Computational Insight in the Identification of Non-Synonymous Single-Nucleotide Polymorphism Affecting the Structure and Function of Interleukin-4

BACKGROUND

IL4 is a versatile cytokine essentially known for differentiation, proliferation and cell death in cells. Its dysregulation has been found to be associated with the development of inflammatory disorders.

OBJECTIVE

The goal of the current investigation is to identify and select non-synonymous single-nucleotide polymorphisms (nsSNPs) in the IL-4 gene by employing computational methods which may have a potential functional impact on the occurrence of disease.

METHOD AND RESULT

Six different nsSNPs were predicted to be deleterious based on the consensus of different algorithms: SIFT, Polyphen2 (Humdiv and HumVar), PredictSNP and SNP&GO. I-mutant and MuPro assessment revealed a decrease in the stability of these mutants except K150M. Modelling was then carried out to build the wild type along with its mutants, followed by superimposition of the wild type with mutants to evaluate the RMSD value, which lies between 0.26 and 0.34. Simulation results of mutant models, along with wild type, showed that four of the mutants (N113Y, A118G, R109W and K150M) deviated most and were unstable. A118G showed a significant deviation from the wild type, while V53A and C123R were stable.

CONCLUSION

The finding establishes the evidence that the identified six nsSNPs of IL-4 can be the new entrant presenting their candidature for genetic testing.

In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual's vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs-R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)-were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00319-3.

Comprehensive computational analysis of deleterious nsSNPs in PTEN gene for structural and functional insights

Single nucleotide polymorphisms (SNPs) are pivotal in understanding the genetic basis of complex disorders. Among them, nonsynonymous SNPs (nsSNPs) that alter amino acid sequences can significantly impact protein structure and function. This study focuses on analyzing deleterious nsSNPs in the tumor suppressor gene PTEN (Phosphatase and TENsin Homolog), which plays a central role in regulating the PI3K/Akt signaling pathway and tumorigenesis. Out of 43,855 SNPs in PTEN, 17 deleterious nsSNPs were identified using six computational tools. Protein stability analysis revealed that 15 variants reduce stability, potentially leading to functional impairment. Structural evaluations using HOPE and ConSurf classified mutations into buried structural residues disrupting protein integrity and exposed functional residues affecting molecular interactions. STRING database analysis highlighted PTEN as a central node in an intricate protein network, with deleterious mutations impairing critical interactions with partners such as PIK3CA, AKT1, and TP53. Secondary structure analysis revealed distinct structural deviations, particularly for G129E, which exhibited the most pronounced destabilization. Molecular dynamics simulations confirmed stability variations across mutants, with G129E exhibiting greater instability. This comprehensive analysis enhances understanding of PTEN nsSNP impacts, offering insights for therapeutic interventions and future experimental validation.

Evolutionary history of calcium-sensing receptors unveils hyper/hypocalcemia-causing mutations

Despite advancements in understanding the structure and functions of the Calcium Sensing Receptor (CaSR), gaps persist in our knowledge of the specific functions of its residues. In this study, we used phylogeny-based techniques to identify functionally equivalent orthologs of CaSR, predict residue significance, and compute specificity-determining position (SDP) scores to understand its evolutionary basis. The analysis revealed exceptional conservation of the CaSR subfamily, emphasizing the critical role of residues with high SDP scores in receptor activation and pathogenicity. To further enhance the findings, gradient-boosting trees were applied to differentiate between gain- and loss-of-function mutations responsible for hypocalcemia and hypercalcemia. Lastly, we investigated the importance of these mutations in the context of receptor activation dynamics. In summary, through comprehensive exploration of the evolutionary history of the CaSR subfamily, coupled with innovative phylogenetic methodologies, we identified activating and inactivating residues, providing valuable insights into the regulation of calcium homeostasis and its connections to associated disorders.

Clinical and functional characterization of a novel KCNJ11 (c.101G > A, p.R34H) mutation associated with maturity-onset diabetes mellitus of the young type 13

PURPOSE

This study aimed to describe the clinical features, diagnostic and therapeutic course of a patient with MODY13 caused by KCNJ11 (c.101G > A, p.R34H) and how it contributes to the pathogenesis of MODY13, and to explore new therapeutic targets.

METHODS

Whole-exome sequencing was used to screen prediagnosed individuals and family members with clinically suspected KCNJ11 mutations. Real-time fluorescence quantitative PCR, western blotting, thallium flux of potassium channels, glucose-stimulated insulin secretion (GSIS), and immunofluorescence assays were used to analyze the regulation of insulin secretion by the KCNJ11 mutant in MIN6 cells. Daily blood glucose levels were continuously monitored for 14 days in the proband using the ambulatory blood glucose meter (SIBIONICS).

RESULTS

Mutation screening of the entire exon of the gene identified a heterozygous KCNJ11 (c.101G > A, p.R34H) mutation in the proband and his mother. Cell-based GSIS assays after transfection of MIN6 using wild-type and mutant plasmids revealed that this mutation impaired insulin secretory function. Furthermore, we found that this impaired secretory function is associated with reduced functional activity of the mutant KCNJ11 protein and reduced expression of the insulin secretion-associated exocytosis proteins STXBP1 and SNAP25.

CONCLUSION

For the first time, we revealed the pathogenic mechanism of KCNJ11 (c.101G > A, p.R34H) associated with MODY13. This mutant can cause alterations in KATP channel activity, reduce sensitivity to glucose stimulation, and impair pancreatic β-cell secretory function by downregulating insulin secretion-associated exocytosis proteins. Therefore, oral sulfonylurea drugs can lower blood glucose levels through pro-insulinotropic effects and are more favorable for patients with this mutation.

Production Technologies for Recombinant Antibodies: Insights into Eukaryotic, Prokaryotic, and Transgenic Expression Systems

Recombinant antibodies, a prominent class of recombinant proteins, are witnessing substantial growth in research and diagnostics. Recombinant antibodies are being produced employing diverse hosts ranging from highly complex eukaryotes, for instance, mammalian cell lines (and insects, fungi, yeast, etc.) to unicellular prokaryotic models like gram-positive and gram-negative bacteria. This review delves into these production methods, highlighting approaches like antibody phage display that employs bacteriophages for gene library creation. Recent studies emphasize monoclonal antibody generation through hybridoma technology, utilizing hybridoma cells from myeloma and B-lymphocytes. Transgenic plants and animals have emerged as sources for polyclonal and monoclonal antibodies, with transgenic animals preferred due to their human-like post-translational modifications and reduced immunogenicity risk. Chloroplast expression offers environmental safety by preventing transgene contamination in pollen. Diverse production technologies, such as stable cell pools and clonal cell lines, are available, followed by purification via techniques like affinity chromatography. The burgeoning applications of recombinant antibodies in medicine have led to their large-scale industrial production.

Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications

Insertions and deletions constitute the second most important source of natural genomic variation. Insertions and deletions make up to 25% of genomic variants in humans and are involved in complex evolutionary processes including genomic rearrangements, adaptation, and speciation. Recent advances in long-read sequencing technologies allow detailed inference of insertions and deletion variation in species and populations. Yet, despite their importance, evolutionary studies have traditionally ignored or mishandled insertions and deletions due to a lack of comprehensive methodologies and statistical models of insertions and deletion dynamics. Here, we discuss methods for describing insertions and deletion variation and modeling insertions and deletions over evolutionary time. We provide practical advice for tackling insertions and deletions in genomic sequences and illustrate our discussion with examples of insertions and deletion-induced effects in human and other natural populations and their contribution to evolutionary processes. We outline promising directions for future developments in statistical methodologies that would allow researchers to analyze insertions and deletion variation and their effects in large genomic data sets and to incorporate insertions and deletions in evolutionary inference.

Mapping the functional B-cell epitopes of Shigella invasion plasmid antigen D (IpaD)

Shigella bacteria utilize the type III secretion system (T3SS) to invade host cells and establish local infection. Invasion plasmid antigen D (IpaD), a component of Shigella T3SS, has garnered extensive interest as a vaccine target, primarily due to its pivotal role in the Shigella invasion, immunogenic property, and a high degree of conservation across Shigella species and serotypes. Currently, we are developing an epitope- and structure-based multivalent vaccine against shigellosis and require functional epitope antigens of key Shigella virulence determinants including IpaD. However, individual IpaD B-cell epitopes, their contributions to the overall immunogenicity, and functional activities attributing to bacteria invasion have not been fully characterized. In this study, we predicted continuous B-cell epitopes in silico and fused each epitope to a carrier protein. Then, we immunized mice intramuscularly with each epitope fusion protein, examined the IpaD-specific antibody responses, and measured antibodies from each epitope fusion for the activity against Shigella invasion in vitro. Data showed that all epitope fusion proteins induced similar levels of anti-IpaD IgG antibodies in mice, and differences were noted for antibody inhibition activity against Shigella invasion. IpaD epitope 1 (SPGGNDGNSV), IpaD epitope 2 (LGGNGEVVLDNA), and IpaD epitope 5 (SPNNTNGSSTET) induced antibodies significantly better in inhibiting invasion from Shigella flexneri 2a, and epitopes 1 and 5 elicited antibodies more effectively at preventing invasion of Shigella sonnei. These results suggest that IpaD epitopes 1 and 5 can be the IpaD representative antigens for epitope-based polyvalent protein construction and protein-based cross-protective Shigella vaccine development.IMPORTANCEShigella is a leading cause of diarrhea in children younger than 5 years in developing countries (children's diarrhea) and continues to be a major threat to public health. No licensed vaccines are currently available against the heterogeneous Shigella species and serotype strains. Aiming to develop a cross-protective multivalent vaccine against shigellosis and dysentery, we applied novel multiepitope fusion antigen (MEFA) technology to construct a broadly immunogenic polyvalent protein antigen, by presenting functional epitopes of multiple Shigella virulence determinants on a backbone protein. The functional IpaD epitopes identified from this study will essentially allow us to construct an optimal polyvalent Shigella immunogen, leading to the development of a cross-protective vaccine against shigellosis (and dysentery) and the improvement of global health. In addition, identifying functional epitopes from heterogeneous virulence determinants and using them as antigenic representatives for the development of cross-protective multivalent vaccines can be applied generally in vaccine development.

Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment Including Long Indels

Despite having important biological implications, insertion, and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modeling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts.

Review of predicting protein stability changes upon variations

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler

Background

Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem.

Methods

Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods.

Results

We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs.

Conclusion

Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.

Not exclusively the activity, but the sweet spot: a dehydrogenase point mutation synergistically boosts activity, substrate tolerance, thermal stability and yield

Catalytic activity is undoubtedly a key focus in enzyme engineering. The complicated reaction conditions hinder some enzymes from industrialization even though they have relatively promising activity. This has occurred to some dehydrogenases. Hydroxysteroid dehydrogenases (HSDHs) specifically catalyze the conversion between hydroxyl and keto groups, and hold immense potential in the synthesis of steroid medicines. We underscored the importance of 7α-HSDH activity, and analyzed the overall robustness and underlying mechanisms. Employing a high-throughput screening approach, we comprehensively assessed a mutation library, and obtained a mutant with enhanced enzymatic activity and overall stability/tolerance. The superior mutant (I201M) was identified to harbor improved thermal stability, substrate susceptibility, cofactor affinity, as well as the yield. This mutant displayed a 1.88-fold increase in enzymatic activity, a 1.37-fold improvement in substrate tolerance, and a 1.45-fold increase in thermal stability when compared with the wild type (WT) enzyme. The I201M mutant showed a 2.25-fold increase in the kcat/KM ratio (indicative of a stronger binding affinity for the cofactor). This mutant did not exhibit the highest enzyme activity compared with all the tested mutants, but these improved characteristics contributed synergistically to the highest yield. When a substrate at 100 mM was present, the 24 h yield by I201M reached 89.7%, significantly higher than the 61.2% yield elicited by the WT enzyme. This is the first report revealing enhancement of the catalytic efficiency, cofactor affinity, substrate tolerance, and thermal stability of NAD(H)-dependent 7α-HSDH through a single-point mutation. The mutated enzyme reached the highest enzymatic activity of 7α-HSDH ever reported. High enzymatic activity is undoubtedly crucial for enabling the industrialization of an enzyme. Our findings demonstrated that, when compared with other mutants boasting even higher enzymatic activity, mutants with excellent overall robustness were superior for industrial applications. This principle was exemplified by highly active enzymes such as 7α-HSDH.

Engineering Human Pancreatic RNase 1 as an Immunotherapeutic Agent for Cancer Therapy Through Computational and Experimental Studies

Most plant and bacterial toxins are highly immunogenic with non-specific toxic effects. Human ribonucleases are thought to provide a promising basis for reducing the toxic agent's immunogenic properties, which are candidates for cancer therapy. In the cell, the ribonuclease inhibitor (RI) protein binds to the ribonuclease enzyme and forms a tight complex. This study aimed to engineer and provide a gene construct encoding an improved version of Human Pancreatic RNase 1 (HP-RNase 1) to reduce connection to RI and modulate the immunogenic effects of immunotoxins. To further characterize the interaction complex of HP-RNase 1 and RI, we established various in silico and in vitro approaches. These methods allowed us to specifically monitor interactions within native and engineered HP-RNase 1/RI complexes. In silico research involved molecular dynamics (MD) simulations of native and mutant HP-RNase 1 in their free form and when bound to RI. For HP-RNase 1 engineering, we designed five mutations (K8A/N72A/N89A/R92D/E112/A) based on literature studies, as this combination proved effective for the intended investigation. Then, the cDNA encoding HP-RNase 1 was generated by RT-PCR from blood and cloned into the pSYN2 expression vector. Consequently, wild-type and the engineered HP-RNase 1 were over-expressed in E. coli TG1 and purified using an IMAC column directed against a poly-his tag. The protein products were detected by SDS-PAGE and Western blot analysis. HP-RNase 1 catalytic activity, in the presence of various concentrations of RI, demonstrated that the mutated version of the protein is able to escape the ribonuclease inhibitor and target the RNA substrate 2.5 folds more than that of the wild type. From these data, we tend to suggest the engineered recombinant HP-RNase 1 potentially as a new immunotherapeutic agent for application in human cancer therapy.

Characterisation of lepidopteran geranylgeranyl diphosphate synthase as a putative pesticide target

Geranylgeranyl pyrophosphate (diphosphate) synthase (GGPPS) plays an important role in various physiological processes in insects, such as isoprenoid biosynthesis and protein prenylation. Here, we functionally characterised the GGPPS from the major agricultural lepidopteran pests Spodoptera frugiperda and Helicoverpa armigera. Partial disruption of GGPPS by CRISPR in S. frugiperda decreased embryo hatching rate and larval survival, suggesting that this gene is essential. Functional expression in vitro of Helicoverpa armigera GGPPS in Escherichia coli revealed a catalytically active enzyme. Next, we developed and optimised an enzyme assay to screen for potential inhibitors, such as the zoledronate and the minodronate, which showed a dose-dependent inhibition. Phylogenetic analysis of GGPPS across insects showed that GGPPS is highly conserved but also revealed several residues likely to be involved in substrate binding, which were substantially different in bee pollinator and human GGPPS. Considering the essentiality of GGPPS and its putative binding residue variability qualifies a GGPPS as a novel pesticide target. The developed assay may contribute to the identification of novel insecticide leads.

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.

Engineering the cofactor binding site of 7α-hydroxysteroid dehydrogenase for improvement of catalytic activity, thermostability, and alteration of substrate preference

Hydroxysteroid dehydrogenases (HSDHs) are crucial for bile acid metabolism and influence the size of the bile acid pool and gut microbiota composition. HSDHs with high activity, thermostability, and substrate selectivity are the basis for constructing engineered bacteria for disease treatment. In this study, we designed mutations at the cofactor binding site involving Thr15 and Arg16 residues of HSDH St-2-2. The T15A, R16A, and R16Q mutants exhibited 7.85-, 2.50-, and 4.35-fold higher catalytic activity than the wild type, respectively, while also displaying an altered substrate preference (from taurocholic acid (TCA) to taurochenodeoxycholic acid (TCDCA)). These mutants showed lower Km and higher kcat values, indicating stronger binding to the substrate and resulting in 3190-, 3123-, and 3093-fold higher kcat/Km values for TCDCA oxidation. Furthermore, the Tm values of the T15A, R16A, and R16Q mutants were found to increase by 4.3 °C, 6.0 °C, and 7.0 °C, respectively. Molecular structure analysis indicated that reshaped internal hydrogens and surface mutations could improve catalytic activity and thermostability, and altered interactions among the catalytic triad, cofactor binding sites, and substrates could change substrate preference. This work provides valuable insights into modifying substrate preference as well as enhancing the catalytic activity and thermostability of HSDHs by targeting the cofactor binding site.

Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method

Pathogenic variation in DNA mismatch repair (MMR) gene MLH1 is associated with Lynch syndrome (LS), an autosomal dominant hereditary cancer. Of the 3798 MLH1 germline variants collected in the ClinVar database, 38.7% (1469) were missense variants, of which 81.6% (1199) were classified as Variants of Uncertain Significance (VUS) due to the lack of functional evidence. Further determination of the impact of VUS on MLH1 function is important for the VUS carriers to take preventive action. We recently developed a protein structure-based method named "Deep Learning-Ramachandran Plot-Molecular Dynamics Simulation (DL-RP-MDS)" to evaluate the deleteriousness of MLH1 missense VUS. The method extracts protein structural information by using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, then combines the variation data with an unsupervised learning model composed of auto-encoder and neural network classifier to identify the variants causing significant change in protein structure. In this report, we applied the method to classify 447 MLH1 missense VUS. We predicted 126/447 (28.2%) MLH1 missense VUS were deleterious. Our study demonstrates that DL-RP-MDS is able to classify the missense VUS based solely on their impact on protein structure.

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/ .

In silico and structure-based assessment to classify VUS identified in the α-helical domain of BRCA2

Breast cancer type 2 susceptibility (BRCA2) protein plays a crucial role in DNA double-strand breaks repair mechanism by homologous recombination. Pathogenic mutations in the BRCA2 gene confer an increased risk of hereditary breast and ovarian cancer (HBOC). Different missense mutations are identified from a larger cohort of patient populations in the BRCA2. However, most missense mutations are classified as 'Variants of Uncertain Significance' (VUS) due to a lack of data from structural, functional, and clinical assessments. Therefore, this study focused on assessing VUS identified in the α-helical domain of h-BRCA2 using different in silico tools and structure-based molecular dynamics simulation. A total of 286 identified VUS were evaluated using Align-GVGD, PROVEAN and PANTHER servers and 18 variants were predicted to be pathogenic. Further, out of 18 variants analyzed using the ConSurf server, 16 variants were found to be evolutionary conserved. These 16 conserved variants were submitted to PremPS and Dynamut server to assess the effect of the mutation at the protein structure level; 12 mutations were predicted to have a destabilizing effect on the native protein structure. Finally, molecular dynamics simulations revealed 5 variants BRCA2 Cys2646Tyr, Asp2665Val, Trp2619Arg, Trp2619Ser and Tyr2660Cys can alter the folding pattern and need further validation using in vitro, structural and in vivo studies to classify as pathogenic.Communicated by Ramaswamy H. Sarma.

Genome-wide identification and evolutionary analysis of the FGF gene family in buffalo

Fibroblast growth factors (FGFs) are important polypeptide growth factors that play a critical role in many developmental processes, including differentiation, cell proliferation, and migration in mammals. This study employs in silico analyses to characterize the FGF gene family in buffalo, investigating their genome-wide identification, physicochemical properties, and evolutionary patterns. For this purpose, genomic and proteomic sequences of buffalo, cattle, goat, and sheep were retrieved from NCBI database. We identified a total of 22 FGF genes in buffalo. Physicochemical properties observed through ProtParam tool showed notable features of these proteins including in-vitro instability, thermostability, hydrophilicity, and basic nature. Phylogenetic analysis grouped 22 identified genes into nine sub-families based on evolutionary relationships. Additionally, analysis of gene structure, motif patterns, and conserved domains using TBtools revealed the remarkable conservation of this gene family across selected species throughout the course of evolution. Comparative amino acid analysis performed through ClustalW demonstrated significant conservation between buffalo and cattle FGF proteins. Mutational analysis showed three non-synonymous mutations at positions R103 > G, P7 > L, and E98 > Q in FGF4, FGF6, and FGF19, respectively in buffalo. Duplication events revealed only one segmental duplication (FGF10/FGF22) in buffalo and two in cattle (FGF10/FGF22 and FGF13/FGF13-like) with Ka/Ks values <1 indicating purifying selection pressure for these duplications. Comparison of protein structures of buffalo, goat, and sheep exhibited more similarities in respective structures. In conclusion, our study highlights the conservation of the FGF gene family in buffalo during evolution. Furthermore, the identified non-synonymous mutations may have implications for the selection of animals with better performance.Communicated by Ramaswamy H. Sarma.

Cysteine Enrichment Mediates Co-Option of Uricase in Reptilian Skin and Transition to Uricotelism

Uric acid is the main means of nitrogen excretion in uricotelic vertebrates (birds and reptiles) and the end product of purine catabolism in humans and a few other mammals. While uricase is inactivated in mammals unable to degrade urate, the presence of orthologous genes without inactivating mutations in avian and reptilian genomes is unexplained. Here we show that the Gallus gallus gene we name cysteine-rich urate oxidase (CRUOX) encodes a functional protein representing a unique case of cysteine enrichment in the evolution of vertebrate orthologous genes. CRUOX retains the ability to catalyze urate oxidation to hydrogen peroxide and 5-hydroxyisourate (HIU), albeit with a 100-fold reduced efficiency. However, differently from all uricases hitherto characterized, it can also facilitate urate regeneration from HIU, a catalytic property that we propose depends on its enrichment in cysteine residues. X-ray structural analysis highlights differences in the active site compared to known orthologs and suggests a mechanism for cysteine-mediated self-aggregation under H2O2-oxidative conditions. Cysteine enrichment was concurrent with the transition to uricotelism and a shift in gene expression from the liver to the skin where CRUOX is co-expressed with β-keratins. Therefore, the loss of urate degradation in amniotes has followed opposite evolutionary trajectories: while uricase has been eliminated by pseudogenization in some mammals, it has been repurposed as a redox-sensitive enzyme in the reptilian skin.

Genome-Wide Identification, Evolutionary and Mutational Analysis of the Buffalo Sox Gene Family

The Sox gene family constitutes transcription factors with a conserved high mobility group box (HMG) that regulate a variety of developmental processes, including sex differentiation, neural, cartilage, and early embryonic development. In this study, we systematically analyzed and characterized the 20 Sox genes from the whole buffalo genome, using comparative genomic and evolutionary analyses. All the buffalo Sox genes were divided into nine sub-groups, and each gene had a specific number of exons and introns, which contributed to different gene structures. Molecular phylogeny revealed more sequence similarity of buffalo Sox genes with those of cattle. Furthermore, evolutionary analysis revealed that the HMG domain remained conserved in the all members of the Sox gene family. Similarly, all the genes are under strong purifying selection pressure; seven segmental duplications occurred from 9.65 to 21.41 million years ago (MYA), and four potential recombination breakpoints were also predicted. Mutational analysis revealed twenty non-synonymous mutations with potential effects on physiological functions, including embryonic development and cell differentiation in the buffalo. The present study provides insights into the genetic architecture of the Sox gene family in buffalo, highlights the significance of mutations, and provides their potential utility for marker-assisted selection for targeted genetic improvement in buffalo.

Mutational analysis of catalytic site domain of CCHFV L RNA segment

INTRODUCTION

Crimean-Congo haemorrhagic fever virus (CCHFV) has tripartite RNA genome and is endemic in various countries of Asia, Africa and Europe.

METHOD

The present study is focused on mutation profiling of CCHFV L segment and phylogenetic clustering of protein dataset into six CCHFV genotypes.

RESULTS

Phylogenetic tree rooted with NCBI reference sequence (YP_325663.1) indicated less divergence from genotype III and the sequences belonging to same genotypes have shown less divergence among each other. Mutation frequency at 729 mutated positions was calculated and 563, 49, 33, 46 and 38 amino acid positions were found to be mutated at mutation frequency intervals of 0-0.2, 0.21-0.4, 0.41-0.6, 0.61-0.8 and 0.81-1.0 respectively. Thirty-eight highly frequent mutations (0.81-1.0 interval) were found in all genotypes and mapping in L segment (encoded for RdRp) revealed four mutations (V2074I, I2134T/A, V2148A and Q2695H/R) in catalytic site domain and no mutation in OTU domain. Molecular dynamic simulation and in silico analysis showed that catalytic site domain displayed large deviation and fluctuation upon introduction of these point mutations.

CONCLUSION

Overall study provides strong evidence that OTU domain is highly conserved and less prone to mutation whereas point mutations recorded in catalytic domain have affected the stability of protein and were found to be persistent in the large population.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Novel in-frame duplication variant characterization in late infantile metachromatic leukodystrophy using whole-exome sequencing and molecular dynamics simulation

Metachromatic leukodystrophy (MLD) is a neurodegenerative lysosomal storage disease caused by a deficiency in the arylsulfatase A (ARSA). ARSA deficiency leads to sulfatide accumulation, which involves progressive demyelination. The profound impact of early diagnosis on MLD treatment options necessitates the development of new or updated analysis tools and approaches. In this study, to identify the genetic etiology in a proband from a consanguineous family with MLD presentation and low ARSA activity, we employed Whole-Exome Sequencing (WES) followed by co-segregation analysis using Sanger sequencing. Also, MD simulation was utilized to study how the variant alters the structural behavior and function of the ARSA protein. GROMACS was applied and the data was analyzed by RMSD, RMSF, Rg, SASA, HB, atomic distance, PCA, and FEL. Variant interpretation was done based on the American College of Medical Genetics and Genomics (ACMG) guidelines. WES results showed a novel homozygous insertion mutation, c.109_126dup (p.Asp37_Gly42dup), in the ARSA gene. This variant is located in the first exon of ARSA, fulfilling the criteria of being categorized as likely pathogenic, according to the ACMG guidelines and it was also found to be co-segregating in the family. The MD simulation analysis revealed this mutation influenced the structure and the stabilization of ARSA and led to the protein function impairment. Here, we report a useful application of WES and MD to identify the causes of a neurometabolic disorder.

In silico engineering a CD80 variant with increased affinity to CTLA-4 and decreased affinity to CD28 for optimized cancer immunotherapy

CD80 or cluster of differentiation 80, also known as B7-1, is a member of the immunoglobulin super family, which binds to CTLA-4 and CD28 T cell receptors and induces inhibitory and inductive signals respectively. Although CTLA-4 and CD28 receptors belong to the same protein family, slight differences in their structures leads to CD80 having a higher binding affinity to CTLA-4 (-14.55 kcal/mol) compared with CD28(-12.51 kcal/mol). In this study, we constructed a variant of CD80 protein with increased binding affinity to CTLA-4 and decreased binding affinity to CD28. This variant has no signaling capability, and can act as a cap for these receptors to protect them from natural CD80 proteins existing in the body. The first step was the evolutionary and alanine scanning analysis of CD80 protein to determine conserved regions in this protein. Next, complex alanine scanning technique was employed to determine CD80 protein hotspots in CD80-CTLA-4 and CD80-CD28 protein complexes. This information was fed into a computational model developed in R for in silico mutagenesis and CD80 variant library construction. The 3D structures of variants were modeled using the Swiss model webserver. After modeling the 3D structures, HADDOCK server was employed to build all protein-protein complexes, which contain CTLA-4-CD80 variant complexes, Wild type CD80-CD28 complexes and CD28-CD80 variant complexes. Protein-protein binding free energy was determined using FoldX and the variant number 316 with mutations at 29, 31, 33 positions showed increased binding affinity to CTLA-4 (-21.43 kcal/mol) and decreased binding affinity to CD28 (- 9.54 kcal/mol). Finally, molecular dynamics (MD) simulations confirmed the stability of variant 316. In conclusion, we designed a new CD80 protein variant with potential immunotherapeutic applications.

Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex

In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.

Computational screening and structural analysis of Gly201Arg and Gly201Asp missense mutations in human cyclin-dependent kinase 4 protein

The regulatory proteins, cyclins, and cyclin-dependent kinases (CDKs) control the cell cycle progression. CDK4 gene mutations are associated with certain cancers such as melanoma, breast cancer, and rhabdomyosarcoma. Therefore, understanding the mechanisms of cell cycle control and cell proliferation is essential in developing cancer treatment regimens. In this study, we obtained cancer-causing CDK4 mutations from the COSMIC database and subjected them to a series of in silico analyses to identify the most significant mutations. An overall of 238 mutations (119 missense mutations) retrieved from the COSMIC database were investigated for the pathogenic and destabilizing properties using the PredictSNP and iStable algorithms. Further, the amino acid position of the most pathogenic and destabilizing mutations were analyzed to understand the nature of amino acid conservation across the species during the evolution. We observed that the missense mutations G201R and G201D were more significant and the Glycine at position 201 was found to highly conserved. These significant mutations were subjected to molecular dynamics simulation analysis to understand the protein's structural changes. The results from molecular dynamics simulations revealed that both G201R and G201D of CDK4 are capable of altering the protein's native form. On comparison among the most significant mutations, G201R disrupted the protein structure higher than the protein with G201D.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

The Fast and the Furriest: Investigating the Rate of Selection on Mammalian Toxins

The evolution of venom and the selection pressures that act on toxins have been increasingly researched within toxinology in the last two decades, in part due to the exceptionally high rates of diversifying selection observed in animal toxins. In 2015, Sungar and Moran proposed the 'two-speed' model of toxin evolution linking evolutionary age of a group to the rates of selection acting on toxins but due to a lack of data, mammals were not included as less than 30 species of venomous mammal have been recorded, represented by elusive species which produce small amounts of venom. Due to advances in genomics and transcriptomics, the availability of toxin sequences from venomous mammals has been increasing. Using branch- and site-specific selection models, we present the rates of both episodic and pervasive selection acting upon venomous mammal toxins as a group for the first time. We identified seven toxin groups present within venomous mammals, representing Chiroptera, Eulipotyphla and Monotremata: KLK1, Plasminogen Activator, Desmallipins, PACAP, CRiSP, Kunitz Domain One and Kunitz Domain Two. All but one group (KLK1) was identified by our results to be evolving under both episodic and pervasive diversifying selection with four toxin groups having sites that were implicated in the fitness of the animal by TreeSAAP (Selection on Amino Acid Properties). Our results suggest that venomous mammal ecology, behaviour or genomic evolution are the main drivers of selection, although evolutionary age may still be a factor. Our conclusion from these results indicates that mammalian toxins are following the two-speed model of selection, evolving predominately under diversifying selection, fitting in with other younger venomous taxa like snakes and cone snails-with high amounts of accumulating mutations, leading to more novel adaptions in their toxins.

In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa

PURPOSE

Increased susceptibility towards anorexia nervosa (AN) was reported with reduced levels of neuronatin (NNAT) gene. We sought to investigate the most pathogenic rare-coding missense mutations, non-synonymous single-nucleotide polymorphisms (nsSNPs) of NNAT and their potential damaging impact on protein function through transcript level sequence and structure based in silico approaches.

METHODS

Gene sequence, single nucleotide polymorphisms (SNPs) of NNAT was retrieved from public databases and the putative post-translational modification (PTM) sites were analyzed. Distinctive in silico algorithms were recruited for transcript level SNPs analyses and to characterized high-risk rare-coding nsSNPs along with their impact on protein stability function. Ab initio 3D-modeling of wild-type, alternate model prediction for most deleterious nsSNP, validation and recognition of druggable binding pockets were also performed. AN 3D therapeutic compounds that followed rule of drug-likeness were docked with most pathogenic variant of NNAT to estimate the drugs' binding free energies.

RESULTS

Conclusively, 10 transcript (201-205)-based nsSNPs from 3 rare-coding missense variants, i.e., rs539681368, rs542858994, rs560845323 out of 840 exonic SNPs were identified. Transcript-based functional impact analyses predicted rs539681368 (C30Y) from NNAT-204 as the high-risk rare-coding pathogenic nsSNP, deviating protein functions. The 3D-modeling analysis of AN drugs' binding energies indicated lowest binding free energy (ΔG) and significant inhibition constant (K_i) with mutant models C30Y.

CONCLUSIONS

Mutant model (C30Y) exhibiting significant drug binding affinity and the commonest interaction observed at the acetylation site K59. Thus, based on these findings, we concluded that the identified nsSNP may serve as potential targets for various studies, diagnosis and therapeutic interventions.

LEVEL OF EVIDENCE

No level of evidence-open access bioinformatics research.

Molecular and Structural Analysis of Specific Mutations from Saudi Isolates of SARS-CoV-2 RNA-Dependent RNA Polymerase and their Implications on Protein Structure and Drug-Protein Binding

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has stressed the global health system to a significant level, which has not only resulted in high morbidity and mortality but also poses a threat for future pandemics. This situation warrants efforts to develop novel therapeutics to manage SARS-CoV-2 in specific and other emerging viruses in general. This study focuses on SARS-CoV2 RNA-dependent RNA polymerase (RdRp) mutations collected from Saudi Arabia and their impact on protein structure and function. The Saudi SARS-CoV-2 RdRp sequences were compared with the reference Wuhan, China RdRp using a variety of computational and biophysics-based approaches. The results revealed that three mutations-A97V, P323I and Y606C-may affect protein stability, and hence the relationship of protein structure to function. The apo wild RdRp is more dynamically stable with compact secondary structure elements compared to the mutants. Further, the wild type showed stable conformational dynamics and interaction network to remdesivir. The net binding energy of wild-type RdRp with remdesivir is -50.76 kcal/mol, which is more stable than the mutants. The findings of the current study might deliver useful information regarding therapeutic development against the mutant RdRp, which may further furnish our understanding of SARS-CoV-2 biology.

Embeddings from protein language models predict conservation and variant effects

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient-MCC-for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA , and PredictProtein.

Comparative Genomic Characterization of Insulin-Like Growth Factor Binding Proteins in Cattle and Buffalo

The somatotropic axis consists of genes associated with economic traits like muscle growth and carcass traits in livestock. Insulin-like growth factor binding proteins (IGFBPs) are the major proteins that play a vital role in the somatotropic axis. The present study performed a genome-wide characterization of IGFBP genes in cattle. Genomic sequences of the IGFBP gene family for different mammals (cattle, buffalo, goat, and sheep) were recovered from the NCBI database. Sequence analyses were performed to investigate cattle's genomic variations in the IGFBP gene family. Phylogenetic analysis, gene structure, motif patterns, and conserved domain analysis (CDA) of the IGFBP family revealed the evolutionarily conserved nature of the IGFBP genes in cattle and other studied species. Physicochemical properties of IGFBP proteins in cattle revealed that most of these proteins are unstable, hydrophilic, thermostable, and acidic. Comparative amino acid analysis revealed variations in all protein sequences with single indels in IGFBP3 and IGFBP6. Mutation analysis revealed only one nonsynonymous mutation D212 > E in the IGFBP6 protein of cattle. A total of 245 nuclear hormone receptor (NHRs) sites were detected, including 139 direct repeats (DR), 65 everted repeats (ER), and 41 inverted repeats (IR). Out of 133 transcription factors (TFs), 10 TFs (AHR, AHRARNT, AP4, CMYB, E47, EGR2, GATA, SP1, and SRF) had differential distribution (P value < 0.05) of putative transcriptional binding sites (TFBS) in cattle compared to buffalo. Synteny analysis revealed the conserved nature of genes between cattle and buffalo. Two gene pairs (IGFBP1/IGFBP3 and IGFBP2/IGFBP5) showed tandem duplication events in cattle and buffalo. This study highlights the functional importance of genomic variation in IGFBP genes and necessitates further investigations better to understand the role and mechanisms of IGFBPs in bovines.

Insertions and deletions in protein evolution and engineering

Protein evolution or engineering studies are traditionally focused on amino acid substitutions and the way these contribute to fitness. Meanwhile, the insertion and deletion of amino acids is often overlooked, despite being one of the most common sources of genetic variation. Recent methodological advances and successful engineering stories have demonstrated that the time is ripe for greater emphasis on these mutations and their understudied effects. This review highlights the evolutionary importance and biotechnological relevance of insertions and deletions (indels). We provide a comprehensive overview of approaches that can be employed to include indels in random, (semi)-rational or computational protein engineering pipelines. Furthermore, we discuss the tolerance to indels at the structural level, address how domain indels can link the function of unrelated proteins, and feature studies that illustrate the surprising and intriguing potential of frameshift mutations.

Chikungunya Encephalitis: an Inconsistently Reported Headache and Cause of Death in Patients with Pre-Existing Conditions

Chikungunya virus (CHIKV) is an alphavirus of the family Togaviridae with outbreaks occurring across Africa, Asia, parts of Europe, and South and Central America. There are three main lineages of CHIKV, including the West African lineage, the East Central South African (ECSA) lineage, and the Asian lineage. While CHIKV infection usually results in a self-limited febrile illness, there have been reports of concerning neurological manifestations, including encephalitis. Herein we discuss findings of over 700 cases of CHIKV encephalitis and risk factors for death. Additionally, we examined the genotypes of CHIKV associated with encephalitis and found that both the Asian and ECSA lineages were responsible for encephalitis but not the West African lineage. Protein analysis of consensus sequences of CHIKV strains associated with encephalitis identified mutations in the nsP1, nsP2, and nsP3 proteins. Reports and manuscripts of CHIKV encephalitis were inconsistent in reporting viral, demographic, and clinical features which complicated the delineation of risk factors associated with the disease and viral evolution. As climate change contributes to the range expansion of natural vectors, it is important for researchers and clinicians to consistently report patient and viral data to facilitate research and countermeasures for the ecology and epidemiology of CHIKV due to the lack of a targeted treatment or vaccine.

Coevolved Canonical Loops Conformations of Single-Domain Antibodies: A Tale of Three Pockets Playing Musical Chairs

Single-domain antibodies (sdAbs) are a promising class of biotherapeutics with unique structural traits within their paratope region. The distribution of canonical conformations explored by their complementarity determining region (CDR) loops differs to some extent from conventional two-chain Fv fragments of monoclonal antibodies (mAbs). In this study, we explored in detail the canonical structures of sdAb CDR-H1 and CDR-H2 loops and compared those with mAbs from the IGHV3 and IGHV1 gene families. We surveyed the antibody structures catalogued in SAbDab and clustered the CDR canonical loops in Cartesian space. While most of the sdAb clusters were sub-populations of previously defined canonical Fv conformations of CDR-H1 and CDR-H2, our stricter clustering approach defined narrower clusters in sequence-space. Meticulous visual inspection of sub-populations allowed a clearer understanding of sequence-structure relationships. The packing densities within structural pockets contacted by CDR-H1 and CDR-H2 canonical conformations were analyzed on the premise that these pockets cannot be left vacant as they would leave exposed supportive hydrophobic residues. The fine resolution of the canonical clusters defined here revealed unique signatures within these pockets, including distinct structural complementarities between CDR-H1 and CDR-H2 canonical clusters, which could not be perceived with the previous coarser clusters. We highlight examples where a single residue change in CDR-H1 sequence is sufficient to induce a dramatic population shift in CDR-H2 conformation. This suggests that preferences in combining CDR-H1 and CDR-H2 emerged naturally during antibody evolution, leading to preferred sets of conserved amino acids at key positions in the framework as well as within the CDR loops. We outline a game of musical chairs that is necessary to maintain the integrity of the antibody structures that arose during evolution. Our study also provides refined CDR-H1 and CDR-H2 structural templates for sdAb homology modeling that could be leveraged for improved antibody design.

Increased Frequency of Indels in Hypervariable Regions of SARS-CoV-2 Proteins—A Possible Signature of Adaptive Selection

Most attention in the surveillance of evolving SARS-CoV-2 genome has been centered on nucleotide substitutions in the spike glycoprotein. We show that, as the pandemic extends into its second year, the numbers and ratio of genomes with in-frame insertions and deletions (indels) increases significantly, especially among the variants of concern (VOCs). Monitoring of the SARS-CoV-2 genome evolution shows that co-occurrence (i.e., highly correlated presence) of indels, especially deletions on spike N-terminal domain and non-structural protein 6 (NSP6) is a shared feature in several VOCs such as Alpha, Beta, Delta, and Omicron. Indels distribution is correlated with spike mutations associated with immune escape and growth in the number of genomes with indels coincides with the increasing population resistance due to vaccination and previous infections. Indels occur most frequently in the spike, but also in other proteins, especially those involved in interactions with the host immune system. We also showed that indels concentrate in regions of individual SARS-CoV-2 proteins known as hypervariable regions (HVRs) that are mostly located in specific loop regions. Structural analysis suggests that indels remodel viral proteins’ surfaces at common epitopes and interaction interfaces, affecting the virus’ interactions with host proteins. We hypothesize that the increased frequency of indels, the non-random distribution of them and their independent co-occurrence in several VOCs is another mechanism of response to elevated global population immunity.

Molecular Docking Study of IPBCC.08.610 Glucose Oxidase Mutant for Increasing Gluconic Acid Production

Glucose oxidase (GOD) is an oxidoreductase enzyme that catalyzes the oxidation of glucose to gluconolactone and hydrogen peroxide. Then, gluconolactone will be hydrolyzed to gluconic acid. The wide application of gluconic acid in various industries has increased production demand. However, glucose concentrations higher than 40% (w/w) inhibited the conversion of glucose to gluconic acid due to a decrease in the oxygen solubility concentration at pH 6, 30℃, and 1 bar pressure. Therefore, decreasing the value of Km is predicted to reduce saturation and enhance gluconic acid production. This study aimed to analyze the interaction between the IPBCC.08.610 GOD mutant with β-D-Glucose in improving gluconic acid production by decreasing the Km value. Mutations were performed in silico using Chimera and then docked using AutoDock Vina. The mutations resulted in distinct ligand poses in the binding pocket, different -OH conformations of the ligands, and changes in the T554M/D578P mutant’s hydrophobicity index (554 mutated from threonine to methionine, and 578 mutated from aspartate to proline), and decreased ΔG and Km values in the H559D mutant (559 mutated from histidine to aspartate), D578P and T554M/D578P. This decrease might strengthen the ligand-receptor interaction, increasing gluconic acid production. The H559D was the best mutant to increase production based on the ΔG, Km value, and stability due to the addition of hydrogen bonds.

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases

Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.

Loss of the collagen IV modifier prolyl 3-hydroxylase 2 causes thin basement membrane nephropathy

The glomerular filtration barrier (GFB) produces primary urine and is composed of a fenestrated endothelium, a glomerular basement membrane (GBM), podocytes, and a slit diaphragm. Impairment of the GFB leads to albuminuria and microhematuria. The GBM is generated via secreted proteins from both endothelial cells and podocytes and is supposed to majorly contribute to filtration selectivity. While genetic mutations or variations of GBM components have been recently proposed to be a common cause of glomerular diseases, pathways modifying and stabilizing the GBM remain incompletely understood. Here, we identified prolyl 3-hydroxylase 2 (P3H2) as a regulator of the GBM in an a cohort of patients with albuminuria. P3H2 hydroxylates the 3' of prolines in collagen IV subchains in the endoplasmic reticulum. Characterization of a P3h2ΔPod mouse line revealed that the absence of P3H2 protein in podocytes induced a thin basement membrane nephropathy (TBMN) phenotype with a thinner GBM than that in WT mice and the development of microhematuria and microalbuminuria over time. Mechanistically, differential quantitative proteomics of the GBM identified a significant decrease in the abundance of collagen IV subchains and their interaction partners in P3h2ΔPod mice. To our knowledge, P3H2 protein is the first identified GBM modifier, and loss or mutation of P3H2 causes TBMN and focal segmental glomerulosclerosis in mice and humans.

Mutations in G6PC2 gene with increased risk for development of type 2 diabetes: Understanding via computational approach

An increase in the fast blood glucose (FBG) levels has been linked to an increased risk of developing a chronic condition, type 2 diabetes (T2D). The mutation in the G6PC2 gene was identified to have a lead role in the modulation of FBG levels. The abnormal regulation of this enzyme influences glucose-stimulated insulin secretion (GSIS), which controls the insulin levels corresponding to the system's glucose level. This study focuses on the mutations at the G6PC2 gene, which cause the variation from normal expression levels and increase the risk of T2D. We examined the non-synonymous single nucleotide polymorphisms (nsSNPs) present in the G6PC2 and subjected them to pathogenicity, stability, residue conservation, and membrane simulation. The individual representation of surrounding amino acids in the mutant (I63T) model showed the loss of hydrophobic interactions compared to the native G6PC2. In addition, the trajectory results from the membrane simulation exhibited reduced stability, and the least compactness was identified for the I63T mutant model. Our study shed light on the structural and conformational changes at the transmembrane region due to the I63T mutation in G6PC2. Additionally, the Gibbs free energy landscape analysis against the two principal components showed structural differences and decreased the conformational stability of the I63T mutant model compared to the native. Like those presented in this study, dynamical simulations may indeed be crucial to comprehending the structural insights of G6PC2 mutations in cardiovascular-associated mortality and T2D.

LYRUS: a machine learning model for predicting the pathogenicity of missense variants

SUMMARY

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.

AVAILABILITY AND IMPLEMENTATION

LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Comprehensive mapping of binding hot spots of SARS-CoV-2 RBD-specific neutralizing antibodies for tracking immune escape variants

BACKGROUND

The receptor-binding domain (RBD) variants of SARS-CoV-2 could impair antibody-mediated neutralization of the virus by host immunity; thus, prospective surveillance of antibody escape mutants and understanding the evolution of RBD are urgently needed.

METHODS

Using the single B cell cloning technology, we isolated and characterized 93 RBD-specific antibodies from the memory B cells of four COVID-19 convalescent individuals in the early stage of the pandemic. Then, global RBD alanine scanning with a panel of 19 selected neutralizing antibodies (NAbs), including several broadly reactive NAbs, was performed. Furthermore, we assessed the impact of single natural mutation or co-mutations of concern at key positions of RBD on the neutralization escape and ACE2 binding function by recombinant proteins and pseudoviruses.

RESULTS

Thirty-three amino acid positions within four independent antigenic sites (1 to 4) of RBD were identified as valuable indicators of antigenic changes in the RBD. The comprehensive escape mutation map not only confirms the widely circulating strains carrying important immune escape RBD mutations such as K417N, E484K, and L452R, but also facilitates the discovery of new immune escape-enabling mutations such as F486L, N450K, F490S, and R346S. Of note, these escape mutations could not affect the ACE2 binding affinity of RBD, among which L452R even enhanced binding. Furthermore, we showed that RBD co-mutations K417N, E484K, and N501Y present in B.1.351 appear more resistant to NAbs and human convalescent plasma from the early stage of the pandemic, possibly due to an additive effect. Conversely, double mutations E484Q and L452R present in B.1.617.1 variant show partial antibody evasion with no evidence for an additive effect.

CONCLUSIONS

Our study provides a global view of the determinants for neutralizing antibody recognition, antigenic conservation, and RBD conformation. The in-depth escape maps may have value for prospective surveillance of SARS-CoV-2 immune escape variants. Special attention should be paid to the accumulation of co-mutations at distinct major antigenic sites. Finally, the new broadly reactive NAbs described here represent new potential opportunities for the prevention and treatment of COVID-19.

Saturation Mutagenesis of the Transmembrane Region of HokC in Escherichia coli Reveals Its High Tolerance to Mutations

Cells adapt to different stress conditions, such as the antibiotics presence. This adaptation sometimes is achieved by changing relevant protein positions, of which the mutability is limited by structural constrains. Understanding the basis of these constrains represent an important challenge for both basic science and potential biotechnological applications. To study these constraints, we performed a systematic saturation mutagenesis of the transmembrane region of HokC, a toxin used by Escherichia coli to control its own population, and observed that 92% of single-point mutations are tolerated and that all the non-tolerated mutations have compensatory mutations that reverse their effect. We provide experimental evidence that HokC accumulates multiple compensatory mutations that are found as correlated mutations in the HokC family multiple sequence alignment. In agreement with these observations, transmembrane proteins show higher probability to present correlated mutations and are less densely packed locally than globular proteins; previous mutagenesis results on transmembrane proteins further support our observations on the high tolerability to mutations of transmembrane regions of proteins. Thus, our experimental results reveal the HokC transmembrane region high tolerance to loss-of-function mutations that is associated with low sequence conservation and high rate of correlated mutations in the HokC family sequences alignment, which are features shared with other transmembrane proteins.

Analyzing the Effect of Mutations in SARS-CoV2 Papain-Like Protease from Saudi Isolates on Protein Structure and Drug-Protein Binding: Molecular Modelling and Dynamics Studies

The continuous and rapid development of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus remains a health concern especially with the emergence of numerous variants and mutations worldwide. As with other RNA viruses, SARS-CoV-2 has a genetically high mutation rate. These mutations have an impact on the virus characteristics, including transmissibility, antigenicity and development of drug and vaccine resistance. This work was pursued to identify the differences that exist in the papain-like protease (PL^Pro) from 58 Saudi isolates in comparison to the first reported sequence from Wuhan, China and determine their implications on protein structure and the inhibitor binding. PL^pro is a key protease enzyme for the host cells invasion and viral proteolytic cleavage, hence, it emerges as a valuable antiviral therapeutic target. Two mutations were identified including D108G and A249V and shown to increase the molecular flexibility of PL^Pro protein and alter the protein stability, particularly with D108G mutation. The effect of these mutations on the stability and dynamic behavior of PL^Pro structures as well as their effect on the binding of a known inhibitor; GRL0617 were further investigated by molecular docking and dynamic simulation.

Evolution-Based Protein Engineering for Antifungal Peptide Improvement

Antimicrobial peptides (AMPs) have been considered as the alternatives to antibiotics because of their less susceptibility to microbial resistance. However, compared with conventional antibiotics they show relatively low activity and the consequent high cost and nonspecific cytotoxicity, hindering their clinical application. What’s more, engineering of AMPs is a great challenge due to the inherent complexity in their sequence, structure, and function relationships. Here, we report an evolution-based strategy for improving the antifungal activity of a nematode-sourced defensin (Cremycin-5). This strategy utilizes a sequence-activity comparison between Cremycin-5 and its functionally diverged paralogs to identify sites associated with antifungal activity for screening of enhanceable activity-modulating sites for subsequent saturation mutagenesis. Using this strategy, we identified a site (Glu-15) whose mutations with nearly all other types of amino acids resulted in a universally enhanced activity against multiple fungal species, which is thereby defined as a Universally Enhanceable Activity-Modulating Site (UEAMS). Especially, Glu15Lys even exhibited >9-fold increased fungicidal potency against several clinical isolates of Candida albicans through inhibiting cytokinesis. This mutant showed high thermal and serum stability and quicker killing kinetics than clotrimazole without detectable hemolysis. Molecular dynamic simulations suggest that the mutations at the UEAMS likely limit the conformational flexibility of a distant functional residue via allostery, enabling a better peptide–fungus interaction. Further sequence, structural, and mutational analyses of the Cremycin-5 ortholog uncover an epistatic interaction between the UEAMS and another site that may constrain its evolution. Our work lights one new road to success of engineering AMP drug leads.

Qualitative and semiquantitative assessment of thyroid hormone binding proteins in greyhounds and other dog breeds

Total thyroxine (T4) concentrations are lower in healthy greyhounds compared to most other non-sighthound breeds. In humans, variations in the structure or concentration of the major thyroid hormone binding proteins are responsible for most reported differences between total T4 concentrations in healthy individuals from different ethnic groups or other subpopulations. The aim of this study was to determine if such variations are also responsible for the lower total T4 concentrations in greyhounds. The predicted protein sequences of thyroxine-binding globulin (TBG), transthyretin and albumin were determined in liver tissue from a euthyroid greyhound with decreased T4 concentration and a Jack Russell terrier using reverse-transcriptase PCR. Sequences were compared to each other and online reference sequences. Serum proteins from 21 greyhounds and 21 non-sighthound dogs were separated by denaturing electrophoresis and immunoblots probed with polyclonal antibodies to human TBG and transthyretin. Reactive bands were quantified by densitrometry, expressed relative to the mean of reference samples included in each gel. Serum albumin concentrations were measured using a commercially-available assay. Several SNPs were identified but none was thought likely to explain the lower total T4 concentrations in greyhounds. There was no significant difference between the quantity of any of the binding proteins in serum from greyhounds and non-sighthound dogs. However, total T4 and transthyretin concentrations were highly correlated in the greyhound group (r = 0.73, P = 0.0002). Variation in the sequence of thyroid hormone binding proteins is not responsible for low greyhound total T4 concentrations. Further evaluation of the role of transthyretin is warranted.

Conditions under which distributions of edge length ratios on phylogenetic trees can be used to order evolutionary events

Two recent high profile studies have attempted to use edge (branch) length ratios from large sets of phylogenetic trees to determine the relative ages of genes of different origins in the evolution of eukaryotic cells. This approach can be straightforwardly justified if substitution rates are constant over the tree for a given protein. However, such strict molecular clock assumptions are not expected to hold on the billion-year timescale. Here we propose an alternative set of conditions under which comparisons of edge length distributions from multiple sets of phylogenies of proteins with different origins can be validly used to discern the order of their origins. We also point out scenarios where these conditions are not expected to hold and caution is warranted.