Done Stojanov

In this study, we have performed computational PTM analysis on a panel of hypertrophic cardiomyopathy (HCM)-associated proteins: MYH7, MYBPC3, TNNT2, and TNNI3. We aimed to benchmark the prediction of PTM sites of three ML-based tools: MusiteDeep, PTMGPT2, and SiteTack, using PhosphoSitePlus as a reference for true positives. Notably, because the highest precision tool varied by protein and PTM type, our results indicate there is no single best tool for PTM prediction. Specifically, for HCM-associated proteins, MusiteDeep had the highest precision for MYBPC3 and MYH7; PTMGPT2 was best for TNNI3, and SiteTack for TNNT2. Examining PTM type and phosphorylation in particular, MusiteDeep had the highest precision, followed by PTMGPT2 and SiteTack. However, MusiteDeep did not identify acetylation sites, where PTMGPT2 outperformed SiteTack. Beyond these benchmarking results, we also report on five high-priority candidates for experimental validation in two HCM-associated proteins: MYH7 (K1451 acetylation, K129 methylation) and MYBPC3 (T705 phosphorylation, K14 acetylation, R44 methylation).

Complex transcriptional and epigenetic regulation, including variation in promoter-level cis-regulatory architecture, influences infertility. In this study, we performed a purely in silico analysis of the −1000 to −1 bp promoter regions (relative to the annotated TSS) of 13 human fertility-related genes using an integrated motif-discovery and annotation workflow (NNPP, MEME/STREME, Tomtom, FIMO/CentriMo, GOMo, and MethPrimer). Motif discovery identified multiple statistically supported de novo promoter motifs, and motif scanning mapped their occurrences across the analyzed promoters. Similarity searches against curated PWM databases did not yield significant matches under stringent criteria, consistent with divergent or under-represented motif patterns. Functional association analysis and CpG island profiling further highlighted promoter segments that merit prioritization for follow-up testing. As the analysis is purely in silico and restricted to a fixed promoter window, the identified motifs should be interpreted as candidate regulatory elements pending experimental validation.

Mutations in hormone receptors significantly influence infertility and the outcomes of assisted reproductive technologies (ART). This review explores the functional interplay among mutations in FSHR, LHCGR, AR, ESR1, and ESR2 hormone receptors and their combined effects on hormonal regulation, ovarian response, and implantation. Rather than analyzing receptor mutations in isolation, we explore how mutations in these genes interact within a complex hormonal signaling network, shaping reproductive outcomes. We detail the molecular mechanisms of receptor dysfunction, their associated clinical phenotypes, and the role of genetic screening in guiding personalized ART protocols. A comprehensive understanding of these interactions is crucial for optimizing treatment strategies, improving reproductive success, and advancing targeted therapeutic approaches in reproductive medicine.

In this study, we analyzed the regulatory properties of 26 (twenty-six) genes associated with nonsyndromic male infertility. We applied an in silico analysis in order to determine the number and distribution of promoters and identify relevant promoter consensus sequences and potential transcription factors. Underlining the concept of alternative transcriptional initiation (ATI), we have found that 65.4% of genes associated with nonsyndromic male infertility have 1 (one) to 6 (six) promoters, located in the region 1 kb upstream of the TSS, and 41% of them are located at a position below −500 bp. Although the TATA box consensus sequence TAWAAA, such as W is A or T, appears at a common location in all genes, it is shifted for at least 10 bp in the EFCAB9 gene. The C2H2 zinc finger is found to be the most significant common transcription factor, binding genes’ promoters GLIS1, ZSCAN21, GLIS3, GLIS1, ZNF770, ZNF780A, ZNF81, and ZNF264. On the other hand, basic leucine zipper factors (bZIPs) bind the JUNB gene promoter specifically, exhibiting unique regulatory properties of all genes associated with nonsyndromic male infertility. Two genes, NANOS1 and ZMYND15, are expected to be less susceptible to DNA methylation, due to the high density of CpG content found in their promoter regions.

This study aims to protocolize the utilization of the center-of-mass (CoM) distance method in GROMACS MD simulation software as a useful method for evaluating the binding affinity change in heterodimeric protein due to induced changes in one of the units. The hypothesis underlines the basic principles in biophysics, that an increase of the binding affinity is expected to reduce the relative CoM distance between monomers, while the opposite is expected to increase the relative CoM distance. However, it has been found that the CoM distance analysis must be strictly preformed during the convergent phase of systems’ dynamics, once the monomers enter mutually stable conformation — a limitation which has usually been overlooked. The method was used to study the impact of K417Y severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface glycoprotein (S-protein) mutation. It has been found that the K417Y mutation favors reduced binding affinity between SARS-CoV-2 S-protein and human angiotensin-converting enzyme 2 (hACE2) receptor, which is due to the loss of the permanent K417-D30 salt bridge in favor of a temporary Y417-D30 hydrogen bond. The destabilizing impact of K417Y mutation on S-protein–hACE2 complex was confirmed by radius of gyration analysis.

In this study, time and memory optimized (TMO) algorithm is presented. Compared with Smith–Waterman's algorithm, TMO is applicable for a more accurate detection of continuous insertion/deletions (indels) in genes’ fragments, associated with disorders caused by over-repetition of a certain codon. The improvement comes from the tendency to pinpoint indels in the least preserved nucleotide pairs. All nucleotide pairs that occur less frequently are classified as less preserved and they are considered as mutated codons whose mid-nucleotides were deleted. Other benefit of the proposed algorithm is its general tendency to maximize the number of matching nucleotides included per alignment, regardless of any specific alignment metrics. Since the structure of the solution, when applying Smith–Waterman, depends on the adjustment of the alignment parameters and, therefore, an incomplete (shortened) solution may be derived, our algorithm does not reject any of the consistent matching nucleotides that can be included in the final solution. In terms of computational aspects, our algorithm runs faster than Smith–Waterman for very similar DNA and requires less memory than the most memory efficient dynamic programming algorithms. The speed up comes from the reduced number of nucleotide comparisons that have to be performed, without having to imperil the completeness of the solution. Due to the fact that four integers (16 Bytes) are required for tracking matching fragment, regardless its length, our algorithm requires less memory than Huang's algorithm.