Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Comprehensive classification of TP53 somatic missense variants based on their impact on p53 structural stability

Somatic variation is a major type of genetic variation contributing to human diseases including cancer. Of the vast quantities of somatic variants identified, the functional impact of many somatic variants, in particular the missense variants, remains unclear. Lack of the functional information prevents the translation of rich variation data into clinical applications. We previously developed a method named Ramachandran Plot-Molecular Dynamics Simulations (RP-MDS), aiming to predict the function of germline missense variants based on their effects on protein structure stability, and successfully applied to predict the deleteriousness of unclassified germline missense variants in multiple cancer genes. We hypothesized that regardless of their different genetic origins, somatic missense variants and germline missense variants could have similar effects on the stability of their affected protein structure. As such, the RP-MDS method designed for germline missense variants should also be applicable to predict the function of somatic missense variants. In the current study, we tested our hypothesis by using the somatic missense variants in TP53 as a model. Of the 397 somatic missense variants analyzed, RP-MDS predicted that 195 (49.1%) variants were deleterious as they significantly disturbed p53 structure. The results were largely validated by using a p53-p21 promoter-green fluorescent protein (GFP) reporter gene assay. Our study demonstrated that deleterious somatic missense variants can be identified by referring to their effects on protein structural stability.

Machine Learning of Three-Dimensional Protein Structures to Predict the Functional Impacts of Genome Variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a gene's downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2L2)-related factor 2 (NRF2) and c-Myc. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-Myc or NRF2 transcriptional pathway activities.

Pathogenic variants in human DNA damage repair genes mostly arose after the latest human out-of-Africa migration

Introduction

The DNA damage repair (DDR) system in human genome is pivotal in maintaining genomic integrity. Pathogenic variation (PV) in DDR genes impairs their function, leading to genome instability and increased susceptibility to diseases, especially cancer. Understanding the evolution origin and arising time of DDR PV is crucial for comprehending disease susceptibility in modern humans.

Methods

We used big data approach to identify the PVs in DDR genes in modern humans. We mined multiple genomic databases derived from 251,214 modern humans of African and non-Africans. We compared the DDR PVs between African and non-African. We also mined the DDR PVs in the genomic data derived from 5,031 ancient humans. We used the DDR PVs from ancient humans as the intermediate to further the DDR PVs between African and non-African.

Results and discussion

We identified 1,060 single-base DDR PVs across 77 DDR genes in modern humans of African and non-African. Direct comparison of the DDR PVs between African and non-African showed that 82.1% of the non-African PVs were not present in African. We further identified 397 single-base DDR PVs in 56 DDR genes in the 5,031 ancient humans dated between 45,045 and 100 years before present (BP) lived in Eurasian continent therefore the descendants of the latest out-of-Africa human migrants occurred 50,000-60,000 years ago. By referring to the ancient DDR PVs, we observed that 276 of the 397 (70.3%) ancient DDR PVs were exclusive in non-African, 106 (26.7%) were shared between non-African and African, and only 15 (3.8%) were exclusive in African. We further validated the distribution pattern by testing the PVs in BRCA and TP53, two of the important genes in genome stability maintenance, in African, non-African, and Ancient humans. Our study revealed that DDR PVs in modern humans mostly emerged after the latest out-of-Africa migration. The data provides a foundation to understand the evolutionary basis of disease susceptibility, in particular cancer, in modern humans.

Interaction Between SARS-CoV-2 Spike Protein S1 Subunit and Oyster Heat Shock Protein 70

There is growing evidence that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contaminates the marine environment and is bioaccumulated in filter-feeding shellfish. Previous study shows the Pacific oyster tissues can bioaccumulate the SARS-CoV-2, and the oyster heat shock protein 70 (oHSP70) may play as the primary attachment receptor to bind SARS-CoV-2's recombinant spike protein S1 subunit (rS1). However, detailed information about the interaction between rS1 and oHSP70 is still unknown. In this study, we confirmed that the affinity of recombinant oHSP70 (roHSP70) for rS1 (KD = 20.4 nM) is comparable to the receptor-binding affinity of rACE2 for rS1 (KD = 16.7 nM) by surface plasmon resonance (SPR)-based Biacore and further validated by enzyme-linked immunosorbent assay (ELISA). Three truncated proteins (roHSP70-N/C/M) and five mutated proteins (p.I229del, p.D457del, p.V491_K495del, p.K556I, and p.ΣroHSP70) were constructed according to the molecular docking results. All three truncated proteins have significantly lower affinity for rS1 than the full-length roHSP70, indicating that all three segments of roHSP70 are involved in binding to rS1. Further, the results of SPR and ELISA showed that all five mutant proteins had significantly lower affinity for rS1 than roHSP70, suggesting that amino acids at these sites are involved in binding to rS1. This study provides a preliminary theoretical basis for the bioaccumulation of SARS-CoV-2 in oyster tissues or using roHSP70 as the capture unit to selectively enrich virus particles for detection.

Clinical-Genomic Analysis of 1261 Patients with Ehlers-Danlos Syndrome Outlines an Articulo-Autonomic Gene Network (Entome)

Systematic evaluation of 80 history and 40 history findings diagnosed 1261 patients with Ehlers-Danlos syndrome (EDS) by direct or online interaction, and 60 key findings were selected for their relation to clinical mechanisms and/or management. Genomic testing results in 566 of these patients supported EDS relevance by their differences from those in 82 developmental disability patients and by their association with general rather than type-specific EDS findings. The 437 nuclear and 79 mitochondrial DNA changes included 71 impacting joint matrix (49 COL5), 39 bone (30 COL1/2/9/11), 22 vessel (12 COL3/8VWF), 43 vessel-heart (17FBN1/11TGFB/BR), 59 muscle (28 COL6/12), 56 neural (16 SCN9A/10A/11A), and 74 autonomic (13 POLG/25porphyria related). These genes were distributed over all chromosomes but the Y, a network analogized to an 'entome' where DNA change disrupts truncal mechanisms (skin constraint, neuromuscular support, joint vessel flexibility) and produces a mirroring cascade of articular and autonomic symptoms. The implied sequences of genes from nodal proteins to hypermobility to branching tissue laxity or dysautonomia symptoms would be ideal for large language/artificial intelligence analyses.

A comparative study of structural and catalytic activity alterations in firefly luciferase induced by carbon quantum dots containing amine and carboxyl functional groups

Despite of growing interest in use of carbon-based nanomaterials as carriers of functional proteins, less attention has been paid to the effects of these nanomaterials on the structure and function of the proteins. In this study, with the aim of shedding light on the mechanisms of interaction between carbon-based nanomaterials and proteins, the interactions of carbon quantum dots (CQDs) containing amine (CQD-NH2) or carboxyl groups (CQD-COOH) with Photinus pyralis firefly luciferase enzyme were investigated by experimental and computational approaches. The structural changes and reduction in activity of the luciferase upon treatment with CQDs were experimentally proved. CQD-NH2 induced more reduction in enzyme activity (15 %) compared to CQD-COOH (7.4 %). The interactions CQD-NH2 with luciferase led to higher affinity of the enzyme for its substrate. It was found by molecular dynamic simulations that CQD-NH2 binds to multiple regions on the surface of luciferase. Secondary structure analysis showed that CQD-NH2 had more profound effects on the active site amino acids, the adjacent amino acids to the active site and the residues involved in ATP binding site. In addition, CQD-NH2 interactions with luciferase were suggested to be stronger than CQD-COOH based on the number of hydrogen bonds and the binding energies.

Transient Structural Dynamics of Glycogen Phosphorylase from Nonequilibrium Hydrogen/Deuterium-Exchange Mass Spectrometry

It remains a major challenge to ascertain the specific structurally dynamic changes that underpin protein functional switching. There is a growing need in molecular biology and drug discovery to complement structural models with the ability to determine the dynamic structural changes that occur as these proteins are regulated and function. The archetypal allosteric enzyme glycogen phosphorylase is a clinical target of great interest to treat type II diabetes and metastatic cancers. Here, we developed a time-resolved nonequilibrium millisecond hydrogen/deuterium-exchange mass spectrometry (HDX-MS) approach capable of precisely locating dynamic structural changes during allosteric activation and inhibition of glycogen phosphorylase. We resolved obligate transient changes in the localized structure that are absent when directly comparing active/inactive states of the enzyme and show that they are common to allosteric activation by AMP and inhibition by caffeine, operating at different sites. This indicates that opposing allosteric regulation by inhibitor and activator ligands is mediated by pathways that intersect with a common structurally dynamic motif. This mass spectrometry approach uniquely stands to discover local transient structural dynamics and could be used broadly to identify features that influence the structural transitions of proteins.

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

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

Complete genome sequence of Acinetobacter indicus and identification of the hydrolases provides direct insights into phthalate ester degradation

A strain designated Acinetobacter indicus WMB-7 with the ability to hydrolyze phthalate esters (PAEs) was isolated from the fermented grains of Baijiu. The genome of the strain was sequenced with a length of 3,256,420 bp and annotated with 3183 genes, of which 36 hydrolases encoding genes were identified. The hydrolases were analyzed by protein structure modeling and molecular docking, and 14 enzymes were docked to the ligand di-butyl phthalate with the catalytic active regions, and showed binding affinity. The 14 enzymes were expressed in E. coli and 5 of them showed the ability for PAEs hydrolysis. Enzyme GK020_RS15665 showed high efficiency for PAEs hydrolysis and could efficiently hydrolyze di-butyl phthalate under an initial concentration of 1000 mg/L with a half-life of 4.24 h. This work combined a series of methods for identifying PAEs hydrolases and offered a molecular basis for PAEs degradation of A. indicus strains from Baijiu.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01334-w.

Investigating sulfonamides - Human serum albumin interactions: A comprehensive approach using multi-spectroscopy, DFT calculations, and molecular docking

The environmental and health risks associated with sulfonamide antibiotics (SAs) are receiving increasing attention. Through multi-spectroscopy, density functional theory (DFT), and molecular docking, this study investigated the interaction features and mechanisms between six representative SAs and human serum albumin (HSA). Multi-spectroscopy analysis showed that the six SAs had significant binding capabilities with HSA. The order of binding constants at 298 K was as follows: sulfadoxine (SDX): 7.18 × 105 L mol-1 > sulfamethizole (SMT): 6.28 × 105 L mol-1 > sulfamerazine (SMR): 2.70 × 104 L mol-1 > sulfamonomethoxine (SMM): 2.54 × 104 L mol-1 > sulfamethazine (SMZ): 3.06 × 104 L mol-1 > sulfadimethoxine (SDM): 2.50 × 104 L mol-1. During the molecular docking process of the six SAs with HSA, the binding affinity range is from -7.4 kcal mol-1 to -8.6 kcal mol-1. Notably, the docking result of HSA-SDX reached the maximum of -8.6 kcal mol-1, indicating that SDX may possess the highest binding capacity to HSA. HSA-SDX binding, identified as a static quenching and exothermic process, is primarily driven by hydrogen bonds (H bonds) or van der Waals (vdW) interactions. The quenching processes of SMR/SMZ/SMM/SDX/SMT to HSA are a combination of dynamic and static quenching, indicating an endothermic reaction. Hydrophobic interactions are primarily accountable for SMR/SMZ/SMM/SDX/SMT and HSA binding. Competition binding results revealed that the primary HSA-SAs binding sites are in the subdomain IB of the HAS structure, consistent with the results of molecule docking. The correlation analysis based on DFT calculations revealed an inherent relationship between the structural chemical features of SAs and the binding performance of HSA-SAs. The dual descriptor (DD) and the electrophilic Fukui function were found to have a significant relationship (0.71 and -0.71, respectively) with the binding constants of HSA-SAs, predicting the binding performance of SAs and HSA. These insights have substantial scientific value for evaluating the environmental risks of SAs as well as understanding their impact on biological life activities.

A Molecular Integrative Study on the Inhibitory Effects of WRR and ERW on Amyloid β Peptide (1-42) Polymerization and Cell Toxicity

Alzheimer's disease (AD) is a neurodegenerative disease and the main pathological characteristic of AD is the deposition of Aβ42 in the brain. Inhibition of Aβ42 polymerization is one of the important research directions. Due to the pathological complexity of Alzheimer's disease, studies on Aβ42 polymerization inhibitors have not made significant progress worldwide. Using an independently constructed structure database of oligopeptides, in this study, molecular docking, umbrella sampling analysis of free energy, ThT fluorescence detection of Aβ42 polymerization, transmission electron microscopy, and flow cytometry detection of reactive oxygen species (ROS) and apoptosis were performed to screen tripeptides and pentapeptides that inhibit polymerization. It was found that two tripeptides, i.e., WRR and ERW, bind stably to the core of Aβ42 polymerization in the molecular dynamics analysis, and they significantly inhibited the aggregation of Aβ42 and reduced their cell toxicity in vitro.

Statistical Mechanics Metrics in Pairing and Parsing In Silico and Phenotypic Data of a Novel Genetic NFκB1 (c.T638A) Variant

(1) Background: Mutations in NFκB1, a transcriptional regulator of immunomodulating proteins, are a known cause of inborn errors of immunity. Our proband is a 22-year-old male with a diagnosis of common variable immunodeficiency (CVID), cytopenias with massive splenomegaly, and nodular regenerative hyperplasia of the liver. Genetic studies identified a novel, single-point mutation variant in NFκB1, c. T638A p. V213E. (2) Methods: Next-generation panel sequencing of the patient uncovered a novel single-point mutation in the NFκB1 gene that was modeled using the I-TASSER homology-modeling software, and molecular dynamics were assessed using the YASARA2 software (version 20.14.24). (3) Results: This variant replaces valine with glutamic acid at position 213 in the NFκB1 sequence. Molecular modeling and molecular dynamic studies showed altered dynamics in and around the rel homology domain, ankyrin regions, and death domain of the protein. We postulate that these changes alter overall protein function. (4) Conclusions: This case suggests the pathogenicity of a novel variant using protein-modeling techniques and molecular dynamic simulations.

Classification of PTEN missense VUS through exascale simulations

Phosphatase and tensin homolog (PTEN), a tumor suppressor with dual phosphatase properties, is a key factor in PI3K/AKT signaling pathway. Pathogenic germline variation in PTEN can abrogate its ability to dephosphorylate, causing high cancer risk. Lack of functional evidence lets numerous PTEN variants be classified as variants of uncertain significance (VUS). Utilizing Molecular Dynamics (MD) simulations, we performed a thorough evaluation for 147 PTEN missense VUS, sorting them into 66 deleterious and 81 tolerated variants. Utilizing replica exchange molecular dynamic (REMD) simulations, we further assessed the variants situated in the catalytic core of PTEN's phosphatase domain and uncovered conformational alterations influencing the structural stability of the phosphatase domain. There was a high degree of agreement between our results and the variants classified by Variant Abundance by Massively Parallel Sequencing, saturation mutagenesis, multiplexed functional data and experimental assays. Our extensive analysis of PTEN missense VUS should benefit their clinical applications in PTEN-related cancer.

SIGNIFICANCE STATEMENT

Classification of PTEN variants affecting its lipid phosphatase activity is important for understanding the roles of PTEN variation in the pathogenesis of hereditary and sporadic malignancies. Of the 3000 variants identified in PTEN, 1296 (43%) were assigned as VUS. Here, we applied MD and REMD simulations to investigate the effects of PTEN missense VUS on the structural integrity of the PTEN phosphatase domain consisting the WPD, P and TI active sites. We classified a total of 147 missense VUS into 66 deleterious and 81 tolerated variants by referring to the control group comprising 54 pathogenic and 12 benign variants. The classification was largely in concordance with these classified by experimental approaches.

A Clinical Qualification Protocol Highlights Overlapping Genomic Influences and Neuro-Autonomic Mechanisms in Ehlers-Danlos and Long COVID-19 Syndromes

A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers-Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic symptoms like headaches, muscle weakness, brain fog, chronic fatigue, dyspnea, and bowel irregularity to those of arthralgia and skin laxity, 15 of these symptoms shared with those of post-infectious SARS-CoV-2 (long COVID-19). Underlying articulo-autonomic mechanisms guided a clinical qualification protocol that qualified DNA variants in 317 genes as having diagnostic utility for EDS, six of them identical (F2-LIFR-NLRP3-STAT1-T1CAM1-TNFRSF13B) and eighteen similar to those modifying COVID-19 severity/EDS, including ADAMTS13/ADAMTS2-C3/C1R-IKBKG/IKBKAP-PIK3C3/PIK3R1-POLD4/POLG-TMPRSS2/TMPRSS6-WNT3/WNT10A. Also, contributing to EDS and COVID-19 severity were forty and three genes, respectively, impacting mitochondrial functions as well as parts of an overlapping gene network, or entome, that are hypothesized to mediate the cognitive-behavioral, neuro-autonomic, and immune-inflammatory alterations of connective tissue in these conditions. The further characterization of long COVID-19 natural history and genetic predisposition will be necessary before these parallels to EDS can be carefully delineated and translated into therapies.

DARVIC: Dihedral angle-reliant variant impact classifier for functional prediction of missense VUS

BACKGROUND

Of the large number of genetic variants identified, the functional impact for most of them remains unknown. Mutations in DNA damage repair genes such as MUTYH, which is involved in repairing A:8-oxoG mismatches caused by reactive oxygen species, can cause a higher risk of cancer. Mutations happening in other key genes such as TP53 also pose huge health threats and risk of cancer. The interpretation of genetic variants' functional impact is a forefront issue that needs to be addressed. Many different in silico methods based on different principles have been developed and applied in interpreting genetic variants. However, a current challenge is that many existing methods tend to overpredict the pathogenicity of benign variants. A new approach is needed to tackle this issue to improve genetic variant interpretation through the use of in silico methods.

METHODS

In this study, we developed another protein structural-based approach called Dihedral angle-reliant variant impact classifier (DARVIC) to predict the deleterious impact of the coding-changing missense variants. DARVIC uses Ramachandran's principle of protein stereochemistry as the theoretical foundation and uses molecular dynamics simulations coupled with a supervised machine learning algorithm XGBoost to determine the functional impact of missense variants on protein structural stability.

RESULTS

We characterized the features of dihedral angles in dynamic protein structures. We also tested the performance of DARVIC in MUTYH and TP53 missense variants and achieved satisfactory results in reflecting the functional impacts of the variants on protein structure. The method achieved a balanced accuracy of 84% in a functionally validated MUTYH dataset containing both benign and pathogenic missense variants, higher than other existing in silico methods. Along with that, DARVIC was able to predict 119 (47%) deleterious variants from a dataset of 254 MUTYH VUS. Further application of DARVIC to a functionally validated TP53 dataset had a balanced accuracy of 94%, topping other methods, demonstrating DARVIC's robustness.

CONCLUSION

DARVIC provides a valuable tool to predict the functional impacts of missense variants based on their effects on protein structural stability and motion. At its current state, DARVIC performed well in predicting the functional impact of the missense variants both in MUTYH and TP53. We expect its high potential to predict functional impact for the missense variants in other genes.

Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification

Functional classification of genetic variants is a key for their clinical applications in patient care. However, abundant variant data generated by the next-generation DNA sequencing technologies limit the use of experimental methods for their classification. Here, we developed a protein structure and deep learning (DL)-based system for genetic variant classification, DL-RP-MDS, which comprises two principles: 1) Extracting protein structural and thermodynamics information using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, 2) combining those data with an unsupervised learning model of auto-encoder and a neural network classifier to identify the statistical significance patterns of the structural changes. We observed that DL-RP-MDS provided higher specificity than over 20 widely used in silico methods in classifying the variants of three DNA damage repair genes: TP53, MLH1, and MSH2. DL-RP-MDS offers a powerful platform for high-throughput genetic variant classification. The software and online application are available at https://genemutation.fhs.um.edu.mo/DL-RP-MDS/.

Ethnic-specificity, evolution origin and deleteriousness of Asian BRCA variation revealed by over 7500 BRCA variants derived from Asian population

Pathogenic variation in BRCA1 and BRCA2 (BRCA) causes high risk of breast and ovarian cancer, and BRCA variation data are important markers for BRCA-related clinical cancer applications. However, comprehensive BRCA variation data are lacking from the Asian population despite its large population size, heterogenous genetic background and diversified living environment across the Asia continent. We performed a systematic study on BRCA variation in Asian population including extensive data mining, standardization, annotation and characterization. We identified 7587 BRCA variants from 685 592 Asian individuals in 40 Asia countries and regions, including 1762 clinically actionable pathogenic variants and 4915 functionally unknown variants (https://genemutation.fhs.um.edu.mo/Asian-BRCA/). We observed the highly ethnic-specific nature of Asian BRCA variants between Asian and non-Asian populations and within Asian populations, highlighting that the current European descendant population-based BRCA data is inadequate to reflect BRCA variation in the Asian population. We also provided archeological evidence for the evolutionary origin and arising time of Asian BRCA variation. We further provided structural-based evidence for the deleterious variants enriched within the functionally unknown Asian BRCA variants. The data from our study provide a current view of BRCA variation in the Asian population and a rich resource to guide clinical applications of BRCA-related cancer for the Asian population.

Potential Inhibitors of Monkeypox Virus Revealed by Molecular Modeling Approach to Viral DNA Topoisomerase I

The monkeypox outbreak has become a global public health emergency. The lack of valid and safe medicine is a crucial obstacle hindering the extermination of orthopoxvirus infections. The identification of potential inhibitors from natural products, including Traditional Chinese Medicine (TCM), by molecular modeling could expand the arsenal of antiviral chemotherapeutic agents. Monkeypox DNA topoisomerase I (TOP1) is a highly conserved viral DNA repair enzyme with a small size and low homology to human proteins. The protein model of viral DNA TOP1 was obtained by homology modeling. The reliability of the TOP1 model was validated by analyzing its Ramachandran plot and by determining the compatibility of the 3D model with its sequence using the Verify 3D and PROCHECK services. In order to identify potential inhibitors of TOP1, an integrated library of 4103 natural products was screened via Glide docking. Surface Plasmon Resonance (SPR) was further implemented to assay the complex binding affinity. Molecular dynamics simulations (100 ns) were combined with molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations to reveal the binding mechanisms of the complex. As a result, three natural compounds were highlighted as potential inhibitors via docking-based virtual screening. Rosmarinic acid, myricitrin, quercitrin, and ofloxacin can bind TOP1 with KD values of 2.16 μM, 3.54 μM, 4.77 μM, and 5.46 μM, respectively, indicating a good inhibitory effect against MPXV. The MM/PBSA calculations revealed that rosmarinic acid had the lowest binding free energy at -16.18 kcal/mol. Myricitrin had a binding free energy of -13.87 kcal/mol, quercitrin had a binding free energy of -9.40 kcal/mol, and ofloxacin had a binding free energy of -9.64 kcal/mol. The outputs (RMSD/RMSF/Rg/SASA) also indicated that the systems were well-behaved towards the complex. The selected compounds formed several key hydrogen bonds with TOP1 residues (TYR274, LYS167, GLY132, LYS133, etc.) via the binding mode analysis. TYR274 was predicted to be a pivotal residue for compound interactions in the binding pocket of TOP1. The results of the enrichment analyses illustrated the potential pharmacological networks of rosmarinic acid. The molecular modeling approach may be acceptable for the identification and design of novel poxvirus inhibitors; however, further studies are warranted to evaluate their therapeutic potential.

Structural evolution of Delta lineage of SARS-CoV-2

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

Exploring the mechanism of C473D mutation on CDC25B causing weak binding affinity with CDK2/CyclinA by molecular dynamics study

CDC25B belongs to the CDC25 family, and it plays an important part in regulating the activity of CDK/CyclinA. Studies have shown that CDC25B is closely related to cancer development. When CYS473 on CDC25B is mutated into ASP, the affinity between CDC25B and CDK2/CyclinA weakens, and their dissociation speed is greatly improved. However, the mechanism by which the CDC25BC473D mutant weakens its binding to CDK2/CyclinA is unclear. In order to study the effect of CDC25BC473D mutants on CDK2/CyclinA substrates, we constructed and verified the rationality of the CDC25BWT:CDK2/CyclinA system and CDC25BC473D:CDK2/CyclinA system and conducted molecular dynamics (MD) simulation analysis. In the post-analysis, the fluctuations of residues ARG488-SER499, LYS541-TRP550 on CDC25B and residues ASP206-ASP210 on CDK2 were massive in the mutant CDC25BC473D:CDK2/CyclinA system. And the interactions between residue ARG492 and residue GLU208, residue ARG544 and residue GLU42, residue ARG544 and TRP550 were weakened in the mutant CDC25BC473D:CDK2/CyclinA system. The results showed that when CYS473 on CDC25B was mutated into ASP473, the mutant CDC25BC473D:CDK2/CyclinA system was less stable than the wild-type CDC25BWT:CDK2/CyclinA system. Finally, active site CYS473 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BCYS473 and CDK2 in the CDC25BC473D:CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately provided a useful understanding of the weak interactions between CDC25BCYS473D and CDK2/CyclinA.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain.Communicated by Ramaswamy H. Sarma.

A Theoretical and Experimental Study for Enzymatic Biodiesel Production from Babassu Oil (Orbignya sp.) Using Eversa Lipase

A theoretical and experimental study was carried out on the biocatalytic production of babassu biodiesel through enzymatic hydroesterification. The complete hydrolysis of babassu oil was carried out using a 1:1 mass solution at 40 °C for 4 h using 0.4% of lipase from Thermomyces lanuginosus (TLL). Then, with the use of Eversa® Transform 2.0 lipase in the esterification step, a statistical design was used, varying the temperature (25–55 °C), the molar ratio between free fatty acids (FFAs) and methanol (1:1 to 1:9), the percentage of biocatalyst (0.1% to 0.9%), and the reaction time (1–5 h) using the Taguchi method. The ideal reaction levels obtained after the statistical treatment were 5 h of reaction at 40 °C at a molar ratio of 1:5 (FFAs/methanol) using 0.9% of the biocatalyst. These optimal conditions were validated by chromatographic analysis; following the EN 14103 standard, the sample showed an ester concentration of 95.76%. A theoretical study was carried out to evaluate the stability of Eversa with FFAs. It was observed in the molecular docking results that the ligands interacted directly with the catalytic site. Through molecular dynamics studies, it was verified that there were no significant conformational changes in the studied complexes. Theoretical and experimental results show the feasibility of this process.

Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5

There is a need of computational tools to rank bRo5 drug candidates in the very early phases of drug discovery when chemical matter is unavailable. In this study, we selected three compounds: (a) a Ro5 drug (Pomalidomide), (b) a bRo5 orally available drug (Saquinavir), and (c) a polar PROTAC (CMP 98) to focus on computational access to physicochemical properties. To provide a benchmark, the three compounds were first experimentally characterized for their lipophilicity, polarity, IMHBs, and chameleonicity. To reproduce the experimental information content, we generated conformer ensembles with conformational sampling and molecular dynamics in both water and nonpolar solvents. Then we calculated Rgyr, 3D PSA, and IMHB number. An innovative pool of strategies for data analysis was then provided. Overall, we report a contribution to close the gap between experimental and computational methods for characterizing bRo5 physicochemical properties.

Comprehensive Identification of Deleterious TP53 Missense VUS Variants Based on Their Impact on TP53 Structural Stability

TP53 plays critical roles in maintaining genome stability. Deleterious genetic variants damage the function of TP53, causing genome instability and increased cancer risk. Of the large quantity of genetic variants identified in TP53, however, many remain functionally unclassified as variants of unknown significance (VUS) due to the lack of evidence. This is reflected by the presence of 749 (42%) VUS of the 1785 germline variants collected in the ClinVar database. In this study, we addressed the deleteriousness of TP53 missense VUS. Utilizing the protein structure-based Ramachandran Plot-Molecular Dynamics Simulation (RPMDS) method that we developed, we measured the effects of missense VUS on TP53 structural stability. Of the 340 missense VUS tested, we observed deleterious evidence for 193 VUS, as reflected by the TP53 structural changes caused by the VUS-substituted residues. We compared the results from RPMDS with those from other in silico methods and observed higher specificity of RPMDS in classification of TP53 missense VUS than these methods. Data from our current study address a long-standing challenge in classifying the missense VUS in TP53, one of the most important tumor suppressor genes.

Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population

Background

Identifying genetic disease-susceptible individuals through population screening is considered as a promising approach for disease prevention. DNA mismatch repair (MMR) genes including MLH1, MSH2, MSH6 and PMS2 play essential roles in maintaining microsatellite stability through DNA mismatch repair, and pathogenic variation in MMR genes causes microsatellite instability and is the genetic predisposition for cancer as represented by the Lynch syndrome. While the prevalence and spectrum of MMR variation has been extensively studied in cancer, it remains largely elusive in the general population. Lack of the knowledge prevents effective prevention for MMR variation–caused cancer. In the current study, we addressed the issue by using the Chinese population as a model.

Methods

We performed extensive data mining to collect MMR variant data from 18 844 ethnic Chinese individuals and comprehensive analyses for the collected MMR variants to determine its prevalence, spectrum and features of the MMR data in the Chinese population.

Results

We identified 17 687 distinct MMR variants. We observed substantial differences of MMR variation between the general Chinese population and Chinese patients with cancer, identified highly Chinese-specific MMR variation through comparing MMR data between Chinese and non-Chinese populations, predicted the enrichment of deleterious variants in the unclassified Chinese-specific MMR variants, determined MMR pathogenic prevalence of 0.18% in the general Chinese population and determined that MMR variation in the general Chinese population is evolutionarily neutral.

Conclusion

Our study provides a comprehensive view of MMR variation in the general Chinese population, a resource for biological study of human MMR variation, and a reference for MMR-related cancer applications.