Optimizing siRNA Therapeutics Targeting HIF-1α: Computational Design, Screening, and Molecular Dynamics Simulation Studies

Hypoxia-inducible factor-1 alpha (HIF-1α) is an important transcription factor regulating glycolysis, angiogenesis, metastasis, and erythropoiesis under hypoxic conditions in solid tumors. Small interfering RNAs (siRNAs) have emerged as a promising therapeutic approach for solid tumors by selectively silencing target genes. This study explored siRNA-mediated degradation of HIF-1α by specifically targeting HIF-1α mRNA. We retrieved the HIF-1α gene sequence from the database and used various computational tools like siDirect and OligoWalk to get potential 19-21nts long siRNAs. Furthermore, these siRNAs were screened using parameters like sequence specificity, BLASTn, secondary structure formation, GC content, binding affinity between siRNA and mRNA, and thermodynamic properties. The potential siRNAs were further evaluated through molecular docking studies for interaction with the human Argonaute-2 protein (hAgo2), followed by molecular dynamics simulation studies. Post-MD studies revealed S4 (5'UAUAUGGUGAUGAUGUGGC3') as the most potential siRNA candidate against HIF-1α, based on root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and H-bond analysis. Molecular mechanics Poisson-Boltzmann surface area (MMPBSA) analysis was also performed to further validate the selected siRNA candidates, which further affirmed S4 (5'UAUAUGGUGAUGAUGUGGC3') as a potential candidate against HIF-1α.

Authors

Institutions

Publication Details

Journal
Molecular Pharmaceutics
Published
2025-05-19
DOI
https://doi.org/10.1021/acs.molpharmaceut.5c00104
Citations
3
Primary Topic
RNA Interference and Gene Delivery
Type
article
Field-Weighted Citation Impact
2.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing siRNA Therapeutics Targeting HIF-1α: Computational Design, Screening, and Molecular Dynamics Simulation Studies

Neeraj Kumar Shrivastava, Gaurav Kaithwas, Pratibha Verma, Sara A. Aldossary et al.
3 citations
Molecular Pharmaceutics
RNA Interference and Gene Delivery
2.42
article

Optimizing siRNA Therapeutics Targeting HIF-1α: Computational Design, Screening, and Molecular Dynamics Simulation Studies

Neeraj Kumar Shrivastava, Gaurav Kaithwas, Pratibha Verma, Sara A. Aldossary, Mohd Nazam Ansari, Yusuf Akhter, Abdulaziz S. Saeedan, Anurag Kumar, Garima Singh, Jyoti Singh, Sneha Yadav, Archana Bharti Sonkar
article en
3 citations

Abstract

Hypoxia-inducible factor-1 alpha (HIF-1α) is an important transcription factor regulating glycolysis, angiogenesis, metastasis, and erythropoiesis under hypoxic conditions in solid tumors. Small interfering RNAs (siRNAs) have emerged as a promising therapeutic approach for solid tumors by selectively silencing target genes. This study explored siRNA-mediated degradation of HIF-1α by specifically targeting HIF-1α mRNA. We retrieved the HIF-1α gene sequence from the database and used various computational tools like siDirect and OligoWalk to get potential 19-21nts long siRNAs. Furthermore, these siRNAs were screened using parameters like sequence specificity, BLASTn, secondary structure formation, GC content, binding affinity between siRNA and mRNA, and thermodynamic properties. The potential siRNAs were further evaluated through molecular docking studies for interaction with the human Argonaute-2 protein (hAgo2), followed by molecular dynamics simulation studies. Post-MD studies revealed S4 (5'UAUAUGGUGAUGAUGUGGC3') as the most potential siRNA candidate against HIF-1α, based on root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and H-bond analysis. Molecular mechanics Poisson-Boltzmann surface area (MMPBSA) analysis was also performed to further validate the selected siRNA candidates, which further affirmed S4 (5'UAUAUGGUGAUGAUGUGGC3') as a potential candidate against HIF-1α.

Molecular PharmaceuticsVol. 22(6)
Prince Sattam Bin Abdulaziz University (SA), Babasaheb Bhimrao Ambedkar University (IN), King Faisal University (SA)
Openalex Percentile: Top 12%
RNA Interference and Gene Delivery
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.