SDM: a server for predicting effects of mutations on protein stability

Probing the effects of single point mutations in the GKWWRPS motif on the PNAIG motif within Loop 2 of sclerostin (SOST) using in-silico techniques

Sclerostin (SOST), a Wnt signaling pathway inhibitor, is involved in the pathogenesis of skeletal disorders. This study investigated the impact of the GKWWRPS motif on the PNAIG motif in Loop 2 of SOST, which is accountable for the interactions with the LRP6 protein that triggers the down-regulation of the Wnt signaling pathway. Single amino acid mutations on the GKWWRPS motif, hypothesized to have a probable stabilization effect towards the PNAIG motif, led to a significant reduction in the primary interactions between the SOST and LRP6 proteins. Protein-protein docking and molecular dynamic studies were conducted to investigate the role of the motif. The study found that a solitary mutation in the GKWWRPS motif significantly reduced the primary interactions between SOST and LRP6 proteins, except for probable cold-spot residues. The study's findings establish the GKWWRPS motif as a promising target for therapeutic interventions. Based on the obtained results, it can be inferred that alterations implemented within the GKWWRPS motif could lead to the destabilization of the PNAIG motif, which would directly modulate the interactions between the SOST and LRP6 proteins. The present investigation thus presents novel opportunities in the field of anti-sclerostin interventions.

Unlocking the Door for Precision Medicine in Rare Conditions: Structural and Functional Consequences of Missense ACVR1 Variants

Rare diseases and conditions have thus far received relatively less attention in the field of precision/personalized medicine than common chronic diseases. There is a dire need for orphan drug discovery and therapeutics in ways that are informed by the precision/personalized medicine scholarship. Moreover, people with rare conditions, when considered collectively across diseases worldwide, impact many communities. In this overarching context, Activin A Receptor Type 1 (ACVR1) is a transmembrane kinase from the transforming growth factor-β superfamily and plays a critical role in modulating the bone morphogenetic protein signaling. Missense variants of the ACVR1 gene result in modifications in structure and function and, by extension, abnormalities and have been predominantly linked with two rare conditions: fibrodysplasia ossificans progressiva and diffuse intrinsic pontine glioma. We report here an extensive bioinformatic analyses assessing the pool of 50,951 variants and forecast seven highly destabilizing mutations (R206H, G356D, R258S, G328W, G328E, R375P, and R202I) that can significantly alter the structure and function of the native protein. Protein-protein interaction and ConSurf analyses revealed the crucial interactions and localization of highly deleterious mutations in highly conserved domains that may impact the binding and functioning of the protein. cBioPortal, CanSAR Black, and existing literature affirmed the association of these destabilizing mutations with posterior fossa ependymoma, uterine corpus carcinoma, and pediatric brain cancer. The current findings suggest these deleterious nonsynonymous single nucleotide polymorphisms as potential candidates for future functional annotations and validations associated with rare conditions, further aiding the development of precision medicine in rare diseases.

Structural and biochemical investigation into stable FGF2 mutants with novel mutation sites and hydrophobic replacements for surface-exposed cysteines

Fibroblast growth factor 2 (FGF2) is an attractive biomaterial for pharmaceuticals and functional cosmetics. To improve the thermo-stability of FGF2, we designed two mutants harboring four-point mutations: FGF2-M1 (D28E/C78L/C96I/S137P) and FGF2-M2 (D28E/C78I/C96I/S137P) through bioinformatics, molecular thermodynamics, and molecular modeling. The D28E mutation reduced fragmentation of the FGF2 wild type during preparation, and the substitution of a whale-specific amino acid, S137P, enhanced the thermal stability of FGF2. Surface-exposed cysteines that participate in oligomerization through intermolecular disulfide bond formation were substituted with hydrophobic residues (C78L/C78I and C96I) using the in silico method. High-resolution crystal structures revealed at the atomic level that the introduction of mutations stabilizes each local region by forming more favorable interactions with neighboring residues. In particular, P137 forms CH-π interactions with the side chain indole ring of W123, which seems to stabilize a β-hairpin structure, containing a heparin-binding site of FGF2. Compared to the wild type, both FGF2-M1 and FGF2-M2 maintained greater solubility after a week at 45 °C, with their Tm values rising by ~ 5 °C. Furthermore, the duration for FGF2-M1 and FGF2-M2 to reach 50% residual activity at 45 °C extended to 8.8- and 8.2-fold longer, respectively, than that of the wild type. Interestingly, the hydrophobic substitution of surface-exposed cysteine in both FGF2 mutants makes them more resistant to proteolytic cleavage by trypsin, subtilisin, proteinase K, and actinase than the wild type and the Cys → Ser substitution. The hydrophobic replacements can influence protease resistance as well as oligomerization and thermal stability. It is notable that hydrophobic substitutions of surface-exposed cysteines, as well as D28E and S137P of the FGF2 mutants, were designed through various approaches with structural implications. Therefore, the engineering strategies and structural insights adopted in this study could be applied to improve the stability of other proteins.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Comparative Atlas of SARS-CoV-2 Substitution Mutations: A Focus on Iranian Strains Amidst Global Trends

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19.

METHODS

we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference.

RESULTS

Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran.

CONCLUSIONS

real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments.

Molecular insights into TP53 mutation (p. Arg267Trp) and its connection to Choroid Plexus Carcinomas and Li-Fraumeni Syndrome

BACKGROUND

Choroid plexus carcinomas (CPCs) are rare malignant tumors primarily affecting pediatric patients and often co-occur with Li-Fraumeni Syndrome (LFS), an inherited predisposition to early-onset malignancies in multiple organ systems. LFS is closely linked to TP53 mutations, with germline TP53 gene mutations present in approximately 75% of Li-Fraumeni syndrome families and 25% of Li-Fraumeni-like syndrome families. Individuals with TP53 mutations also have an elevated probability of carrying mutations in BRCA1 and BRCA2 genes.

OBJECTIVE

To investigate the structural and functional implications of the TP53: 799C > T, p. (Arg267Trp) missense mutation, initially identified in a Saudi family, and understand its impact on TP53 functionality and related intermolecular interactions.

METHODS

Computational analyses were conducted to examine the structural modifications resulting from the TP53: 799C > T, p. (Arg267Trp) mutation. These analyses focused on the mutation's impact on hydrogen bonding, ionic interactions, and the specific interaction with Cell Cycle and Apoptosis Regulator 2 (CCAR2), as annotated in UniProt.

RESULTS

The study revealed that the native Arg267 residue is critical for a salt bridge interaction with glutamic acid at position 258. The mutation-induced charge alteration has the potential to disrupt this ionic bonding. Additionally, the mutation is located within an amino acid region crucial for interaction with CCAR2. The altered properties of the amino acid within this domain may affect its functionality and disrupt this interaction, thereby impacting the regulation of catalytic enzyme activity.

CONCLUSIONS

Our findings highlight the intricate intermolecular interactions governing TP53 functionality. The TP53: 799C > T, p. (Arg267Trp) mutation causes structural modifications that potentially disrupt critical ionic bonds and protein interactions, offering valuable insights for the development of targeted mutants with distinct functional attributes. These insights could inform therapeutic strategies for conditions associated with TP53 mutations.

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

MOTIVATION

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.

RESULTS

We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.

AVAILABILITY AND IMPLEMENTATION

Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.

SUPPLEMENTARY INFORMATION

Supplementary Information is available at Bioinformatics online.

MutationExplorer: a webserver for mutation of proteins and 3D visualization of energetic impacts

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorer strives for efficiency in user experience. For example, we have prepared 45 000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/.

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic Variant Impact

The ability of freely available in silico tools to predict the effect of non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes on protein function is not well defined. We assessed the performance of seven sequence-based (SIFT, PolyPhen2, mutation accessor, FATHMM, PhD-SNP, MutPred2, and SNPs & Go) and five structure-based (mCSM, SDM, DDGun, CupSat, and MAESTROweb) tools in predicting the impact of 118 nsSNPs in the CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known function (24 normal, one increased, 42 decreased, and 51 no-function). Sequence-based tools had a higher median (IQR) positive predictive value (89% [89-94%] vs. 12% [10-15%], P < 0.001) and lower negative predictive value (30% [24-34%] vs. 90% [80-93%], P < 0.001) than structure-based tools. Accuracy did not significantly differ between sequence-based (59% [37-67%]) and structure-based (34% [23-44%]) tools (P = 0.070). Notably, the no-function CYP2C9*3 allele and decreased function CYP2C9*8 allele were predicted incorrectly as tolerated by 100% of sequenced-based tools and as stabilizing by 60% and 20% of structure-based tools, respectively. As a case study, we performed mutational analysis for the CYP2C9*1, *3 (I359L), and *8 (R150H) proteins through molecular dynamic (MD) simulations using S-warfarin as the substrate. The I359L variant increased the distance of the major metabolic site of S-warfarin to the oxy-ferryl center of CYP2C9, and I359L and R150H caused shifts in the conformation of S-warfarin to a position less favorable for metabolism. These data suggest that MD simulations may better capture the impact of nsSNPs in pharmacogenes than other tools.

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

Engineering G protein-coupled receptors for stabilization

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

Unraveling the genetic variations underlying virulence disparities among SARS-CoV-2 strains across global regions: insights from Pakistan

Over the course of the COVID-19 pandemic, several SARS-CoV-2 variants have emerged that may exhibit different etiological effects such as enhanced transmissibility and infectivity. However, genetic variations that reduce virulence and deteriorate viral fitness have not yet been thoroughly investigated. The present study sought to evaluate the effects of viral genetic makeup on COVID-19 epidemiology in Pakistan, where the infectivity and mortality rate was comparatively lower than other countries during the first pandemic wave. For this purpose, we focused on the comparative analyses of 7096 amino-acid long polyprotein pp1ab. Comparative sequence analysis of 203 SARS-CoV-2 genomes, sampled from Pakistan during the first wave of the pandemic revealed 179 amino acid substitutions in pp1ab. Within this set, 38 substitutions were identified within the Nsp3 region of the pp1ab polyprotein. Structural and biophysical analysis of proteins revealed that amino acid variations within Nsp3's macrodomains induced conformational changes and modified protein-ligand interactions, consequently diminishing the virulence and fitness of SARS-CoV-2. Additionally, the epistatic effects resulting from evolutionary substitutions in SARS-CoV-2 proteins may have unnoticed implications for reducing disease burden. In light of these findings, further characterization of such deleterious SARS-CoV-2 mutations will not only aid in identifying potential therapeutic targets but will also provide a roadmap for maintaining vigilance against the genetic variability of diverse SARS-CoV-2 strains circulating globally. Furthermore, these insights empower us to more effectively manage and respond to potential viral-based pandemic outbreaks of a similar nature in the future.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of -242 kcal/mol for wild-type, while the mutant complexes had scores ranging from -217 to -220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was -64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were -45.90 kcal/mol, -53.52 kcal/mol, -55.67 kcal/mol, and -61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Three-Dimensional Structural Stability and Local Electrostatic Potential at Point Mutations in Spike Protein of SARS-CoV-2 Coronavirus

The contagiousness of SARS-CoV-2 β-coronavirus is determined by the virus-receptor electrostatic association of its positively charged spike (S) protein with the negatively charged angiotensin converting enzyme-2 (ACE2 receptor) of the epithelial cells. If some mutations occur, the electrostatic potential on the surface of the receptor-binding domain (RBD) could be altered, and the S-ACE2 association could become stronger or weaker. The aim of the current research is to investigate whether point mutations can noticeably alter the electrostatic potential on the RBD and the 3D stability of the S1-subunit of the S-protein. For this purpose, 15 mutants with different hydrophilicity and electric charge (positive, negative, or uncharged) of the substituted and substituting amino acid residues, located on the RBD at the S1-ACE2 interface, are selected, and the 3D structure of the S1-subunit is reconstructed on the base of the crystallographic structure of the S-protein of the wild-type strain and the amino acid sequence of the unfolded polypeptide chain of the mutants. Then, the Gibbs free energy of folding, isoelectric point, and pH-dependent surface electrostatic potential of the S1-subunit are computed using programs for protein electrostatics. The results show alterations in the local electrostatic potential in the vicinity of the mutant amino acid residue, which can influence the S-ACE2 association. This approach allows prediction of the relative infectivity, transmissibility, and contagiousness (at equal social immune status) of new SARS-CoV-2 mutants by reconstruction of the 3D structure of the S1-subunit and calculation of the surface electrostatic potential.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

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.

OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures

Determining changes in the protein's thermal stability following mutations is critical in protein engineering and understanding pathogenic missense mutations. Despite the development of various computational methods to predict the effects of single-point mutations, their accuracy remains limited. In this study, we propose a new computational method, OmeDDG, that more accurately predicts mutation-induced Gibbs free energy changes in protein folding (ΔΔG). OmeDDG takes the sequences of wild-type and mutant proteins as input, utilizes OmegaFold to obtain the 3D structure, employs a convolutional neural network to extract structural features, and combines them with protein mutation features and pretraining features to predict the stability of single-point mutations in proteins. We performed a comprehensive comparison between OmeDDG and other available prediction methods on four blind test datasets, confirming that OmeDDG can effectively enhance protein mutation prediction performance. Notably, on the antisymmetric dataset Ssym, OmeDDG achieves the best performance, demonstrating favorable antisymmetry with PCC = 0.79 and RMSE = 0.96 for forward mutations and PCC = 0.77 and RMSE = 0.97 for reverse mutant types.

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

The orexin/hypocretin receptor type 1 (OX1R) plays a crucial role in regulating various physiological functions, especially feeding behavior, addiction, and reward. Genetic variations in the OX1R have been associated with several neurological disorders. In this study, we utilized a combination of sequence and structure-based computational tools to identify the most deleterious missense single nucleotide polymorphisms (SNPs) in the OX1R gene. Our findings revealed four highly conserved and structurally destabilizing missense SNPs, namely R144C, I148N, S172W, and A297D, located in the GTP-binding domain. Molecular dynamics simulations analysis demonstrated that all four most detrimental mutant proteins altered the overall structural flexibility and dynamics of OX1R protein, resulting in significant changes in the structural organization and motion of the protein. These findings provide valuable insights into the impact of missense SNPs on OX1R function loss and their potential contribution to the development of neurological disorders, thereby guiding future research in this field.

Coevolution-based computational approach to detect resistance mechanism of epidermal growth factor receptor

Tyrosine kinase epidermal growth factor receptor (EGFR) correlates the neoplastic cell metastasis, angiogenesis, neoplastic incursion, and apoptosis. Due to the involvement of EGFR in these biological processes, it becomes a most potent target for treating non-small cell lung cancer (NSCLC). The tyrosine kinase inhibitors (TKI) have endorsed high efficacy and anticipation to patients but unfortunately, within a year of treatment, drug targets develop resistance due to mutations. The present study detected the compensatory mutations in EGFR to know the evolutionary mechanism of drug resistance. The results of this study demonstrate that compensatory mutations enlarge the drug-binding pocket which may lead to the altered orientation of the ligand (gefitinib and erlotinib) causing drug resistance. This indicates that coevolutionary forces play a significant role in fine-tuning the structure of EGFR protein against the drugs. The analysis provides insight into the evolution-induced structural aspects of drug resistance changes in EGFR which in turn be useful in designing drugs with better efficacy.

Lipid and cholesterols modulate the dynamics of SARS-CoV-2 viral ion channel ORF3a and its pathogenic variants

SARS-CoV-2 accessory protein, ORF3a is a putative ion channel which immensely contributes to viral pathogenicity by modulating host immune responses and virus-host interactions. Relatively high expression of ORF3a in diseased individuals and implication with inflammasome activation, apoptosis and autophagy inhibition, ratifies as an effective target for developing vaccines and therapeutics. Herein, we present the elusive dynamics of ORF3a-dimeric state using all-atoms molecular dynamics (MD) simulations at μ-seconds scale in a heterogeneous lipid-mimetic system in multiple replicates. Additionally, we also explore the effect of non-synonymous pathogenic mutations on ORF3a ion channel activity and viral pathogenicity in different SARS-CoV-2 variants using various structure-based protein stability (ΔΔG) tools and computational saturation mutagenesis. Our study ascertains the role of phosphatidylcholines and cholesterol in modulating the structure of ORF3a, which perturbs the size and flexibility of the polar cavity that allows permeation of large cations. Discrete trend in ion channel pore radius and area per lipid arises the premise that presence of lipids might also affect the overall conformation of ORF3a. MD structural-ensembles, in some replicates rationalize the crucial role of TM2 in maintaining the native structure of ORF3a. We also infer that loss of structural stability primarily grounds for pathogenicity in more than half of the pathogenic variants of ORF3a. Overall, the effect of mutation on alteration of ion permeability of ORF3a, proposed in this study brings mechanistic insights into variant consequences on viral membrane proteins of SARS-CoV-2, which can be utilized for the development of novel therapeutics to treat COVID-19 and other coronavirus diseases.

Unveiling clinically significant PPARγ mutations for thiazolidinedione treatment responsiveness through atomistic simulations

In Type 2 diabetes, increased insulin sensitivity is induced by thiazolidinedione activation of the peroxisome proliferator-activated receptor gamma (PPARγ). Recent data indicate a relationship between SNPs in PPARγ and poor drug response. Therefore, understanding the pathogenic consequences of mutations in PPARγ-mediated protein-drug interactions will be prima-facie for establishing personalized medicine. The PPARG gene has 197 missense SNPs, 22 of which were determined to be both deleterious and destabilizing, employing in silico approaches. Molecular docking analysis suggested that the mutation influenced the binding energy of at least seven of the variants. The mutant R316H was identified as the most damaging and deleterious from the observed results. For a better understanding of the dynamic variation upon mutation at the atomic level, molecular dynamics simulations of the wild-type and R316H mutant PPARγ structure were performed. The analysis indicates that the mutation increased protein structural compactness while decreasing flexibility. The reduced dynamics in the mutant structure was further validated by principal component analysis. This mechanistic evaluation of the PPARγ protein variants provides insight into the relationship between genetic variation and interindividual variability of drug responsiveness and will facilitate the future studies for the development of tailored treatment regime for precision medicine.

Molecular docking and MD simulations reveal protease inhibitors block the catalytic residues in Prp8 intein of Aspergillus fumigatus: a potential target for antimycotics

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.Communicated by Ramaswamy H. Sarma.

Structural Evolution of Primate Glutamate Dehydrogenase 2 as Revealed by In Silico Predictions and Experimentally Determined Structures

Glutamate dehydrogenase (GDH) interconverts glutamate to a-ketoglutarate and ammonia, interconnecting amino acid and carbohydrate metabolism. In humans, two functional GDH genes, GLUD1 and GLUD2, encode for hGDH1 and hGDH2, respectively. GLUD2 evolved from retrotransposition of the GLUD1 gene in the common ancestor of modern apes. These two isoenzymes are involved in the pathophysiology of human metabolic, neoplastic, and neurodegenerative disorders. The 3D structures of hGDH1 and hGDH2 have been experimentally determined; however, no information is available about the path of GDH2 structure changes during primate evolution. Here, we compare the structures predicted by the AlphaFold Colab method for the GDH2 enzyme of modern apes and their extinct primate ancestors. Also, we analyze the individual effect of amino acid substitutions emerging during primate evolution. Our most important finding is that the predicted structure of GDH2 in the common ancestor of apes was the steppingstone for the structural evolution of primate GDH2s. Two changes with a strong functional impact occurring at the first evolutionary step, Arg443Ser and Gly456Ala, had a destabilizing and stabilizing effect, respectively, making this step the most important one. Subsequently, GDH2 underwent additional modifications that fine-tuned its enzymatic properties to adapt to the functional needs of modern-day primate tissues.

Novel pathogenic WHRN variant causing hearing loss in a moroccan family

OBJECTIVES

The most prevalent sensory disease in humans is deafness. A variety of genes have been linked to hearing loss, which can either be isolated (non-syndromic) or associated with lesions in other organs (syndromic). It has been discovered that WHRN variants are responsible for non-syndromic hearing loss and Usher syndrome type II.

METHODS AND RESULTS

Exome sequencing in a consanguineous Moroccan patient with severe hearing loss identified a single homozygous mutation c.619G > T; p.Ala207Ser in WHRN, encoding a cytoskeletal scaffold protein that binds membrane protein complexes to the cytoskeleton in ocular photoreceptors and ear hair cell stereocilia. Bioinformatics methods and molecular dynamic modeling were able to predict the pathogenic implications of this variation.

CONCLUSION

We used whole exome sequencing to find a homozygous WHRN gene variant in a Moroccan family. Numerous bioinformatics methods predict that this modification might result in a change in the WHRN protein's structure.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

J, Ahmad SF, Attia SM, Emran TB, Patil RB, Ahmed SSSJ. Structural Characteristics of PON1 with Leu55Met and Gln192Arg Variants Influencing Oxidative-Stress-Related Diseases: An Integrated Molecular Modeling and Dynamics Study

Background and Objectives: PON1 is a multi-functional antioxidant protein that hydrolyzes a variety of endogenous and exogenous substrates in the human system. Growing evidence suggests that the Leu55Met and Gln192Arg substitutions alter PON1 activity and are linked with a variety of oxidative-stress-related diseases. Materials and Methods: We implemented structural modeling and molecular dynamics (MD) simulation along with essential dynamics of PON1 and molecular docking with their endogenous (n = 4) and exogenous (n = 6) substrates to gain insights into conformational changes and binding affinity in order to characterize the specific functional ramifications of PON1 variants. Results: The Leu55Met variation had a higher root mean square deviation (0.249 nm) than the wild type (0.216 nm) and Gln192Arg (0.202 nm), implying increased protein flexibility. Furthermore, the essential dynamics analysis confirms the structural change in PON1 with Leu55Met vs. Gln192Arg and wild type. Additionally, PON1 with Leu55Met causes local conformational alterations at the substrate binding site, leading to changes in binding affinity with their substrates. Conclusions: Our findings highlight the structural consequences of the variants, which would increase understanding of the role of PON1 in the pathogenesis of oxidative-stress-related diseases, as well as the management of endogenous and exogenous chemicals in the treatment of diseases.

Characterization on the oncogenic effect of the missense mutations of p53 via machine learning

Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.

Functional Characterization of Novel MC4R Variants Identified in Two Unrelated Patients with Morbid Obesity in Qatar

The leptin-melanocortin pathway is pivotal in appetite and energy homeostasis. Pathogenic variants in genes involved in this pathway lead to severe early-onset monogenic obesity (MO). The MC4R gene plays a central role in leptin-melanocortin signaling, and heterozygous variants in this gene are the most common cause of MO. A targeted gene panel consisting of 52 obesity-related genes was used to screen for variants associated with obesity. Variants were analyzed and filtered to identify potential disease-causing activity and validated using Sanger sequencing. We identified two novel heterozygous variants, c.253A>G p.Ser85Gly and c.802T>C p.Tyr268His, in the MC4R gene in two unrelated patients with morbid obesity and evaluated the functional impact of these variants. The impact of the variants on the MC4R gene was assessed using in silico prediction tools and molecular dynamics simulation. To further study the pathogenicity of the identified variants, GT1-7 cells were transfected with plasmid DNA encoding either wild-type or mutant MC4R variants. The effects of allelic variations in the MC4R gene on cAMP synthesis, MC4R protein level, and activation of PKA, ERB, and CREB signaling pathways in both stimulated and unstimulated ɑ-MSH paradigms were determined for their functional implications. In silico analysis suggested that the variants destabilized the MC4R structure and affected the overall dynamics of the MC4R protein, possibly leading to intracellular receptor retention. In vitro analysis of the functional impact of these variants showed a significant reduction in cell surface receptor expression and impaired extracellular ligand binding activity, leading to reduced cAMP production. Our analysis shows that the variants do not affect total protein expression; however, they are predicted to affect the post-translational localization of the MC4R protein to the cell surface and impair downstream signaling cascades such as PKA, ERK, and CREB signaling pathways. This finding might help our patients to benefit from the novel therapeutic advances for monogenic forms of obesity.

Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets

Accurate prediction of protein stability upon a point mutation has important applications in drug discovery and personalized medicine. It remains a challenging issue in computational biology. Existing computational prediction methods, which range from mechanistic to supervised learning approaches, have experienced limited progress over the last few decades. This stagnation is largely due to their heavy reliance on both the quantity and quality of the training data. This is evident in recent state-of-the-art methods that continue to yield substantial errors on two challenging blind test sets: frataxin and p53, with average root-mean-square errors exceeding 3 and 1.5 kcal/mol, respectively, which is still above the theoretical 1 kcal/mol prediction barrier. Rigorous approaches, on the other hand, offer greater potential for accuracy without relying on training data but are computationally demanding and require both wild-type and mutant structure information. Although they showed high accuracy for conserving mutations, their performance is still limited for charge-changing mutation cases. This might be due to the lack of an available mutant structure, often represented by a simplified capped peptide. The recent advances in protein structure prediction methods now make it possible to obtain structures comparable to experimental ones, including complete mutant structure information. In this work, we compare the performance of supervised learning-based methods and rigorous approaches for predicting protein stability on point mutations in difficult targets: frataxin and p53. The rigorous alchemical method significantly surpasses state-of-the-art techniques in terms of both the root-mean-squared error and Pearson correlation coefficient in these two challenging blind test sets. Additionally, we propose an improved alchemical method that employs the pmx double-system/single-box approach to accurately predict the folding free energy change upon both conserving and charge-changing mutations. The enhanced protocol can accurately predict both types of mutations, thereby outperforming existing state-of-the-art methods in overall performance.

Novel association of Dandy-Walker malformation with CAPN15 variants expands the phenotype of oculogastrointestinal neurodevelopmental syndrome

Oculogastrointestinal neurodevelopmental syndrome has been described in seven previously published individuals who harbor biallelic pathogenic variants in the CAPN15 gene. Biallelic missense variants have been reported to demonstrate a phenotype of eye abnormalities and developmental delay, while biallelic loss of function variants exhibit phenotypes including microcephaly and craniofacial abnormalities, cardiac and genitourinary malformations, and abnormal neurologic activity. We report six individuals from three unrelated families harboring biallelic deleterious variants in CAPN15 with phenotypes overlapping those previously described for this disorder. Of the individuals affected, four demonstrate radiographic evidence of the classical triad of Dandy-Walker malformation including hypoplastic vermis, fourth ventricle enlargement, and torcular elevation. Cerebellar anomalies have not been previously reported in association with CAPN15-related disease. Here, we present three unrelated families with findings consistent with oculogastrointestinal neurodevelopmental syndrome and cerebellar pathology including Dandy-Walker malformation. To corroborate these novel clinical findings, we present supporting data from the mouse model suggesting an important role for this protein in normal cerebellar development. Our findings add six molecularly confirmed cases to the literature and additionally establish a new association of Dandy-Walker malformation with biallelic CAPN15 variants, thereby expanding the neurologic spectrum among patients affected by CAPN15-related disease.

PROSTATA: a framework for protein stability assessment using transformers

MOTIVATION

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.

RESULTS

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

AVAILABILITY AND IMPLEMENTATION

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

Comprehensive analysis of mitochondrial DNA variants, mitochondrial DNA copy number and oxidative damage in psoriatic arthritis

Growing evidence suggests that abnormalities in mitochondrial DNA (mtDNA) are involved in the pathogenesis of various inflammatory and immuno-mediated diseases. The present study analysed the entire mitochondrial genome by next-generation sequencing (NGS) in 23 patients with psoriatic arthritis (PsA) and 20 healthy controls to identify PsA-related variants. Changes in mtDNA copy number (mtDNAcn) were also evaluated by quantitative polymerase chain reaction (qPCR) and mtDNA oxidative damage was measured using an 8-hydroxy-2'-deoxyguanosine assay. NGS analysis revealed a total of 435 variants including 187 in patients with PsA only and 122 in controls only. Additionally, 126 common variants were found, of which 2 variants differed significantly in their frequencies among patients and controls (P<0.05), and may be associated with susceptibility to PsA. A total of 33 missense variants in mtDNA-encoded genes for complexes I, III, IV and V were identified only in patients with PsA. Of them, 25 variants were predicted to be deleterious by affecting the functions and structures of encoded proteins, and 13 variants were predicted to affect protein's stability. mtDNAcn analysis revealed decreased mtDNA content in patients with PsA compared with controls (P=0.0001) but the decrease in mtDNAcn was not correlated with patients' age or inflammatory biomarkers (P>0.05). Moreover, a higher level of oxidative damage was observed in patients with PsA compared with controls (P=0.03). The results of the present comprehensive analysis of mtDNA in PsA revealed that certain mtDNA variants may be implicated in the predisposition/pathogenesis of PsA, highlighting the importance of NGS in the identification of mtDNA variants in PsA. The current results also demonstrated that decreased mtDNAcn in PsA may be a consequence of increased oxidative stress. These data provide valuable insights into the contribution of mtDNA defects to the pathogenesis of PsA. Additional studies in larger cohorts are needed to elucidate the role of mtDNA defects in PsA.

SARS-CoV-2 main protease mutation analysis via a kinematic method

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is the virus responsible for the COVID-19 pandemic. COVID-19 continues to cause millions of deaths globally in part due to immune-evading mutations. SARS-CoV-2 main protease (Mpro) is an important enzyme for viral replication and potentially an effective drug target. Mutations affect the dynamics of enzymes and thereby their activity and ability to bind ligands. Here, we use kinematic flexibility analysis (KFA) to identify how mutations and ligand binding changes the conformational flexibility of Mpro. KFA decomposes macromolecules into regions of different flexibility near-instantly from a static structure, allowing conformational dynamics analysis at scale. Altogether, we analyzed 47 mutation sites across 69 Mpro-ligand complexes resulting in more than 3300 different structures which includes 69 mutated structures with all 47 sites mutated simultaneously and 3243 single residue mutated structures. We found that mutations generally increased the conformational flexibility of the protein. Understanding the impact of mutations on the flexibility of Mpro is essential for identifying potential drug targets in the treatment of SARS-CoV-2. Further studies in this area can offer valuable insights into the mechanisms of molecular recognition.

Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction

MOTIVATION

Proteins play crucial roles in biological processes, with their functions being closely tied to thermodynamic stability. However, measuring stability changes upon point mutations of amino acid residues using physical methods can be time-consuming. In recent years, several computational methods for protein thermodynamic stability prediction (PTSP) based on deep learning have emerged. Nevertheless, these approaches either overlook the natural topology of protein structures or neglect the inherent noisy samples resulting from theoretical calculation or experimental errors.

RESULTS

We propose a novel Global-Local Graph Neural Network powered by Unbiased Curriculum Learning for the PTSP task. Our method first builds a Siamese graph neural network to extract protein features before and after mutation. Since the graph's topological changes stem from local node mutations, we design a local feature transformation module to make the model focus on the mutated site. To address model bias caused by noisy samples, which represent unavoidable errors from physical experiments, we introduce an unbiased curriculum learning method. This approach effectively identifies and re-weights noisy samples during the training process. Extensive experiments demonstrate that our proposed method outperforms advanced protein stability prediction methods, and surpasses state-of-the-art learning methods for regression prediction tasks.

AVAILABILITY AND IMPLEMENTATION

All code and data is available at https://github.com/haifangong/UCL-GLGNN.

Comprehensive Genotyping and Phenotyping Analysis of GUCY2D-Associated Rod- and Cone-Dominated Dystrophies

PURPOSE

To describe the genetic and clinical spectrum of GUCY2D-associated retinopathies and to accurately establish their prevalence in a large cohort of patients.

DESIGN

Retrospective case series.

METHODS

Institutional study of 47 patients from 27 unrelated families with retinal dystrophies carrying disease-causing GUCY2D variants from the Fundación Jiménez Díaz hospital dataset of 8000 patients. Patients underwent ophthalmological examination and molecular testing by Sanger or exome sequencing approaches. Statistical and principal component analyses were performed to determine genotype-phenotype correlations.

RESULTS

Four clinically different associated phenotypes were identified: 66.7% of families with cone/cone-rod dystrophy, 22.2% with Leber congenital amaurosis, 7.4% with early-onset retinitis pigmentosa, and 3.7% with congenital night blindness. Twenty-three disease-causing GUCY2D variants were identified, including 6 novel variants. Biallelic variants accounted for 28% of patients, whereas most carried dominant alleles associated with cone/cone-rod dystrophy. The disease onset had statistically significant differences according to the functional variant effect. Patients carrying GUCY2D variants were projected into 3 subgroups by allelic combination, disease onset, and presence of nystagmus or night blindness. In contrast to patients with the most severe phenotype of Leber congenital amaurosis, 7 patients with biallelic GUCY2D had a later and milder rod form with night blindness in infancy as the first symptom.

CONCLUSIONS

This study represents the largest GUCY2D cohort in which 4 distinctly different phenotypes were identified, including rare intermediate presentations of rod-dominated retinopathies. We established that GUCY2D is linked to about 1% of approximately 3000 molecularly characterized families of our cohort. All of these findings are critical for defining cohorts for inclusion in future clinical trials.

Functional and structural impact of 10 ACADM missense mutations on human medium chain acyl-Coa dehydrogenase

Medium chain acyl-CoA dehydrogenase (MCAD) deficiency (MCADD) is associated with ACADM gene mutations, leading to an impaired function and/or structure of MCAD. Importantly, after import into the mitochondria, MCAD must incorporate a molecule of flavin adenine dinucleotide (FAD) per subunit and assemble into tetramers. However, the effect of MCAD amino acid substitutions on FAD incorporation has not been investigated. Herein, the commonest MCAD variant (p.K304E) and 11 additional rare variants (p.Y48C, p.R55G, p.A88P, p.Y133C, p.A140T, p.D143V, p.G224R, p.L238F, p.V264I, p.Y372N, and p.G377V) were functionally and structurally characterized. Half of the studied variants presented a FAD content <65 % compared to the wild-type. Most of them were recovered as tetramers, except the p.Y372N (mainly as dimers). No correlation was found between the levels of tetramers and FAD content. However, a correlation between FAD content and the cofactor's affinity, proteolytic stability, thermostability, and thermal inactivation was established. We showed that the studied amino acid changes in MCAD may alter the substrate chain-length dependence and the interaction with electron-transferring-flavoprotein (ETF) necessary for a proper functioning electron transfer thus adding additional layers of complexity to the pathological effect of ACADM missense mutations. Although the majority of the variant MCADs presented an impaired capacity to retain FAD during their synthesis, some of them were structurally rescued by cofactor supplementation, suggesting that in the mitochondrial environment the levels and activity of those variants may be dependent of FAD's availability thus contributing for the heterogeneity of the MCADD phenotype found in patients presenting the same genotype.

Improving the thermostability of a novel PL-6 family alginate lyase by rational design engineering for industrial preparation of alginate oligosaccharides

Alginate is degraded into alginate oligosaccharides with various biological activities by enzymes. However, the thermostability of the enzyme limits its industrial application. In this study, a novel PL-6 alginate lyase, AlyRm6A from Rhodothermus marinus 4252 was expressed and characterized. In addition, an efficient comprehensive strategy was proposed, including automatic design of heat-resistant mutants, multiple computer-aided ΔΔGfold value calculation, and conservative analysis of mutation sites. AlyRm6A has naturally high thermostability. Compared with the WT, T43I and Q216I kept their original activities, and their half-lives were increased from 3.68 h to 4.29 h and 4.54 h, melting point temperatures increased from 61.5 °C to 62.9 °C and 63.5 °C, respectively. The results of circular dichroism showed that both the mutants and the wild type had the characteristic peaks of β-sheet at 195 nm and 216 nm, which indicated that there was no significant effect on the secondary structure of the protein. Molecular dynamics simulation (MD) analyses suggest that the enhancement of the hydrophobic interaction network, improvement of molecular rigidity, and denser structure could improve the stability of AlyRm6A. To the best of our knowledge, our findings indicate that AlyRm6A mutants exhibit the highest thermostability among the characterized PL-6 alginate lyases, making them potential candidates for industrial production of alginate oligosaccharides.

Uncovering the Molecular Drivers of NHEJ DNA Repair-Implicated Missense Variants and Their Functional Consequences

Variants in non-homologous end joining (NHEJ) DNA repair genes are associated with various human syndromes, including microcephaly, growth delay, Fanconi anemia, and different hereditary cancers. However, very little has been done previously to systematically record the underlying molecular consequences of NHEJ variants and their link to phenotypic outcomes. In this study, a list of over 2983 missense variants of the principal components of the NHEJ system, including DNA Ligase IV, DNA-PKcs, Ku70/80 and XRCC4, reported in the clinical literature, was initially collected. The molecular consequences of variants were evaluated using in silico biophysical tools to quantitatively assess their impact on protein folding, dynamics, stability, and interactions. Cancer-causing and population variants within these NHEJ factors were statistically analyzed to identify molecular drivers. A comprehensive catalog of NHEJ variants from genes known to be mutated in cancer was curated, providing a resource for better understanding their role and molecular mechanisms in diseases. The variant analysis highlighted different molecular drivers among the distinct proteins, where cancer-driving variants in anchor proteins, such as Ku70/80, were more likely to affect key protein-protein interactions, whilst those in the enzymatic components, such as DNA-PKcs, were likely to be found in intolerant regions undergoing purifying selection. We believe that the information acquired in our database will be a powerful resource to better understand the role of non-homologous end-joining DNA repair in genetic disorders, and will serve as a source to inspire other investigations to understand the disease further, vital for the development of improved therapeutic strategies.

Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits

BACKGROUND

Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers.

RESULTS

A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT.

CONCLUSIONS

According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.

Omicron Coronavirus: pH-Dependent Electrostatic Potential and Energy of Association of Spike Protein to ACE2 Receptor

The association of the S-protein of the SARS-CoV-2 beta coronavirus to ACE2 receptors of the human epithelial cells determines its contagiousness and pathogenicity. We computed the pH-dependent electric potential on the surface of the interacting globular proteins and pH-dependent Gibbs free energy at the association of the wild-type strain and the omicron variant. The calculated isoelectric points of the ACE2 receptor (pI 5.4) and the S-protein in trimeric form (pI 7.3, wild type), (pI 7.8, omicron variant), experimentally verified by isoelectric focusing, show that at pH 6-7, the S1-ACE2 association is conditioned by electrostatic attraction of the oppositely charged receptor and viral protein. The comparison of the local electrostatic potentials of the omicron variant and the wild-type strain shows that the point mutations alter the electrostatic potential in a relatively small area on the surface of the receptor-binding domain (RBD) of the S1 subunit. The appearance of seven charge-changing point mutations in RBD (equivalent to three additional positive charges) leads to a stronger S1-ACE2 association at pH 5.5 (typical for the respiratory tract) and a weaker one at pH 7.4 (characteristic of the blood plasma); this reveals the reason for the higher contagiousness but lower pathogenicity of the omicron variant in comparison to the wild-type strain.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods

BACKGROUND

Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants.

AIMS

Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations.

MATERIALS AND METHODS

We employed a set of computational tools to predict the pathogenicity of over forty-two thousand mutations and deposited the kinase-wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]).

RESULTS

Mutations are more likely to be drivers when being present in the kinase domain (vs. non-kinase domain) and belonging to hotspot residues (vs. non-hotspot residues). We identified that, while predictive tools have low specificity in general, PolyPhen-2 had the best accuracy. Further efforts to combine all four tools by consensus, voting, or other simple methods did not significantly improve accuracy.

DISCUSSION

The study provides a large dataset of kinase mutations along with their predicted pathogenicity that can be used as a training set for future studies. Furthermore, a comparative sensitivity and selectivity of commonly used computational tools is presented.

CONCLUSION

Primary-structure-based in silico tools identified more cancerous/deleterious mutations in the kinase domains and at the hot spot residues while having higher sensitivity than specificity in detecting deleterious mutations.

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

Identifying the Molecular Drivers of Pathogenic Aldehyde Dehydrogenase Missense Mutations in Cancer and Non-Cancer Diseases

Human aldehyde dehydrogenases (ALDHs) comprising 19 isoenzymes play a vital role on both endogenous and exogenous aldehyde metabolism. This NAD(P)-dependent catalytic process relies on the intact structural and functional activity of the cofactor binding, substrate interaction, and the oligomerization of ALDHs. Disruptions on the activity of ALDHs, however, could result in the accumulation of cytotoxic aldehydes, which have been linked with a wide range of diseases, including both cancers as well as neurological and developmental disorders. In our previous works, we have successfully characterised the structure-function relationships of the missense variants of other proteins. We, therefore, applied a similar analysis pipeline to identify potential molecular drivers of pathogenic ALDH missense mutations. Variants data were first carefully curated and labelled as cancer-risk, non-cancer diseases, and benign. We then leveraged various computational biophysical methods to describe the changes caused by missense mutations, informing a bias of detrimental mutations with destabilising effects. Cooperating with these insights, several machine learning approaches were further utilised to investigate the combination of features, revealing the necessity of the conservation of ALDHs. Our work aims to provide important biological perspectives on pathogenic consequences of missense mutations of ALDHs, which could be invaluable resources in the development of cancer treatment.

Finding and engineering the newly found bacterial superoxide dismutase enzyme to increase its thermostability and decrease the immunogenicity: a computational and experimental research

Superoxide dismutase (SOD) is one of the most important antioxidant enzymes that can reduce oxidative stress in the cell environment. Nowadays, bacterial sources of enzyme are commercially applicable in the cosmetics and pharmaceutical industries, but the allergenic effect of proteins from non-human sources has been mentioned as disadvantage of these kinds of enzymes. In this study, to find the suitable bacterial SOD candidate for decreasing immunogenicity, the sequences of five thermophilic bacteria were selected as reference species. Then, linear and conformational B-cell epitopes of the SOD were analyzed by different servers. The stability and immunogenicity of mutant positions were also evaluated. The mutant gene was inserted into the pET-23a expression vector and transformed into E. Coli BL21 (DE3) for expression of the recombinant enzyme. Afterward, the expression of the mutant enzyme was evaluated by SDS-PAGE analysis and the recombinant enzyme activity was assessed. Anoxybacillus gonensis was selected as a reasonable SOD source according to BLAST search, physicochemical properties analysis, and prediction of allergenic features. Regarding our results, five residues including E84, E142, K144, G147, and M148 were predicted as candidates for mutagenesis. Finally, the K144A was chosen as the final modification due to the increase in the stability of the enzyme and decreased immunogenicity of the enzyme as well. The enzyme activity was 240 U/ml at room temperature. Alternation in K144 to alanine caused increased stability of the enzyme. In silico studies confirmed non-antigenic protein after mutation.