Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.

Systematic transition modeling analysis in the MEF2B-DNA binding interface due to Y69H and K4E variants.

Transcriptional coactivator myocyte enhancer factor 2B (MEF2B) mutations are the most common cause of germinal center-derived B-cell non-Hodgkin lymphoma. Despite well-established contributions in lymphomagenesis, the structure-function paradigms of these mutations are largely unknown. Here through in silico approaches, we present structural evaluation of two reported missense variants (K4E and Y69H) in MEF2B to investigate their impact on DNA-binding through molecular dynamics simulation assays. Notably, MEF2B-specific MADs box domain (Lys23, Arg24 and Lys31) and N-terminal loop residues (Gly2, Arg3, Lys4, Lys5, Ile6 and Asn13) contribute in DNA binding, while in MEF2BK4E, DNA binding is facilitated by Gly2, Arg3 and Arg91 (α3) residues. Conversely, in MEF2BY69H, Arg3, Lys5, Ser78, Arg79 and Asn81 residues mediate DNA binding. DNA binding induces pronounced conformational readjustments in MEF2BWT-specific α1-N-terminal loop region, while MEF2BY69H and MEF2BK4E exhibit fluctuations in both α1 and α3. Hydrogen (H)-bond occupancy analysis reveals a similar DNA binding behavior for MEF2WT and MEF2BY69H, compared to MEF2BK4E structure. The Anisotropic Network Model analysis depicts α1 and α3 as more fluctuant regions in MEF2BK4E as compared to other systems. MEF2BWT and MEF2BK4E, Tyr69 residue is involved in p300 binding thus possible influence of Y69H variation in the functions other than DNA binding, such as p300 co-activator recruitment may explain the reduced transcriptional activation of MEF2BY69H. Thus, present study may provide a structural basis of DNA recognition by pinpointing the underlying conformational changes in the dynamics of MEF2BK4E, MEF2BY69H, and MEF2BWT structures that may contribute in the identification of novel therapeutic strategies for lymphomagenesis.

XPC as breast cancer susceptibility gene: evidence from genetic profiling, statistical inferences and protein structural analysis.

BACKGROUND: Gene polymorphisms that affect nucleotide excision repair (NER) pathway may link with higher susceptibility of breast cancer (BC); however, the significance of these associations may vary conferring to the individual ethnicity. Xeroderma pigmentosum complementation gene (XPC) plays a substantial role in recognizing damaged DNA during NER process. OBJECTIVE AND METHODS: To estimate the relationship among XPC polymorphisms and breast cancer (BC) risk, we carried out a case-control-association study with 493 BC cases and 387 controls using TETRA-ARMS-PCR. Distributional differences of clinical features, demographic factors and XPC polymorphisms among BC cases and controls were examined by conditional logistic regression model. Kaplan-Meier test was applied to predict survival distributions and protein structure was predicted using computational tools. RESULTS: Obesity, consanguinity, positive marital status and BC family history were associated (P ≤ 0.01) with higher BC risk. Genotyping revealed significant involvement (P ≤ 0.01) of two XPC polymorphisms rs2228001-A > C (OR = 3.8; CI 1.9-7.6) and rs2733532-C > T (OR = 2.6; CI 1.4-5.03) in BC development, asserting them potential risk factors for increased BC incidence. However, no association (P > 0.05) was detected for overall or progression free survival for both XPC polymorphisms possibly due to shorter follow-up time (45 months). As compared to normal XPC structure, pronounced conformational changes have been observed in the C-terminus of XPCQ939K, bearing rs2228001-A > C substitution. In XPCQ939K, two additional α-helices were observed at A292-E297 and Y252-R286, while L623-M630 and L649-L653 helices were converted into loop conformation. CONCLUSION: In conclusion, both XPC polymorphisms confer significant association with increased BC risk. rs2228001 substitution may change the structural and functional preferences of XPC C-terminus, while rs2733532 may have regulatory role thereby leading to potential BC risk.

In Silico Exploration of Conformational Dynamics and Novel Inhibitors for Targeting MEF2-Associated Transcriptional Activity.

Myocyte enhancer factor 2 (MEF2; MEF2A-MEF2D) transcription factors regulate gene expression in a variety of developmental processes by binding to AT-rich DNA motifs via highly conserved N-terminal extensions known as MADS-box and MEF2 domains. Despite the fact that MEF2 proteins exhibit high similarity at their N-terminal regions and share a common consensus DNA binding motif, their functional preferences may vary significantly in the adjacent regions to the DNA binding core segment. The current study delineates the conformational paradigm, clustered recognition, and comparative DNA binding preferences for MEF2A and MEF2B-specific MADS-box/MEF2 domains at the YTA(A/T)4TAR consensus motif. In both MEF2A and MEF2B proteins, α1-helix plays a crucial role through acquiring more flexibility by attaining loop conformation. In comparison to apo-MEF2, an outward disposition of the distal portion of α1-helix and movement of its proximal part to ß1 allows synergistic repositioning of the α1-α2 linker, C-terminal region, and MEF2 domain, resulting in the formation of a hydrophobic groove for DNA binding. In both instances, conformational switching of the helical content is the main contributing factor while preserving the overall ß-topology to maintain the inside-out conformation of subdivided α1-helix flip. Multivariate statistical analysis reveals that MEF2B obscures less accessible conformational space for DNA binding as compared to the MEF2A-DNA complex. The presence of similar structural requirements and conserved residues including Arg10, Phe21, and Arg24 in accentuating the MEF2-specific DNA recognition mechanism led us to perform structure-based virtual screening for isolating novel inhibitors that are able to target MEF2-DNA binding regions. The top hits (acetamide, benzamide, carboxamide, and enamide) obtained through preliminary assay were scrutinized to binding potential analysis at the MEF2-DNA binding groove, energy values, absorption, distribution, toxicity, and Lipinski's rule of five assessments. Based on these findings, we propose valuable active drug-like molecules for selective applications against MEF2A and MEF2B. The current study may help in uncovering the atomistic-level mechanistic DNA binding patterns of MEF2 proteins, and data may be valuable in devising effective therapeutic strategies for MEF2-associated disorders.

Correlation of MLH1 polymorphisms, survival statistics, in silico assessment and gene downregulation with clinical outcomes among breast cancer cases.

This study aimed to investigate the role of MLH1 polymorphisms, respective protein structure prediction, survival analysis, related clinicopathological details and MLH1 expression in breast cancer (BC). Genotyping of selected SNPs in BC patients (493) and age matched controls (387) were performed by Tetra-ARMS PCR. Gene expression among breast tumors (127) and adjacent control tissues were analysed using reverse transcriptase PCR (RT-PCR) and immunohistochemistry. Statistical analysis was performed by SPSS and MedCalc. Conditional logistic regression analysis was applied to compute the odds ratio and confidence interval. Phyre2 and I-TASSER were used to generate MLH1 protein structures and verified by a variety of computational tools. Genotyping illustrated that MLH1 polymorphisms (rs63749795 and rs63749820) were significantly associated (P ≤ 0.05) with risk of developing BC. Down regulation of MLH1 gene expression/loss of the MLH1 protein (OR 12; CI 2.8-53.1) was observed in BC cases, illustrating its potential role in disease development. Moreover, loss of the MLH1 protein was found to be associated with higher grade cancer (P = 0.02) and lymph node positivity (P = 0.03), highlighting its essential role, as a component of the mismatch repair (MMR) machinery. Bioinformatics analysis confirmed that nonsense mutations produce a truncated MLH1 protein, causing a reduction in MMR efficiency. No association between MLH1 polymorphisms and overall and progression free survival statistics was observed among BC cases, possibly due to short follow-up study. Results at DNA, RNA and protein levels, along with in silico analysis, highlights the potential role of MLH1 in DNA repair mechanisms, within BC. Therefore, it was concluded that MLH1 may contribute towards BC development and progression.