More than 4,000 single nucleotide polymorphisms (SNP) variants have been identified in the human ZFP36L2 gene, however only a few have been studied in the context of protein function. The tandem zinc finger domain of ZFP36L2, an RNA binding protein, is the functional domain that binds to its target mRNAs. This protein/RNA interaction triggers mRNA degradation, controlling gene expression. We identified 32 non-synonymous SNPs (nsSNPs) in the tandem zinc finger domain of ZFP36L2 that could have possible deleterious impacts in humans. Using different bioinformatic strategies, we prioritized five among these 32 nsSNPs, namely rs375096815, rs1183688047, rs1214015428, rs1215671792 and rs920398592 to be validated. When we experimentally tested the functionality of these protein variants using gel shift assays, all five (Y154H, R160W, R184C, G204D, and C206F) resulted in a dramatic reduction in RNA binding compared to the WT protein. To understand the mechanistic effect of these variants on the protein/RNA interaction, we employed DUET, DynaMut and PyMOL to investigate structural changes in the protein. Additionally, we conducted Molecular Docking and Molecular Dynamics Simulations to fine tune the active behaviour of this biomolecular system at an atomic level. Our results propose atomic explanations for the impact of each of these five genetic variants identified.

Short rib polydactyly syndrome (SRPS), with or without polydactyly, also known as Verma-Naumoff/Saldino-Noonan syndrome, is a type of skeletal ciliopathy. Initially, variants in the IFT80 gene were implicated; however, approximately half of the SRPS cases are associated with variants in the DYNC2H1 gene. Additionally, digenic variants involving DYNC2H1 and NEK1 can contribute to the syndrome.

This case report describes a male patient presenting with characteristic SRPS features, including a constricted thorax and shortened limbs. Exome sequencing was performed to identify causative variants, followed by functional analyses to assess the pathogenicity of the identified variants, including a synonymous variant.

Exome sequencing identified compound heterozygous variants in the DYNC2H1 gene: a novel missense variant c.6439G>T p.(Asp2147Tyr) and a synonymous variant c.6477G>A p.(Gln2159=). Functional analyses confirmed that the synonymous variant triggers nonsense-mediated decay of the affected allele.

This study expands the spectrum of DYNC2H1 variants associated with SRPS and emphasizes the importance of functional analyses in genetic diagnostics. Demonstrating pathogenicity for a synonymous variant highlights the necessity for comprehensive variant assessments to improve diagnostic accuracy and enable early intervention. These findings have significant implications for molecular diagnostics and personalized therapy strategies in skeletal ciliopathies.

Valosin-containing protein (VCP/p97), a pivotal AAA+ ATPase, orchestrates proteostasis via ER-associated degradation (ERAD), ubiquitin-mediated proteolysis, and organelle surveillance. Pathogenic missense mutations, notably Arg95Gly (R95G) within the evolutionarily conserved double-ψ β-barrel (DPBB) of its N-terminal domain, are implicated in proteinopathies including IBMPFD and ALS. To decode the structural-dynamics perturbations underpinning R95G-driven dysfunction, we integrated AlphaFold3-based modeling, protein-peptide docking, and multiscale enhanced-sampling molecular dynamics (MD) simulations-spanning 1.2 μs all-atom, 12 μs coarse-grained, and umbrella sampling regimes. Our findings reveal that R95G disrupts the β-barrel integrity, destabilizes long-range domain coupling, and engenders conformational heterogeneity deleterious to gp78 cofactor recruitment. Free-energy landscapes of the mutant highlight enthalpically disfavored, low-occupancy binding conformers, corroborated by MM/PBSA-based end-state binding free energy and potential of mean force (PMF) analyses, which indicate impaired binding thermodynamics. Interface hotspot mapping pinpoints dynamic perturbations at critical residues that propagate allosteric decoupling and morphological distortion of the binding interface. Collectively, our results delineate a mechanistic cascade-from local β-barrel destabilization to global interaction network disruption-underlying VCP's functional impairment in disease states. This work provides a computationally derived structural framework to inform targeted biophysical validation and the rational design of therapeutic strategies aimed at rescuing VCP function in IBMPFD and ALS.

Ezrin (EZR) is a crucial linker between the actin cytoskeleton and the plasma membrane. It interacts with proteins involved in cancer-related signaling pathways. To assess the impact of nonsynonymous single nucleotide polymorphisms (nsSNPs) on EZR structure and function, we employed bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, PhD-SNP, SNPs&GO, SuSPect, and FATHMM) and identified deleterious variants. Stability analyses using MUpro, mCSM, I-Mutant 2.0, and DynaMut2 revealed six destabilizing nsSNPs (F240S, H288D, I248T, L59Q, L125S, and L225P). Structural modeling using HOPE, MutPred2, AlphaFold, Swiss-Model, and protein-protein docking using HADDOCK 2.4 assessed the impact on the EZR-EBP50 complex. Binding free energy calculations, salt bridge analysis, and interface residue mapping further confirmed that the L225P, F240S, and I248T mutations significantly impaired EZR-EBP50 interaction, potentially disrupting key signaling pathways. Molecular dynamics simulations indicated that mutant EZR proteins exhibited reduced stability, flexibility, and hydrogen bonding. This first comprehensive in silico analysis of EZR highlights pathogenic nsSNPs that may contribute to disease progression. These findings provide a foundation for experimental validation and may inform targeted therapies for EZR-related pathologies.

Head and neck squamous cell carcinoma (HNSCC) continues to be a formidable epithelial malignancy characterized by late-stage detection and recurrence impacting survival. P21-activated kinase-1 (PAK1) was reported to be overexpressed in head and neck cancers and activated by ionizing radiation (IR), affecting treatment outcomes. Present investigations revealed that PAK1 silencing on HNSCC cells reverted the aggressive phenotype and showed impaired DNA damage repair upon IR exposure. Further HNSCC cells were resistant to IR up to 30 Gy with elevated pPAK1 levels. Radiation-resistant (RR) HNSCC cells expressed radiation-resistant markers, namely MRE-11 and NME-1; stemness markers-OCT4 and SOX2; and EMT & metastasis markers-vimentin, snail, and α-smooth muscle actin (α-SMA). In addition, HNSCC RR cells showed increased levels of DNA damage response protein H2AX, indicative of an aggressive phenotype with an augmented DNA repair machinery and a potential target for inhibition. Since H2AX appears to be a mechanistic hub for PAK1-induced radiation resistance, using in silico methods, peptides were designed, and the PL-8 peptide was chosen to target the phosphorylation of H2AX, which could enhance the sensitivity to IR and push the cells to radiation-induced cell death. PL-8 peptide inhibited H2AX phosphorylation on HNSCC cells and triggered radiation-induced cell death as determined by functional assays. The present study reveals PAK1 induced in HNSCC cells by IR and causes resistance by enhancing DNA damage response mediated through γH2AX. To counteract this complex molecular interplay, we propose inhibiting γH2AX formation & silencing PAK1 appears to be a probable way forward in HNSCC.

Achromatopsia is an autosomal recessive genetic disease, and 95% of achromatopsia patients carry pathogenic mutations in the CNGA3 and CNGB3 genes. Once translated, these genes function together by forming a cone photoreceptor CNG channel protein complex.

There are 150 CNGA3 missense variants reported in achromatopsia patients, but the pathogenicity of 103 variants remains unknown due to inconclusive genetic information. Here, we present clinical features of a novel CNGA3 variant in an achromatopsia patient and demonstrate its pathogenicity by a three-dimensional (3D) proteoform-based structure-function analysis. We first identified six proteotypic groups using 47 pathogenic missense variants with distinctive functional consequences by mapping their spatial proximity in a 3D protein structure. This meta-analysis was further applied to 103 missense variants of unknown significance (VUS) found in patients with achromatopsia. Strikingly, 86.4% of VUS had similar/identical functional consequence to nearby pathogenic variants, which suggested their likely pathogenicity and potential molecular pathology. The distinct proteotypic consequence of CNGA3 mutants shown in our analysis strongly supported the notion that gene supplementation may be the most widely applicable therapeutic option for CNGA3-associated achromatopsia patients.

Thus, proteoform-based analysis can be a valuable approach for assessing novel variants and complement clinical genomics in its utilization.

Predicting the functional impact of single-point mutations on protein residual activity, especially after high-temperature incubation, is critical in protein engineering. We present an innovative machine learning model based on eXtreme Gradient Boosting that leverages protein sequence data to predict thermostability, circumventing the need for three-dimensional structural information. Our model integrates features from the ESM2 language model, physicochemical properties, evolutionary features, and positional features. A key advancement is the use of transfer learning with thermal stability data from various proteins, which enhances prediction accuracy and generalizability. To fine-tune and validate the model, we used experimental data from Candida antarctica lipase B single-point mutants, a widely studied enzyme in biocatalysis and industrial applications. Despite potential limitations of Gibbs free energy values in capturing all factors influencing thermostability, our model represents a significant improvement over traditional approaches, providing valuable insights for protein engineering, enzyme optimization, and therapeutic protein development.

Gamma-amino butyrate aminotransferase (GABA-AT or ABAT) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that catalyzes the conversion of GABA and α-ketoglutarate into succinic semialdehyde and L-glutamate. In humans, the primary physiological role of GABA-AT is to control the level of GABA in neuronal tissues. Mutations on ABAT gene are associated to GABA-AT deficiency, an ultra-rare autosomal recessive disorder characterized by accelerated linear growth, severe psychomotor retardation, seizures, hypotonia, and hyperreflexia. So far, several missense pathogenic mutations of GABA-AT have been identified; however, their molecular effects at protein level have been poorly investigated. In this work a biochemical characterization of 10 pathogenic variants of human GABA-AT was carried out by expressing the protein in HEK-293 cells. Moreover, in-silico analyses of the variants were performed to corroborate the experimental findings. Altogether, the data obtained on protein expression level, GABA transaminase activity, and the predicted structural impact allowed us to classify the variants into three distinct groups, such as: (i) variants with strong structural and catalytic defects (p.P152S, p.L211F, and p.L478P); (ii) variants characterized mainly by a strong catalytic defect (p.R220K, p.Q296H, and p.R377W); (iii) variants exhibiting moderate structural and catalytic defects maintaining substantial transaminase activity (p.R92Q, p.F213C, p.G465D, and p.G465R). Based on these results, we provide a picture of the molecular defects of different GABA-AT pathogenic variants with the aim of gaining insights into the enzymatic phenotypes in GABA-AT deficiency.

Usher syndrome is a rare genetic condition characterized by both hearing and vision impairment that occurs through mutations of multiple genes, including WHRN and MYO15A. In this computational work, we intend to explore how missense SNPs within the WHRN protein affect its interaction with the MYO15A protein, a crucial component of the Usher interactome. Therefore, the identification of missense SNPs that has a potential effect on the function of the WHRN protein was realized using various computational prediction tools, including VEP, SIFT, PolyPhen-2, CADD, REVEL, and Mutation Assessor. Further evaluation of the stability of mutated proteins was conducted through SDM2, MCSM, DeepDDG and CUP-SAT. We used ConSurf web server to identify conserved regions in the WHRN protein. Yasara and Haddock analysis tools were used to minimize the energy of protein 3D structures and to dock protein-protein complexes, respectively. and then the binding energy of the complexes was calculated through PRODIGY. Mutation pathogenicity prediction tools showed that in total, 18 missense SNPs, predicted as deleterious. However, a comprehensive analysis revealed that only SIX single nucleotide polymorphisms were predicted to be the most deleterious with high conservation and less stability. Furthermore, we conducted molecular dynamics analysis to fully comprehend the impact of these variations on the dynamic behavior of the WHRN-MYO15A protein complex, which revealed significant insights into the destabilizing effects of the deleterious SNPs impacting the protein's binding affinity and stability that occurs during the binding process of the WHRN-MYO15A protein complex.

Calpain-1 and calpain-2 are heterodimeric proteases consisting of a common small regulatory subunit CAPNS1 and a large catalytic subunit, CAPN1 or CAPN2, respectively. These calpains have emerged as potential therapeutic targets in cancer and other diseases through their roles in cell signaling pathways affecting sensitivity to chemotherapeutic and targeted drugs, and in promoting metastasis. While inhibition of calpains has the potential to provide therapeutic benefit to cancer patients, there are currently no clinically approved active site directed drugs that specifically and effectively inhibit them. However, the structures of calpain-1 and calpain-2 make them susceptible to allosteric inhibition aimed at interfering with heterodimerization of the catalytic and regulatory subunits, which is necessary for stability and proteolytic activity. Split-Nanoluciferase biosensors were generated to quantify the protein-protein interactions (PPIs) between the calcium-binding penta-EF hand (PEF) domains of CAPN1 or CAPN2 and CAPNS1. These biosensors were used to quantify the heterodimer dissociation constants (KD) of calpain-1 and calpain-2, estimated at 185 nM and 509 nM, respectively, in the presence of 5 mM Ca2+; and 362 nM and 1651 nM, respectively, in the presence of Mg2+. The half-maximal Ca2+ concentrations supporting these PPIs for calpain-1 and calpain-2 were 59.9 μM and 940.8 μM, respectively. Molecular modeling, based on the crystal structure of calpain-2, was used to predict 20 residues of the PEF domains that contribute to heterodimerization. Individual point mutation of CAPNS1 at Q263 reduced the catalytic activity of calpain-2 to 51.0 ± 6.4 % in live cells.

While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effects on protein structure and function. Addressing the challenges of large sequence-space analysis, we present EVOLVE, a web tool enabling researchers to explore prospective mutation sites and their collective behavior. EVOLVE integrates a statistical mechanics-guided machine learning algorithms to predict probable mutational sites, with statistical mechanics calculating mutational entropy to accurately identify mutational hotspots. Validation against a number of viral protein sequences confirms its ability to predict mutations and their functional consequences. By leveraging statistical mechanics of phase transition concept, EVOLVE also quantifies mutational entropy fluctuations, offering a quantitative foundation for identifying Variants of Concern (VOC) or Variants under Monitoring (VUM) as per World Health Organization (WHO) guidelines. EVOLVE streamlines data upload and analysis with a user-friendly interface and comprehensive tutorials. Access EVOLVE free at https://evolve-iiserkol.com.

Cytochrome P450 (CYPs) are crucial heme-containing enzymes that metabolize drugs and endogenous compounds. In humans, 57 CYP isoforms have been identified, with over 200 mutations linked to severe disorders. Our comprehensive computational study assessed the reason for the pathogenicity of mutations by comparing pathogenic and non-pathogenic variants. We analyzed 25,94,151 mutations across 26 CYP structures using structure- and sequence-based methods, revealing a meaningful stability pattern: non-pathogenic > all > pathogenic mutation datasets. Notably, pathogenic mutations were predominantly buried within CYP structures, indicating a higher potential for pathogenesis. We identified three key amino acid properties affected by mutations: Gibbs free energy, isoelectric point, and volume. Furthermore, diseased mutations significantly reduced positive residue content, particularly due to arginine mutations, which directly influenced the isoelectric point. Our findings indicate a greater likelihood of pathogenic mutations occurring at conserved sites, disrupting CYP function. A higher frequency of pathogenic mutations was observed in heme sites, primarily involving arginine, which may interfere with arginine-heme interactions. Molecular docking revealed a differential binding of heme in wild-type and pathogenic CYPs. This study provides a foundational analysis of mutation effects across multiple CYPs. It models the chemical basis of CYP-related pathogenicity, facilitating the development of a semi-quantitative disease prediction model.

G-proteins areindispensable regulators of cellular signaling, with G-protein-gated inwardly rectifying potassium channels (GIRK) as key effectors. GNB1 encephalopathy (GNB1E) is a congenital neurological syndrome resulting from mutations in the GNB1 gene, encoding the Gβ1 subunit of G-proteins trimer (Gαβγ). GNB1E manifests as a global developmental delay, accompanied by tonus disturbances, ataxia, and epilepsy.

We utilized the Xenopus laevis oocyte heterologous expression system to investigate the impact of the L95P mutation in Gβ1 (Gβ1-L95P) on the activation of neuronal GIRK channels GIRK2 and GIRK1/2. Mutant and wild-type (WT) Gβ1 RNAs were co-injected with RNAs encoding the Gγ2 and GIRK channel subunits. The expression levels of both Gβ1 and the channel proteins, as well as the channel activity, were systematically monitored. Additionally, rigid-body docking was used to model the GIRK1/2-Gβγ complex, evaluating L95P's effect on channel-Gβγ interaction, Gβγ stability, and Gβγ-effector affinity.

. Gβ1-L95P exhibited reduced protein expression compared to WT. Even after RNA adjustments to restore comparable membrane localization, the mutant failed to effectively activate GIRK2 and GIRK1/2. Structural analysis revealed that L95 was not consistent in the Gβγ-effector interface. Thermodynamic calculations suggested that the mutation primarily destabilized Gβ1 and Gβ1-effector complex.

Gβ1-L95P leads to both reduced protein expression and impaired function in the GIRK-Gβγ interaction system. The later effect can be attributed to the changes associated with protein misfolding.

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

Advanced genetic strategies have transformed our understanding of the genetic basis and diagnosis of many phenotypes, including rare diseases. However, missense variants (MVs) are frequently identified and often classified as variants of uncertain significance (VUS). Although changes in protein free energy (ΔΔG) were recently proposed as a tool for VUS classification, no objective cut-offs exist to distinguish between benign and pathogenic variants.

We utilized the computational tool mCSM to calculate ΔΔG and predict the impact of MVs on protein stability. Specifically, we systematically analyzed the ΔΔG of MVs in IFT140 to identify those potentially pathogenic and associated with Mainzer-Saldino syndrome (MSS). To this end, we evaluated ΔΔG in IFT140 MVs sourced from ClinVar, gnomAD, and MSS patients, aiming to resolve the diagnosis of MSS in a child with a novel homozygous IFT140 variant, initially reported as a VUS.

IFT140 MVs from MSS patients showed lower ΔΔG values than those reported in gnomAD individuals (-1.389 vs. -0.681 kcal/mol; p = 0.0031). A ROC curve demonstrated strong discriminative ability (AUC = 0.8488; p = 0.0002), and a ΔΔG cut-off of -1.3 kcal/mol achieving 50% sensibility and 90% specificity. The analysis of ClinVar IFT140 variants classified as VUS, showed that 75/323 (23%) presented ΔΔG values below the cut-off. In the child clinically suspicious of MSS, this cut-off allowed the reclassification of the VUS (IFT140:p.W80C; ΔΔG = -1.745 kcal/mol) as likely pathogenic, which confirmed the diagnosis molecularly.

Our findings demonstrate that ΔΔG analysis can effectively distinguish potentially pathogenic variants in IFT140, enabling confirmation of MSS. The established cut-off of -1.3 kcal/mol showed strong discriminative power, aiding in the reclassification of VUS identified in IFT140. This approach highlights the utility of protein stability predictions in resolving diagnostic uncertainty in rare diseases.

The RNA exosome is an evolutionarily conserved, multiprotein complex that is the major RNase in 3' processing and degradation of a wide range of RNAs in eukaryotes. Single amino acid changes in RNA exosome subunits cause rare genetic diseases collectively called exosomopathies. However, distinguishing disease-causing variants from nonpathogenic ones remains challenging, and the mechanism by which these variants cause disease is largely unknown. Previous studies have employed a budding yeast model of RNA exosome-linked diseases that relies on mutating the orthologous yeast genes. Here, we develop a humanized yeast model of exosomopathies that allows us to unambiguously assess damaging effects of the exact patient variant in budding yeast. Individual replacement of the yeast subunits with corresponding mammalian orthologs identified 6 out of 9 noncatalytic core subunits of the budding yeast RNA exosome that can be replaced by a mammalian subunit, with 3 of the replacements supporting close to normal growth. Further analysis of the disease-associated variants utilizing the hybrid yeast/mammalian RNA exosome revealed functional defects caused by both previously characterized and uncharacterized variants of EXOSC2, EXOSC4, EXOSC7, and EXOSC9. Analysis of the protein levels of these variants indicates that a subset of the patient-derived variants causes reduced protein levels, while other variants are defective but are expressed as well as the reference allele, suggesting a more direct contribution of these residues to RNA exosome function. This humanized yeast model of exosomopathies provides a convenient and sensitive genetic tool to help distinguish damaging RNA exosome variants from benign variants. This disease model can be further exploited to uncover the underpinning mechanism of RNA exosome defects.

Proteins play a critical role in biology and biopharma due to their specificity and minimal side effects. Predicting the effects of mutations on protein stability is vital but experimentally challenging. Deep learning offers an efficient solution to this problem. In the present work, we introduced ProstaNet, a deep learning framework that predicts stability changes resulting from single- and multiple-point mutations using geometric vector perceptrons-graph neural network for 3-dimensional feature processing. For training ProstaNet, we meticulously crafted ProstaDB, a comprehensive and pristine thermodynamics repository, including 3,784 single-point mutations and 1,642 multiple-point mutations. We also created thermodynamic looping for enlarging the limited data size of multiple-point mutation and applied an innovative clustering method to generate a standard testing set of multiple-point mutation. Besides, we identified residue scoring as the most important encoding method in protein properties prediction. With these innovations, ProstaNet accurately predicts thermostability changes for both single-point and multiple-point mutations without showing any bias. ProstaNet achieves an accuracy of 0.75, outperforming existing methods for single-point mutation prediction, including ThermoMPNN (0.63), PoPMuSiCsym (0.66), MUPRO (0.52), and FoldX (0.71). ProstaNet also achieves a 1.3-fold increase in accuracy compared to FoldX for multiple-point mutation predictions. Validated by experiment, 4 out of 5 single-point mutation predictions (80%) and all multiple-point mutation predictions (100%) for HuJ3 mutants were accurate, demonstrating the potential benefits of ProstaNet for protein engineering and drug development.

The global energy crisis, driven by economic growth and the increasing demand for energy, highlights the urgency of searching for alternative energy sources to mitigate environmental pollution and climate change. β-Glucosidases act in the final step of the enzymatic hydrolysis of cellulose, cleaving the β-1,4-glycosidic bonds in cellobiose to produce second-generation ethanol. However, these enzymes are easily inhibited by glucose, their final product, which limits the production of this biofuel. Genetic engineering combined with bioinformatics tools can improve key enzymatic characteristics, such as catalytic activity and glucose tolerance, in a more precise, faster, and cost-effective manner compared to traditional methods. In this work, a variant of a β-glucosidase from the GH1 family, isolated from the microbial community of Amazonian soil (Brazil), with enhanced catalytic activity and improved for glucose retroinhibition, was developed.

Bioinformatics analyses suggested the substitution of tryptophan at position 404 with leucine. The produced variant (W404L) was expressed in Escherichia coli and showed activity 3.2 times higher in the presence of glucose than the non-mutated control. Moreover, the partially purified mutated variant of β-glucosidase exhibited a 26-fold increase in catalytic activity compared to the original form of the enzyme. The results confirmed that the mutation proposed by computational analyses had a significant impact on enzyme catalytic activity and glucose retroinhibition.

This new variant may become a promising alternative to reduce the costs of enzyme cocktails used in the hydrolysis of lignocellulosic biomass used as a raw material in the production of second-generation ethanol.

Resistance to azoles and amphotericin B especially in Aspergillus fumigatus is a growing concern towards the treatment of invasive fungal infection. At this critical juncture, intein splicing would be a productive, and innovative target to establish therapies against resistant strains. Intein splicing is the central event for the activation of host protein, essential for the growth and survival of various microorganisms including A. fumigatus. The splicing process is a four-step protease-like nucleophilic cascade. Thus, we hypothesise that protease inhibitors would successfully halt intein splicing and potentially restrict the growth of the aforementioned pathogen. Using Rosetta Fold and molecular dynamics simulations, we modelled Prp8 intein structure; resembling classic intein fold with horse shoe shaped splicing domain. To fully comprehend the active site of Afu Prp8 intein, C1, T62, H65, H818, N819 from intein sequences and S820, the first C-extein residue are selected. Molecular docking shows that two FDA-approved drugs, i.e. Lufotrelvir and Remdesivir triphosphate efficiently interact with Prp8 intein from the assortment of 212 protease inhibitors. MD simulation portrayed that Prp8 undergoes conformational change upon ligand binding, and inferred the molecular recognition and stability of the docked complexes. Per-residue decomposition analysis confirms the importance of F: block R802, V803, and Q807 binding pocket in intein splicing domain towards recognition of inhibitors, along with active site residues through strong hydrogen bonds and hydrophobic contacts. However, in vitro and in vivo assays are required to confirm the inhibitory action on Prp8 intein splicing; which may pave the way for the development of new antifungals for A. fumigatus.

Enzymes are critical biological catalysts involved in maintaining the intricate balance of metabolic processes within living organisms. Mutations in enzymes can result in disruptions to their functionality that may lead to a range of diseases. This review focuses on computational studies that investigate the effects of disease-associated mutations in various enzymes. Through molecular dynamics simulations, multiscale calculations, and machine learning approaches, computational studies provide detailed insights into how mutations impact enzyme structure, dynamics, and catalytic activity. This review emphasizes the increasing impact of computational simulations in understanding molecular mechanisms behind enzyme (dis)function by highlighting the application of key computational methodologies to selected enzyme examples, aiding in the prediction of mutation effects and the development of therapeutic strategies.

Myeloid-derived suppressor cells (MDSCs) are a crucial and diverse group of cells found in the tumor microenvironment (TME) that facilitate progression, invasion, and metastasis within solid tumors. CD84, a homophilic adhesion molecule expressed on MDSCs, plays a critical role in their accumulation and function within the TME. This study aims to investigate the protein-protein interactions of CD84 using molecular dynamics simulations and to explore potential therapeutic strategies targeting these interactions.

Through computational techniques, we generated highly potent mutated CD84 mini-proteins and peptides as antagonists with significantly higher affinity for CD84 to mimic the key features of the IgV-like domain of the protein. Additionally, we engineered an antibody capable of blocking CD84. Binding affinities were assessed using dissociation constant (Kd) calculations.

Data analysis shows that the Kd values for the designed peptides ranged from 10 to 100 times stronger than those of the natural CD84 interactions, indicating efficient inhibition of CD84 interactions. Additionally, mutagenesis of the Ig-like V domain of CD84 resulted in variants with improved binding stability, with a Gibbs free energy change (ΔΔG) indicating enhanced interaction potential.

This study provides insights into CD84 interactions and their implications for immunotherapy targeting MDSCs in solid tumors. However, experimental validation is necessary to confirm the findings of this study and evaluate peptide selectivity as potential molecular therapeutics.

Thyroid cancer (TC) is the most common endocrine malignancy, with 90%-95% of the cases representing non-medullary thyroid cancer (NMTC). Familial cases account only for a few of all cases and the underlying genetic causes are still poorly understood.

We whole-genome sequenced affected and unaffected members of an Italian NMTC family and applied our in-house developed Familial Cancer Variant Prioritization Pipeline (FCVPPv2) which prioritized 12 coding variants. We refined this selection using the VarSome American College of Medical Genetics and Genomics (ACMG) implementation, SNAP2 predictions and further in silico scores.

We prioritized 4 possibly pathogenic variants in 4 genes including Ret proto-oncogene (RET), polypeptide N-acetylgalactosaminyltransferase 10 (GALNT10), ubinuclein-1 (UBN1), and prostaglandin I2 receptor (PTGIR). The role of RET point mutations in medullary thyroid carcinoma is well established. Similarly, somatic rearrangements of RET are known in papillary TC, a specific histotype of NMTC. In contrast to RET, no germline variants in PTGIR, GALNT10, or UBN1 have been linked to the development of TC to date. However, alterations in these genes have been shown to affect pathways related to cell proliferation, apoptosis, growth, and differentiation, as well as posttranslational modification and gene regulation. A thorough review of the available literature together with computational evidence supported the interpretation of the 4 shortlisted variants as possibly disease-causing in this family.

Our results implicate the first germline variant in RET in a family with NMTC as well as the first germline variants in PTGIR, GALNT10, and UBN1 in TC.

Fucosyltransferase 2 (FUT2) gene has been extensively reported to play its role in potential gut microbiota changes and norovirus susceptibility. The normal activity of FUT2 has been found to be disrupted by non-synonymous single nucleotide polymorphisms (nsSNPs) in its gene. To explore the possible mutational changes and their deleterious effects, we employed state-of-the-art computational strategies. Firstly, nine widely-used bioinformatics tools were utilized for initial screening of possibly deleterious nsSNPs. Subsequently, the structural and functional effects of screened nsSNPs on FUT2 were evaluated by utilizing relevant computational tools. Following this, the two shortlisted nsSNPs, including G149S (rs200543547) and V196G (rs367923363), were further validated by their molecular docking with norovirus capsid protein, VP1. As compared to wild-type, the higher stability and lower binding energy scores of the both the mutants indicated their stable binding with VP1, which ultimately leads to norovirus implications. These docking results were further verified by a comprehensive computational approach, molecular dynamic simulation, which gave results in the form of lower RMSD, RMSF, RoG, and hydrogen bond values of both the mutants, depicted in relevant graphs. Overall, this research explores and validated the two FUT2 nsSNPs (G146S and V196G), which may possibly linked with the norovirus susceptibility and gut microbiota changes. Moreover, our findings highlights the value of computational strategies in mutational analysis and welcomes any further experimental validation.

L-type lectin receptor kinases (LECRK) plays a significant role in biotic and abiotic stress response against environmental stimuli in plants. In the Arabidopsis model plant, a total of 45 LECRK was identified but function elucidation is still unresolved. This study carried out a comprehensive analysis of the SNPs associated with L-type lectin protein and how these mutations affect the structure and function of the protein. The computational tools utilized covers both sequence and structure based analysis of the candidate SNPs. The evolutionary analysis identified the conserved residues that are either buried and structurally important or exposed means functionally important hence, can affect the stress response of the protein. A few of the significant mutations are identified M411I, S415C, W431C, A442S, L445F, Q389K, H458Y, and E651V are expected to damage the structure or function of the protein. Among them, the docking studies identified the mutants S415C and W431C as most crucial which can most likely disrupt the protein-protein interactions. Molecular dynamic simulation and principal component analysis further highlights the structural and functional changes in protein resulting by high risks mutations.

Membrane contact sites between organelles are critical for the transfer of biomolecules. Lipid droplets store fatty acids and form contacts with mitochondria, which regulate fatty acid oxidation and adenosine triphosphate production. Protein compartmentalization at lipid droplet-mitochondria contact sites and their effects on biological processes are poorly described. Using proximity-dependent biotinylation methods, we identify 71 proteins at lipid droplet-mitochondria contact sites, including a multimeric complex containing extended synaptotagmin (ESYT) 1, ESYT2, and VAMP Associated Protein B and C (VAPB). High resolution imaging confirms localization of this complex at the interface of lipid droplet-mitochondria-endoplasmic reticulum where it likely transfers fatty acids to enable β-oxidation. Deletion of ESYT1, ESYT2 or VAPB limits lipid droplet-derived fatty acid oxidation, resulting in depletion of tricarboxylic acid cycle metabolites, remodeling of the cellular lipidome, and induction of lipotoxic stress. These findings were recapitulated in Esyt1 and Esyt2 deficient mice. Our study uncovers a fundamental mechanism that is required for lipid droplet-derived fatty acid oxidation and cellular lipid homeostasis, with implications for metabolic diseases and survival.

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

With many amyloidosis-associated missense mutations still unidentified and early diagnostic methods largely unavailable, there is an urgent need for a reliable computational approach to predict hereditary amyloidoses from gene sequencing data. Progress has been made in predicting amyloidosis-triggering sequences within intrinsically disordered regions. However, some diseases are caused by mutations in amyloidogenic regions within structured domains that must unfold for amyloid formation. Accurate prediction of amyloidogenic regions requires tools for detecting amyloidogenicity and assessing mutation effects on protein stability. We developed datasets of mutations linked to hereditary ATTR cardiomyopathy and others likely unrelated, evaluating TTR mutants with amyloidogenicity and stability predictors. Notably, the stability predictors consistently indicated that ATTR-related mutations tend to destabilize the TTR structure more than non-ATTR-associated mutations. Using these datasets and newly generated mutation features, we developed a machine learning model SDAM-TTR to predict mutations leading to ATTR cardiomyopathy.

Ataxia telangiectasia-mutated (ATM) protein kinase, a key player in cellular integrity regulation, is known for its role in DNA damage response. This study investigates the broader impact of ATM on cellular processes and potential clinical manifestations arising from mutations, aiming to expand our understanding of ATM's diverse functions beyond conventional roles. The research employs a comprehensive set of computational techniques for a thorough analysis of ATM mutations. The mutation data are curated from dbSNP and HuVarBase databases. A meticulous assessment is conducted, considering factors such as deleterious effects, protein stability, oncogenic potential, and biophysical characteristics of the identified mutations. Conservation analysis, utilizing diverse computational tools, provides insights into the evolutionary significance of these mutations. Molecular docking and dynamic simulation analyses are carried out for selected mutations, investigating their interactions with Y2080D, AZD0156, and quercetin inhibitors to gauge potential therapeutic implications. Among the 419 mutations scrutinized, five (V1913C, Y2080D, L2656P, C2770G, and C2930G) are identified as both disease causing and protein destabilizing. The study reveals the oncogenic potential of these mutations, supported by findings from the COSMIC database. Notably, Y2080D is associated with haematopoietic and lymphoid cancers, while C2770G shows a correlation with squamous cell carcinomas. Molecular docking and dynamic simulation analyses highlight strong binding affinities of quercetin for Y2080D and AZD0156 for C2770G, suggesting potential therapeutic options. In summary, this computational analysis provides a comprehensive understanding of ATM mutations, revealing their potential implications in cellular integrity and cancer development. The study underscores the significance of Y2080D and C2770G mutations, offering valuable insights for future precision medicine targeting-specific ATM. Despite informative computational analyses, a significant research gap exists, necessitating essential in vitro and in vivo studies to validate the predicted effects of ATM mutations on protein structure and function.

New thermodynamic and functional studies have been recently conducted to evaluate the impact of amino acid substitutions on the Mitogen Activated Protein Kinases 1 and 3 (MAPK1/3). The Critical Assessment of Genome Interpretation (CAGI) data provider, at Sapienza University of Rome, measured the unfolding free energy and the enzymatic activity of a set of variants (MAPK challenge dataset). Thermodynamic measurements for the denaturant-induced equilibrium unfolding of the phosphorylated and unphosphorylated forms of the MAPKs were obtained by monitoring the far-UV circular dichroism and intrinsic fluorescence changes as a function of denaturant concentration. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ). The enzymatic activity of the phosphorylated MAPKs variants was also measured using Chelation-Enhanced Fluorescence to monitor the phosphorylation of a peptide substrate. The MAPK challenge dataset, composed of a total of 23 single amino acid substitutions (11 and 12 for MAPK1 and MAPK3, respectively), was used to assess the effectiveness of the computational methods in predicting the Δ Δ G H 2 O values, associated with the variants, and categorize them as destabilizing and not destabilizing. The data on the enzymatic activity of the MAPKs mutants were used to assess the performance of the methods for predicting the functional impact of the variants. For the sixth edition of CAGI, thirteen independent research groups from four continents (Asia, Australia, Europe and North America) submitted > 80 sets of predictions, obtained from different approaches. In this manuscript, we summarized the results of our assessment to highlight the possible limitations of the available algorithms.

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

Recent thermodynamic and functional studies have been conducted to evaluate the impact of amino acid substitutions on Calmodulin (CaM). The Critical Assessment of Genome Interpretation (CAGI) data provider at University of Verona (Italy) measured the melting temperature (Tm) and the percentage of unfolding (%unfold) of a set of CaM variants (CaM challenge dataset). Thermodynamic measurements for the equilibrium unfolding of CaM were obtained by monitoring far-UV Circular Dichroism as a function of temperature. These measurements were used to determine the Tm and the percentage of protein remaining unfolded at the highest temperature. The CaM challenge dataset, comprising a total of 15 single amino acid substitutions, was used to evaluate the effectiveness of computational methods in predicting the Tm and unfolding percentages associated with the variants, and categorizing them as destabilizing or not. For the sixth edition of CAGI, nine independent research groups from four continents (Asia, Australia, Europe, and North America) submitted over 52 sets of predictions, derived from various approaches. In this manuscript, we summarize the results of our assessment to highlight the potential limitations of current algorithms and provide insights into the future development of more accurate prediction tools. By evaluating the thermodynamic stability of CaM variants, this study aims to enhance our understanding of the relationship between amino acid substitutions and protein stability, ultimately contributing to more accurate predictions of the effects of genetic variants.

Deubiquitinating enzymes function to cleave ubiquitin (Ub) moieties from modified proteins, serving to maintain the pool of free Ub in the cell while simultaneously impacting the fate and function of a target protein. Like all eukaryotes, Plasmodium parasites rely on the dynamic addition and removal of Ub for their own growth and survival. While humans possess around 100 deubiquitinases, Plasmodium contains ∼20 putative Ub hydrolases, many of which bear little to no resemblance to those of other organisms. In this study, we characterize Plasmodium falciparum UBP-1, a large Ub hydrolase unique to Plasmodium spp., which has been linked to endocytosis and drug resistance. We demonstrate its Ub activity, linkage specificity, and assess the repercussions of point mutations associated with drug resistance on catalytic activity and parasite fitness. We confirm that the deubiquitinating activity of UBP-1 is essential for parasite survival, implicating an important role for Ub signaling in endocytosis.

Protein stability is a crucial characteristic that influences both protein activity and structure and plays a significant role in several diseases. Cu/Zn superoxide dismutase 1 (SOD1) mutations serve as a model for elucidating the destabilizing effects on protein folding and misfolding linked to the lethal neurological disease, amyotrophic lateral sclerosis (ALS). In the present study, we have examined the structure and dynamics of the SOD1 protein upon two ALS-associated point mutations at the surface (namely, E49K and R115G), which are located in metal-binding loop IV and Greek key loop VI, respectively. Our analysis was performed through multiple algorithms on the structural characterization of the hSOD1 protein using computational predictions, molecular dynamics (MD) simulations, and experimental studies to understand the effects of amino acid substitutions. Predictive results of computational analysis predicted the deleterious and destabilizing effect of mutants on hSOD1 function and stability. MD outcomes also indicate that the mutations result in structural destabilization by affecting the increased content of β-sheet structures and loss of hydrogen bonds. Moreover, comparative intrinsic and extrinsic fluorescence results of WT-hSOD1 and mutants indicated structural alterations and increased hydrophobic surface pockets, respectively. As well, the existence of β-sheet-dominated structures was observed under amyloidogenic conditions using FTIR spectroscopy. Overall, our findings suggest that mutations in the metal-binding loop IV and Greek key loop VI lead to significant structural and conformational changes that could affect the structure and stability of the hSOD1 molecule, resulting in the formation of toxic intermediate species that cause ALS.

Pyoderma gangrenosum (PG) is an inflammatory skin disorder that belongs to the group of neutrophilic dermatoses. Clinically, it is typified by cutaneous ulcers with distinctive erythematoviolaceous borders and may occur alone or in association with other inflammatory, autoinflammatory, or neoplastic conditions. Although its pathophysiology remains incompletely understood, mounting evidence points toward a predisposing genetic background and dysregulation of both the innate and adaptive immune responses, with follicular or epidermal structures as putative initial targets. To investigate the genetic factors associated with PG susceptibility and severity (arbitrarily defined as unilesional or multilesional), whole-exome sequencing was performed on 11 unrelated patients with PG. Eight carried at least 1 variant of the keratin-encoding genes, including keratin (K)18 gene K18, K20, K23, K32, and K33B. Strikingly, a recurrent variant (rs77999286) of the K18 gene was identified in 5 of 6 patients with multilesional PG and 1 of 5 of those with unilesional PG. AlphaFold modeling and mutation analysis revealed the destabilizing effect of the K18 rs77999286 variant on protein structure. Furthermore, immunohistochemistry revealed undetectable K18 staining in lesional skin compared with that in healthy control skin. Overall, these findings suggest that keratin variants may play a role in PG pathogenesis and indicate that the K18 rs77999286 variant is a potential genetic factor linked to multilesional disease.

Oil degumming process involves the removal of gums, which is required to improve the physicochemical and storage properties of the vegetable oils. Degumming of oils can be carried out by using chemicals, membranes (polymeric, inorganic, and ceramic), or enzymes, for example, phospholipases. Phospholipases are enzymes of tremendous significance in the degumming process as they convert gums to fatty acids and lipophilic substances. They provide a cost-effective and safe alternative to other degumming processes without affecting the oil yield. Lysophospholipases (LPLs) are highly valuable tools for degumming vegetable oils. LPLs can hydrolyze fatty acyl ester bonds of phosphatidylcholine at the sn-1 and sn-2 positions of glycerol moiety. In addition, they have the ability to catalyze hydrolysis lysophospholipids' ester bond either at sn-1 or sn-2 position. In this review, biotechnological production and biochemical characteristics of LPLs from three domains of life are highlighted. In comparison to bacterial and eukaryotic LPLs, archaeal LPLs were found to be active at high temperatures. Broad substrate specificity and thermostability of archaeal LPLs make them ideal candidates for the industrial degumming of oils. However, improvement of activity and substrate specificity of archaeal LPLs is required for enhancing their industrial utility. In the current review, various protein-engineering approaches (directed evolution, rational design, site-saturation mutagenesis, and fusion technology) as well as in silico tools have been discussed to increase the commercial significance of LPLs.

Juvenile polyposis syndrome (JPS) is a rare precancerous condition associated with a high susceptibility to colorectal cancer. The genetic basis of JPS has been reported to lie in germline mutations in BMPR1A or SMAD4, resulting in diverse clinical manifestations and an elusive underlying mechanism. We firstly utilized a 139-gene next-generation sequencing (NGS) panel to detect the germline variants and further employed various prediction tools to assess the pathogenicity and functional alternations. Consequently, we identified a novel pathogenic BMPR1A missense variant (c.355C>T; p.R119C). More importantly, we proposed for the first time that the missense variant would lead to a decrease in molecular weight, potentially associated with reduced protein stability, diminished posttranslational modifications, and aberrant alternative splicing. These findings may provide novel perspectives for further exploration into the role of BMPR1A in JPS development. Also, we hope to encourage clinicians to underscore the importance of genetic testing and analysis in facilitating the diagnosis and treatment of diseases.

Steroid-resistant nephrotic syndrome (SRNS) is a severe kidney disorder linked to over 60 genes, including NUP85, which plays a key role in nuclear pore function and glomerulogenesis. We identified a novel homozygous NUP85 variant (NM_024844.5: c.1379G > A, p.Arg460Gln) in a pediatric SRNS patient who also presented with cleft lip-palate and mild intellectual disability, marking the first reported association of these phenotypes with a NUP85 variant. Molecular dynamics simulations revealed that the variant destabilizes the protein's helix bundle, providing mechanistic insights into its potential pathogenic effects. This study broadens the known phenotypic spectrum of NUP85-related conditions and highlights the value of computational tools, such as molecular dynamics, in unraveling the impact of novel variants.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5-37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke Fahn Marsden's Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at ł months, 1 year and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002 and P = 0.012). At 1 year post-deep brain stimulation, >50% of subjects showed BFMDRS-M and BFMDRS-D improvements of >30%. In the long-term deep brain stimulation cohort (deep brain stimulation inserted for >5 years, n = 8), improvement of >30% was maintained in 5/8 and 3/8 subjects for the BFMDRS-M and BFMDRS-D, respectively. The greatest BFMDRS-M improvements were observed for trunk (53.2%) and cervical (50.5%) dystonia, with less clinical impact on laryngeal dystonia. Improvements in gait dystonia decreased from 20.9% at 1 year to 1ł.2% at last assessment; no patient maintained a fully independent gait. Reduction of BFMDRS-D was maintained for swallowing (52.9%). Five patients developed mild parkinsonism following deep brain stimulation. KMT2B-related disease comprises an expanding continuum from infancy to adulthood, with early evidence of genotype-phenotype correlations. Except for laryngeal dysphonia, deep brain stimulation provides a significant improvement in quality of life and function with sustained clinical benefit depending on symptoms distribution.