Advanced Research Platform for Deep Learning Structure Prediction, Kinase Mechanics & Automated Virtual Screening
The Suleiman Computational Structural Biology organization serves as a centralized computational research laboratory bridging systems biology and structural biophysics.
Our core methodology traces a rigorous multi-scale path:
Important
All raw datasets, full multi-sequence alignments (MSAs), complete AlphaFold2/ColabFold scoring matrices, molecular docking trajectories, manuscript drafts, and backend source pipelines housed in SuleimanComputationalStructuralBiology are strictly private.
How to Request Access: If you are a researcher, academic reviewer, or collaborator and would like to request access to the private core repository or raw research data, please contact: 📧 suleyman.hacizade1@gmail.com
To maintain the highest scientific rigor and protect sensitive pre-publication data, our organization is structured into two dedicated repositories:
SuleimanComputationalStructuralBiology
│
├── 🔒 SuleimanComputationalStructuralBiology (Private Core Backend — Access by Request)
│ ├── Raw multi-sequence alignments (MSAs) & MMseqs2 profiles
│ ├── Unrelaxed & relaxed AlphaFold2 structure models
│ ├── Complete PAE distance matrices & per-residue pLDDT profiles
│ ├── AutoDock Vina receptor/ligand PDBQT preparation pipelines
│ ├── Master's computational structural biology curriculum & tutorial data
│ └── 48-note persistent LLM research wiki & manuscript drafts
│ └── 📩 Request Access: suleyman.hacizade1@gmail.com
│
└── 🌐 SuleimanComputationalStructuralBiology.github.io (Public Interactive Portal)
├── 🌟 Live URL: https://suleimancomputationalstructuralbiology.github.io/
├── Interactive 3Dmol.js molecular structure explorer
├── Real-time pLDDT line charts & PAE heatmap matrix viewer
├── Publication-grade figure gallery with multi-criteria filters
├── Searchable Markdown + KaTeX structural biology knowledge base
└── Complete 9-module structural bioinformatics curriculum
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Target: Human RAC-alpha serine/threonine-protein kinase (UniProt:
P31749) - Context: Identified as the central master regulator hub kinase in triple-negative breast cancer (TNBC) through weighted gene co-expression network analysis (WGCNA).
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Methods: AlphaFold2 (ColabFold v1.6.1, MMseqs2), custom vector-algebraic structural metrics, Ramachandran dihedral analysis (
$\phi/\psi$ ), and pairwise$C_\alpha$ contact topology ($\le 8\text{ \AA}$ ). -
Key Findings: Highly confident catalytic kinase core (
$\text{pLDDT} \ge 90$ ) flanked by intrinsically disordered regulatory linkers ($\text{pLDDT} < 50$ ); 573 long-range tertiary$C_\alpha$ contacts.
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Target: Arabidopsis thaliana thylakoid kinases STN7 (
Q9SHR7) & STN8 (Q9M0E6) - Context: Key photosynthetic regulators responsible for LHCII state transitions and Photosystem II (PSII) core D1 turnover.
- Methods: AF2-multimer complex prediction, SVD-based structural alignment, conserved kinase motif mapping (DFG, HRD, $\alpha$C-helix, P-loop), and high-throughput AutoDock Vina screening.
- Screened Ligands: Staurosporine, K252a, Quercetin, SB216763, ATP, and ADP.
- Key Findings: Identified canonical DFG-in active loop geometry, an extensive heterodimer interface driven by the STN7 C-terminal extension, and sub-nanomolar affinity scaffolds.
Visit our live web platform at suleimancomputationalstructuralbiology.github.io to explore:
- 🧬 3D Molecular Explorer: Rotate, spin, zoom, and recolor AKT1 and STN7–STN8 models (Cartoon, Surface, Sphere, Stick) with AlphaFold pLDDT confidence color-mapping.
- 📊 Linked Structural Charts: Click any residue on the pLDDT confidence plot or PAE matrix to instantly focus and highlight that residue in the 3D viewer.
- 📚 48-Note Scientific Wiki: Search and read interconnected notes spanning entities (AKT1, STN7, STN8, LHCII, PTEN, mTORC2), concepts (Cryo-EM, MSMs, Docking, LDDT), and syntheses.
- 🎓 Structural Bioinformatics Curriculum: 9 master modules covering VMD scripting, MD simulations (GROMACS), enhanced sampling (Metadynamics/PLUMED), QM/MM (CP2K), and de novo protein design (RFdiffusion/ProteinMPNN).
- Lead Researcher: Suleiman Hajizadeh
- Affiliation: Institute of Molecular Biology (IMB), Azerbaijan
- Specialization: Structural Bioinformatics, Deep-Learning Protein Modeling, Kinase Biophysics, Network Biology
- Private Data & Repository Access Inquiries: suleyman.hacizade1@gmail.com
- Personal GitHub: @SuleimanHajizadeh
