Done Stojanov

Dr. Done Stojanov

Faculty of computer science, Goce Delcev University, Shtip, North Macedonia
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 April 2023 in Journal of King Saud University – Science
Verified
Journal Article
Open access
DSDone Stojanov
Done Stojanov
ELElena Lazarova
Elena Lazarova
EVElena Veljkova
Elena Veljkova
PRPaolo Rubartelli
Paolo Rubartelli
MGMauro Giacomini
Mauro Giacomini

29 September 2014
Verified
Conference Paper
Closed access
DSDone Stojanov
Done Stojanov
Saso KoceskiSaso Koceski
Saso Koceski

14 August 2025 in Receptors
Verified
Journal Article
Open access
PDF file
DHDaniela Hristov
Daniela Hristov
DSDone Stojanov
Done Stojanov
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.

10 March 2023 in Biotechnology & Biotechnological Equipment
Verified
Journal Article
Open access
DSDone Stojanov
Done Stojanov
The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD – hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD – hACE2 solubility over N501T, N501 (wild type): SASAN501S=364.66±3.2842 nm2, SASAN501T=360±3.4156 nm2, SASAN501=359.56±3.6473 nm2. The N501Y mutation shifts α-helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from 310-helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY Cα’s RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the Gly502S-ProteinN-H…O=CLys353hACE2 hydrogen bond into permanent polar contact; E(Gly502S-ProteinN-H…O=CLys353hACE2)N501Y=4.816210332kcal/mol; E(Gly502S-ProteinN-H…O=CLys353hACE2)N501=3.909532232kcal/mol.

15 March 2024 in Gene & Protein in Disease
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Journal Article
Open access
PDF file
DSDone Stojanov
Done Stojanov
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.

19 November 2015 in Biotechnology & Biotechnological Equipment
Verified
Journal Article
Open access
DSDone Stojanov
Done Stojanov
ABAna Madevska Bogdanova
Ana Madevska Bogdanova
TOTomasz Marcin Orzechowski
Tomasz Marcin Orzechowski
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.