellular processes. In particular, dysfunction of the receptor EphA2 or the ligand ephrin-A5 lead to a variety of congenital and age-related cataracts, defined as any opacity in the lens, in human patients. In addition, a wealth of animal studies reveal the unique and overlapping functions of EphA2 and ephrin-A5 in lens cell shape, cell organization and patterning, and overall tissue optical and biomechanical properties. Significant differences in lens phenotypes of mouse models with disrupted EphA2 or ephrin-A5 signaling indicate that genetic modifiers likely affect cataract phenotypes and progression, suggesting a possible reason for the variability of human cataracts due to Eph-ephrin dysfunction. This review summarizes the roles of EphA2 and ephrin-A5 in the lens and suggests future avenues of study.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

Genome-wide discovery of DNA polymorphisms via resequencing of chickpea cultivars with contrasting response to drought stress

Drought stress limits plant growth, resulting in a significant yield loss in chickpea. The diversification in genome sequence and selective sweep of allele(s) in different genotypes of a crop plant may play an important role in the determination of agronomic traits, including drought stress response. We investigated, via whole genome resequencing, the DNA polymorphisms between two sets of chickpea genotypes with contrasting drought stress responses (3 drought-sensitive vs. 6 drought-tolerant). In total, 36,406 single nucleotide polymorphisms (SNPs) and 3407 insertions or deletions (InDels) differentiating drought-sensitive and drought-tolerant chickpea genotypes were identified. Interestingly, most (91%) of these DNA polymorphisms were located in chromosomes 1 and 4. The genes harboring DNA polymorphisms in their promoter and/or coding regions and exhibiting differential expression under control and/or drought stress conditions between/within the drought-sensitive and tolerant genotypes were found implicated in the stress response. Furthermore, we identified DNA polymorphisms within the cis-regulatory motifs in the promoter region of abiotic stress-related and QTL-associated genes, which might contribute to the differential expression of the candidate drought-responsive genes. In addition, we revealed the effect of nonsynonymous SNPs on mutational sensitivity and stability of the encoded proteins. Taken together, we identified DNA polymorphisms having relevance in drought stress response and revealed candidate genes to engineer drought tolerance in chickpea.

In-Silico and In-Vitro Analysis of Human SOS1 Protein Causing Noonan Syndrome - A Novel Approach to Explore the Molecular Pathways

Aims

Perform in-silico analysis of human SOS1 mutations to elucidate their pathogenic role in Noonan syndrome (NS).

Background

NS is an autosomal dominant genetic disorder caused by single nucleotide mutation in PTPN11, SOS1, RAF1, and KRAS genes. NS is thought to affect approximately 1 in 1000. NS patients suffer different pathogenic effects depending on the mutations they carry. Analysis of the mutations would be a promising predictor in identifying the pathogenic effect of NS.

Methods

We performed computational analysis of the SOS1 gene to identify the pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) th a t cause NS. SOS1 variants were retrieved from the SNP database (dbSNP) and analyzed by in-silico tools I-Mutant, iPTREESTAB, and MutPred to elucidate their structural and functional characteristics.

Results

We found that 11 nsSNPs of SOS1 that were linked to NS. 3D modeling of the wild-type and the 11 nsSNPs of SOS1 showed that SOS1 interacts with cardiac proteins GATA4, TNNT2, and ACTN2. We also found that GRB2 and HRAS act as intermediate molecules between SOS1 and cardiac proteins. Our in-silico analysis findings were further validated using induced cardiomyocytes (iCMCs) derived from NS patients carrying SOS1 gene variant c.1654A>G (NSiCMCs) and compared to control human skin fibroblast-derived iCMCs (C-iCMCs). Our in vitro data confirmed that the SOS1, GRB2 and HRAS gene expressions as well as the activated ERK protein, were significantly decreased in NS-iCMCs when compared to C-iCMCs.

Conclusion

This is the first in-silico and in vitro study demonstrating that 11 nsSNPs of SOS1 play deleterious pathogenic roles in causing NS.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

Pierce into Structural Changes of Interactions Between Mutated Spike Glycoproteins and ACE2 to Evaluate Its Potential Biological and Therapeutic Consequences

The structural consequences of ongoing mutations on the SARS-CoV-2 spike-protein remains to be fully elucidated. These mutations could change the binding affinity between the virus and its target cell. Moreover, obtaining new mutations would also change the therapeutic efficacy of the designed drug candidates. To evaluate these consequences, 3D structure of a mutant spike protein was predicted and checked for stability, cavity sites, and residue depth. The docking analyses were performed between the 3D model of the mutated spike protein and the ACE2 protein and an engineered therapeutic ACE2 against COVID-19. The obtained results revealed that the N501Y substitution has altered the interaction orientation, augmented the number of interface bonds, and increased the affinity against the ACE2. On the other hand, the P681H mutation contributed to the increased cavity size and relatively higher residue depth. The binding affinity between the engineered therapeutic ACE2 and the mutant spike was significantly higher with a distinguished binding orientation. It could be concluded that the mutant spike protein increased the affinity, preserved the location, changed the orientation, and altered the interface amino acids of its interaction with both the ACE2 and its therapeutic engineered version. The obtained results corroborate the more aggressive nature of mutated SARS-CoV-2 due to their higher binding affinity. Moreover, designed ACe2-baased therapeutics would be still highly effective against covid-19, which could be the result of conserved nature of cellular ACE2.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10346-1.

Association of SNPs within TMPRSS6 and BMP2 genes with iron deficiency status in Saudi Arabia

BACKGROUND

Globally, iron-deficiency anemia (IDA) remains a major health obstacle. This health condition has been identified in 47% of pre-school students (aged 0 to 5 years), 42% of pregnant females, and 30% of non-pregnant females (aged 15 to 50 years) worldwide according to the WHO. Environmental and genetic factors play a crucial role in the development of IDA; genetic testing has revealed the association of a number of polymorphisms with iron status and serum ferritin.

AIM

The current study aims to reveal the association of TMPRSS6 rs141312 and BMP2 rs235756 with the iron status of females in Saudi Arabia.

METHODS

A cohort of 108 female university students aged 18-25 years was randomly selected to participate: 50 healthy and 58 classified as iron deficient. A 3-5 mL sample of blood was collected from each one and analyzed based on hematological and biochemical iron status followed by genotyping by PCR.

RESULTS

The genotype distribution of TMPRSS6 rs141312 was 8% (TT), 88% (TC) and 4% (CC) in the healthy group compared with 3.45% (TT), 89.66% (TC) and 6.89% (CC) in the iron-deficient group (P = 0.492), an insignificant difference in the allelic distribution. The genotype distribution of BMP2 rs235756 was 8% (TT), 90% (TC) and 2% (CC) in the healthy group compared with 3.45% (TT), 82.76% (TC) and 13.79% (CC) in iron-deficient group (P = 0.050) and was significantly associated with decreased ferritin status (P = 0.050). In addition, TMPRSS6 rs141312 is significantly (P<0.001) associated with dominant genotypes (TC+CC) and increased risk of IDA while BMP2 rs235756 is significantly (P<0.026) associated with recessive homozygote CC genotypes and increased risk of IDA.

CONCLUSION

Our finding potentially helps in the early prediction of iron deficiency in females through the genetic testing.

Computational and structural based approach to identify malignant nonsynonymous single nucleotide polymorphisms associated with CDK4 gene

Cycline-dependent kinase 4 (CDK4), an enzyme of the cycline dependent or Ser/Thr protein kinase family, plays a role in cell cycle progression (G1 phase) by phosphorylating a tumor suppressor protein called pRB. Alteration of this enzyme due to missense mutation/ nonsynonymous single nucleotide polymorphisms (nsSNPs) are responsible for various types of cancer progression, e.g. melanoma, lung cancer, and breast cancer. Hence, this study is designed to identify the malignant missense mutation of CDK4 from the single nucleotide polymorphism database (dbSNP) by incorporating computational algorithms. Out of 239 nsSNPs; G15S, D140Y and D140H were predicted to be highly malignant variants which may have a devastating impact on protein structure or function. We also found defective binding motif of these three mutants with the CDK4 inhibitor ribociclib and ATP. However, by incorporating molecular dynamic simulation, our study concludes that the superiority of G15S than the other two mutants (D140Y and D140H) in destabilizing proteins nature.

Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome-Related Genes

Polycystic ovary syndrome (PCOS) is a multifactorial endocrinopathy of indistinguishable etiopathogenesis that is liable to entail genetic and environmental machinery synergistically interacting with its phenotypic expression. It has been hypothesized that the environment secondarily interacts with genes to define the quantifiable phenotype in a primary, genetically determined, hyper-androgenic ovarian defect. The severity and prevalence of the disease are escalating due to uncontrolled diet and lifestyle, the influence of multiple environmental factors as well as genetic disorders. Many candidate genes have been identified to be one of the causes of PCOS. Different studies have been carried out to find the genetic correlation of PCOS. The mutational landscape analysis scans the entire genes for SNPs which usually occurs more frequently in patients and not in healthy individuals. In this study, an extensive computational analysis of all reported nsSNPs of the 27 selected PCOS-related genes was performed to infer the most pathogenic forms associated with PCOS. As a result, 28 genetic variants from 11 genes were predicted to be most harmful. Results of the present study can be useful for building an integrative genotype-phenotype database for further studies.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

Mutation Edgotype Drives Fitness Effect in Human

Missense mutations are known to perturb protein-protein interaction networks (known as interactome networks) in different ways. However, it remains unknown how different interactome perturbation patterns (“edgotypes”) impact organismal fitness. Here, we estimate the fitness effect of missense mutations with different interactome perturbation patterns in human, by calculating the fractions of neutral and deleterious mutations that do not disrupt PPIs (“quasi-wild-type”), or disrupt PPIs either by disrupting the binding interface (“edgetic”) or by disrupting overall protein stability (“quasi-null”). We first map pathogenic mutations and common non-pathogenic mutations onto homology-based three-dimensional structural models of proteins and protein-protein interactions in human. Next, we perform structure-based calculations to classify each mutation as either quasi-wild-type, edgetic, or quasi-null. Using our predicted as well as experimentally determined interactome perturbation patterns, we estimate that >∼40% of quasi-wild-type mutations are effectively neutral and the remaining are mostly mildly deleterious, that >∼75% of edgetic mutations are only mildly deleterious, and that up to ∼75% of quasi-null mutations may be strongly detrimental. These estimates are the first such estimates of fitness effect for different network perturbation patterns in any interactome. Our results suggest that while mutations that do not disrupt the interactome tend to be effectively neutral, the majority of human PPIs are under strong purifying selection and the stability of most human proteins is essential to human life.

Computational and Mass Spectrometry-Based Approach Identify Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in JMJD6

The jumonji domain-containing protein 6 (JMJD6) gene catalyzes the arginine demethylation and lysine hydroxylation of histone and a growing list of its known substrate molecules, including p53 and U2AF65, suggesting a possible role in mRNA splicing and transcription in cancer progression. Mass spectrometry-based technology offers the opportunity to detect SNP variants accurately and effectively. In our study, we conducted a combined computational and filtration workflow to predict the nonsynonymous single nucleotide polymorphisms (nsSNPs) present in JMJD6, followed by a liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and validation. The computational approaches SIFT, PolyPhen-2, SNAP, I-Mutant 2.0, PhD-SNP, PANTHER, and SNPS&GO were integrated to screen out the predicted damaging/deleterious nsSNPs. Through the three-dimensional structure of JMJD6, H187R (rs1159480887) was selected as a candidate for validation. The validation experiments showed that the mutation of this nsSNP in JMJD6 obviously affected mRNA splicing or the transcription of downstream genes through the reduced lysyl-hydroxylase activity of its substrates, U2AF65 and p53, further indicating the accuracy of this prediction method. This research provides an effective computational workflow for researchers with an opportunity to select prominent deleterious nsSNPs and, thus, remains promising for examining the dysfunction of proteins.

Seeing Keratinocyte Proteins through the Looking Glass of Intrinsic Disorder

Epidermal keratinocyte proteins include many with an eccentric amino acid content (compositional bias), atypical ultrastructural fate (built-in protease sensitivity), or assembly visible at the light microscope level (cytoplasmic granules). However, when considered through the looking glass of intrinsic disorder (ID), these apparent oddities seem quite expected. Keratinocyte proteins with highly repetitive motifs are of low complexity but high adaptation, providing polymers (e.g., profilaggrin) for proteolysis into bioactive derivatives, or monomers (e.g., loricrin) repeatedly cross-linked to self and other proteins to shield underlying tissue. Keratohyalin granules developing from liquid–liquid phase separation (LLPS) show that unique biomolecular condensates (BMC) and proteinaceous membraneless organelles (PMLO) occur in these highly customized cells. We conducted bioinformatic and in silico assessments of representative keratinocyte differentiation-dependent proteins. This was conducted in the context of them having demonstrated potential ID with the prospect of that characteristic driving formation of distinctive keratinocyte structures. Intriguingly, while ID is characteristic of many of these proteins, it does not appear to guarantee LLPS, nor is it required for incorporation into certain keratinocyte protein condensates. Further examination of keratinocyte-specific proteins will provide variations in the theme of PMLO, possibly recognizing new BMC for advancements in understanding intrinsically disordered proteins as reflected by keratinocyte biology.

In-silico screening and microsecond molecular dynamics simulations to identify single point mutations that destabilize β-hexosaminidase A causing Tay-Sachs disease

β-hexosaminidase A (HexA) protein is responsible for the degradation of GM2 gangliosides in the central and peripheral nervous systems. Tay-Sachs disease occurs when HexA within Hexosaminidase does not properly function and harmful GM2 gangliosides begin to build up within the neurons. In this study, in silico methods such as SIFT, PolyPhen-2, PhD-SNP, and MutPred were utilized to analyze the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) on HexA in order to identify possible pathogenetic and deleterious variants. Molecular dynamics (MD) simulations showed that two mutants, P25S and W485R, experienced an increase in structural flexibility compared to the native protein. Particularly, there was a decrease in the overall number and frequencies of hydrogen bonds for the mutants compared to the wildtype. MM/GBSA calculations were performed to help assess the change in binding affinity between the wildtype and mutant structures and a mechanism-based inhibitor, NGT, which is known to help increase the residual activity of HexA. Both of the mutants experienced a decrease in the binding affinity from -23.8 kcal/mol in wildtype to -20.9 and -18.7 kcal/mol for the P25S and W485R variants of HexA, respectively.

In silico screening and molecular dynamics simulation of deleterious PAH mutations responsible for phenylketonuria genetic disorder

Phenylketonuria (PKU) is a genetic disorder that if left untreated can lead to behavioral problems, epilepsy, and even mental retardation. PKU results from mutations within the phenylalanine-4-hydroxylase (PAH) gene that encodes for the PAH protein. The study of all PAH causing mutations is improbable using experimental techniques. In this study, a collection of in silico resources, sorting intolerant from tolerant, Polyphen-2, PhD-SNP, and MutPred were used to identify possible pathogenetic and deleterious PAH non-synonymous single nucleotide polymorphisms (nsSNPs). We identified two variants of PAH, I65N and L311P, to be the most deleterious and disease causing nsSNPs. Molecular dynamics (MD) simulations were carried out to characterize these point mutations on the atomic level. MD simulations revealed increased flexibility and a decrease in the hydrogen bond network for both mutants compared to the native protein. Free energy calculations using the MM/GBSA approach found that BH4 , a drug-based therapy for PKU patients, had a higher binding affinity for I65N and L311P mutants compared to the wildtype protein. We also identify important residues in the BH4 binding pocket that may be of interest for the rational drug design of other PAH drug-based therapies. Lastly, free energy calculations also determined that the I65N mutation may impair the dimerization of the N-terminal regulatory domain of PAH.

Computational Studies of the Structural Basis of Human RPS19 Mutations Associated With Diamond-Blackfan Anemia

Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.

Structural genomics approach to investigate deleterious impact of nsSNPs in conserved telomere maintenance component 1

Conserved telomere maintenance component 1 (CTC1) is an important component of the CST (CTC1-STN1-TEN1) complex, involved in maintaining the stability of telomeric DNA. Several non-synonymous single-nucleotide polymorphisms (nsSNPs) in CTC1 have been reported to cause Coats plus syndrome and Dyskeratosis congenital diseases. Here, we have performed sequence and structure analyses of nsSNPs of CTC1 using state-of-the-art computational methods. The structure-based study focuses on the C-terminal OB-fold region of CTC1. There are 11 pathogenic mutations identified, and detailed structural analyses were performed. These mutations cause a significant disruption of noncovalent interactions, which may be a possible reason for CTC1 instability and consequent diseases. To see the impact of such mutations on the protein conformation, all-atom molecular dynamics (MD) simulations of CTC1-wild-type (WT) and two of the selected mutations, R806C and R806L for 200 ns, were carried out. A significant conformational change in the structure of the R806C mutant was observed. This study provides a valuable direction to understand the molecular basis of CTC1 dysfunction in disease progression, including Coats plus syndrome.

Impact of non-synonymous mutations on the structure and function of telomeric repeat binding factor 1

Telomeric repeat binding factor 1 (TRF1) is one of the major components of the shelterin complex. It directly binds to the telomere and controls its function by regulating the telomerase acting on it. Several variations are reported in the TRF1 gene; some are associated with variety of diseases. Here, we have studied the structural and functional significance of these variations in the TRFH domain of TRF1. We have used cutting-edge computational methods such as SIFT, PolyPhen-2, PROVEAN, Mutation Assessor, mCSM, SDM, STRUM, MAESTRO, and DUET to predict the effects of 124 mutations in the TRFH domain of TRF1. Out of 124 mutations, we have identified 12 deleterious mutations with high confidence based on their prediction. To see the impact of the finally selected mutations on the structure and stability of TRF1, all-atom molecular dynamics (MD) simulations on TRF1-Wild type (WT), L79R and P150R mutants for 200 ns were carried out. A significant conformational change in the structure of the P150R mutant was observed. Our integrated computational study provides a comprehensive understanding of structural changes in TRF1 incurred due to the mutations and subsequent function, leading to the progression of many diseases.Communicated by Ramaswamy H. Sarma.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes

MOTIVATION

Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data.

RESULTS

Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code.

AVAILABILITY AND IMPLEMENTATION

UEP algorithm can be found at: https://github.com/pepamengual/UEP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drug design and repurposing with DockThor-VS web server focusing on SARS-CoV-2 therapeutic targets and their non-synonym variants

The COVID-19 caused by the SARS-CoV-2 virus was declared a pandemic disease in March 2020 by the World Health Organization (WHO). Structure-Based Drug Design strategies based on docking methodologies have been widely used for both new drug development and drug repurposing to find effective treatments against this disease. In this work, we present the developments implemented in the DockThor-VS web server to provide a virtual screening (VS) platform with curated structures of potential therapeutic targets from SARS-CoV-2 incorporating genetic information regarding relevant non-synonymous variations. The web server facilitates repurposing VS experiments providing curated libraries of currently available drugs on the market. At present, DockThor-VS provides ready-for-docking 3D structures for wild type and selected mutations for Nsp3 (papain-like, PLpro domain), Nsp5 (Mpro, 3CLpro), Nsp12 (RdRp), Nsp15 (NendoU), N protein, and Spike. We performed VS experiments of FDA-approved drugs considering the therapeutic targets available at the web server to assess the impact of considering different structures and mutations to identify possible new treatments of SARS-CoV-2 infections. The DockThor-VS is freely available at www.dockthor.lncc.br .