In [ ]:
!conda install -c conda-forge jupyter biopython matplotlib pandas -y
!conda install bioconda::pybiolib
!pip install dnachisel pydna
In [104]:
import Bio
import matplotlib
import sys
print(f"Python Version: {sys.version}")
print(f"Biopython Version: {Bio.__version__}")
print("Environment Setup Complete!")
Python Version: 3.9.23 | packaged by conda-forge | (main, Jun 4 2025, 18:02:02) [Clang 18.1.8 ] Biopython Version: 1.85 Environment Setup Complete!
In [99]:
import os
from Bio import SeqIO
from Bio.Blast import NCBIWWW
from Bio.Blast import NCBIXML
Part 1: Sequence Evaluation¶
Objective: Verify the identity of the provided "Target-X" sequence against the SwissProt database to ensure we are working with the correct biological entity.
The code below loads the raw FASTA sequence and performs a local alignment check (or BLAST) to confirm it matches the Human Interleukin-2 (IL-2) Precursor.
In [100]:
fasta_filename = "Target-X.fasta"
# --- Step 1: Read the FASTA file and Print Sequence ---
print("--- Reading Sequence ---")
record = SeqIO.read(fasta_filename, "fasta")
print(f"ID: {record.id}")
print(f"Description: {record.description}")
print(f"Sequence: {record.seq}")
print(f"Length: {len(record.seq)} aa\n")
--- Reading Sequence --- ID: Target_X Description: Target_X = Human IL-2 Precursor Sequence: MYRMQLLSCIALSLALVTNSAPTSSSTKKTQLQLEHLLLDLQMVILNGINNYKNPKLTRMLTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGSETTFMCEYADEKTATIVEFLNRWITFCQSIISTLT Length: 155 aa
Discussion of Results:
The input sequence was successfully loaded using Biopython. The file header identifies the gene as Target_X (Human IL-2 Precursor). The initial check confirms the sequence length is 155 amino acids, which corresponds to the full-length precursor including the signal peptide.
2. Evaluation of Sequence Data (BLAST)¶
Objective: Verify the correctness of the sequence by matching it against the NCBI protein database. This ensures we are designing the strategy for the correct human isoform.
In [102]:
from Bio.Blast import NCBIWWW, NCBIXML
from Bio import SeqIO
# 1. Read FASTA and Define Length (Crucial Step)
record = SeqIO.read("Target-X.fasta", "fasta")
query_len = len(record.seq) # <--- Defined here
# 2. Run BLAST
print(f"Blasting {record.id}...")
result_handle = NCBIWWW.qblast("blastp", "nr", record.seq)
blast_record = NCBIXML.read(result_handle)
# 3. Process First Hit
if blast_record.alignments:
# Get the first alignment
alignment = blast_record.alignments[0]
# Get the first High Scoring Pair (HSP)
hsp = alignment.hsps[0]
# --- Calculate Query Coverage ---
# Coverage = (Aligned Length / Total Query Length) * 100
# Note: hsp.align_length includes gaps, so using query span is safer for coverage
q_start = hsp.query_start
q_end = hsp.query_end
coverage = (hsp.align_length / query_len) * 100
print("-" * 30)
print(f"TOP HIT: {alignment.title[:60]}...") # Truncate title for readability
print(f"Accession: {alignment.accession}")
print(f"Query Coverage: {coverage:.2f}%")
print(f"E-value: {hsp.expect}")
print(f"Bit Score: {hsp.bits}")
print(f"Identities: {hsp.identities}/{hsp.align_length} ({hsp.identities/hsp.align_length:.1%})")
print("-" * 30)
# --- Print Full Alignment ---
print("\nFULL ALIGNMENT:")
# Iterate through the alignment strings in chunks of 60
chunk_size = 60
for i in range(0, hsp.align_length, chunk_size):
# Slice the strings
q_chunk = hsp.query[i:i+chunk_size]
m_chunk = hsp.match[i:i+chunk_size]
s_chunk = hsp.sbjct[i:i+chunk_size]
# Calculate current positions for the side numbers
# Note: query_start is 1-based
current_q_start = hsp.query_start + i
current_s_start = hsp.sbjct_start + i
print(f"Query {current_q_start:<4} {q_chunk}")
print(f" {m_chunk}")
print(f"Sbjct {current_s_start:<4} {s_chunk}")
print()
else:
print("No alignments found.")
result_handle.close()
Blasting Target_X...
------------------------------
TOP HIT: ref|NP_000577.2| interleukin-2 precursor [Homo sapiens] >ref...
Accession: NP_000577
Query Coverage: 100.00%
E-value: 4.70453e-103
Bit Score: 302.753
Identities: 153/155 (98.7%)
------------------------------
FULL ALIGNMENT:
Query 1 MYRMQLLSCIALSLALVTNSAPTSSSTKKTQLQLEHLLLDLQMVILNGINNYKNPKLTRM
MYRMQLLSCIALSLALVTNSAPTSSSTKKTQLQLEHLLLDLQM ILNGINNYKNPKLTRM
Sbjct 1 MYRMQLLSCIALSLALVTNSAPTSSSTKKTQLQLEHLLLDLQM-ILNGINNYKNPKLTRM
Query 61 LTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGS
LTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGS
Sbjct 61 LTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGS
Query 121 ETTFMCEYADEKTATIVEFLNRWITFCQSIISTLT
ETTFMCEYADE TATIVEFLNRWITFCQSIISTLT
Sbjct 121 ETTFMCEYADE-TATIVEFLNRWITFCQSIISTLT
Discussion of Results:
The BLASTP search against the non-redundant (nr) database returned a top hit for Interleukin-2 precursor [Homo sapiens].
- Accession: NP_000577.2
- Query Coverage: 100%
- Identity: 98.7% (153/155 residues)
- E-value: ~4.7e-103 (Highly significant)
Conclusion: The sequence is verified as Human IL-2. The slight variance (98.7% vs 100%) suggests a specific variant or minor sequencing difference, but it is sufficiently homologous for our expression strategy.
**3. Codon Optimization for *Spodoptera frugiperda***¶
Objective: Insect cells (Sf9/Sf21) have different codon usage biases than humans. We perform an initial "naive" reverse translation and then optimize the DNA sequence specifically for Spodoptera frugiperda (TaxID: 7108) to maximize translation efficiency.
In [ ]:
import dnachisel
from dnachisel import DnaOptimizationProblem, EnforceTranslation, CodonOptimize, AvoidPattern, EnforceGCContent
from Bio import SeqIO
from Bio.Seq import Seq
# --- Configuration ---
# Input File (The mature protein we generated in the previous step)
input_fasta = "Target-X.fasta"
output_fasta = "Target-X_optimized_dna.fasta"
# Use the TaxID (7108) so dnachisel fetches it from the web automatically
#Taxon ID 7108 refers to Spodoptera frugiperda,
species_name = "7108"
# Cloning Sites (To be added at ends, and avoided internally)
site_5_prime = "GGATCC" # BamHI
site_3_prime = "CTCGAG" # XhoI
def get_naive_dna(protein_seq):
"""
Generates a simple, non-optimized DNA sequence from a protein
to serve as the starting point for optimization.
"""
# Simple reverse translation using standard table (will be optimized later)
# We use 'Met' (ATG) for M, 'Trp' (TGG) for W, etc.
# For others, we just pick a common codon to start.
back_trans = {
'A': 'GCT', 'C': 'TGT', 'D': 'GAT', 'E': 'GAA', 'F': 'TTC',
'G': 'GGT', 'H': 'CAT', 'I': 'ATT', 'K': 'AAA', 'L': 'TTA',
'M': 'ATG', 'N': 'AAT', 'P': 'CCT', 'Q': 'CAA', 'R': 'CGT',
'S': 'TCT', 'T': 'ACT', 'V': 'GTT', 'W': 'TGG', 'Y': 'TAT',
'*': 'TAA'
}
dna_seq = "".join([back_trans.get(aa, 'NNN') for aa in protein_seq])
return dna_seq
# --- Main Optimization Workflow ---
# 1. Load Protein Sequence
print(f"--- Loading {input_fasta} ---")
record = SeqIO.read(input_fasta, "fasta")
protein_seq = str(record.seq)
print(f"Protein Length: {len(protein_seq)} aa")
# 2. Generate Starting DNA
naive_dna = get_naive_dna(protein_seq)
# --- Corrected Optimization Section ---
print(f"\n--- Setting up Optimization for {species_name} ---")
# Define the problem with the correct parameter names (mini/maxi)
problem = DnaOptimizationProblem(
sequence=naive_dna,
constraints=[
EnforceTranslation(), # Lock the amino acid sequence
AvoidPattern(site_5_prime), # Prevent internal BamHI sites
AvoidPattern(site_3_prime), # Prevent internal XhoI sites
EnforceGCContent(mini=0.3, maxi=0.7, window=50) # CORRECTED: use mini/maxi
],
objectives=[
CodonOptimize(species=species_name) # Optimize for Spodoptera frugiperda
]
)
# Solve
print("Optimizing... (This matches codons to the host tRNA pool)")
problem.resolve_constraints()
problem.optimize()
# Get Result
optimized_seq = problem.sequence
print("\n[SUCCESS] Optimization Complete!")
# --- Final Construction & Stats ---
final_construct = site_5_prime + optimized_seq + site_3_prime
gc_content = (final_construct.count("G") + final_construct.count("C")) / len(final_construct) * 100
print("-" * 40)
print(f"Final Construct: [BamHI] - [Gene] - [XhoI]")
print(f"Length: {len(final_construct)} bp")
print(f"GC Content: {gc_content:.1f}%")
print(f"Sequence Preview: {final_construct[:30]}...{final_construct[-30:]}")
print("-" * 40)
# Save
with open(output_fasta, "w") as f:
f.write(f">Target-X_Optimized_Sf9\n{final_construct}\n")
print(f"Saved to: {output_fasta}")
--- Loading Target-X.fasta --- Protein Length: 155 aa --- Setting up Optimization for 7108 --- Optimizing... (This matches codons to the host tRNA pool)
[SUCCESS] Optimization Complete! ---------------------------------------- Final Construct: [BamHI] - [Gene] - [XhoI] Length: 477 bp GC Content: 56.6% Sequence Preview: GGATCCATGTACCGTATGCAGCTGCTGTCC...CAGTCCATCATCTCCACCCTGACCCTCGAG ---------------------------------------- Saved to: Target-X_optimized_dna.fasta
In [ ]:
import matplotlib.pyplot as plt
from dnachisel import DnaOptimizationProblem
# --- 1. Text-Based Report ---
print("="*60)
print(" DNA CHISEL OPTIMIZATION REPORT ")
print("="*60)
print("\n--- [A] Constraints Check ---")
print(problem.constraints_text_summary())
print("\n--- [B] Objectives Check (Codon Adaptation) ---")
print(problem.objectives_text_summary())
# --- 2. Visual Report (GC Content & CAI) ---
# We will compare the 'Before' (Naive) and 'After' (Optimized) sequences
print("\n--- [C] Generating Visual Report... ---")
# Create a sliding window plot for GC Content
window = 50
def get_gc_window(seq, win_size):
return [
(seq[i : i + win_size].count("G") + seq[i : i + win_size].count("C")) / win_size
for i in range(len(seq) - win_size)
]
gc_before = get_gc_window(problem.sequence_before, window)
gc_after = get_gc_window(problem.sequence, window)
plt.figure(figsize=(12, 6))
# Plot GC Content
plt.subplot(2, 1, 1)
plt.plot(gc_before, label="Before Optimization", color="red", alpha=0.6)
plt.plot(gc_after, label="After Optimization (Sf9)", color="blue", alpha=0.8)
plt.axhline(0.5, color="gray", linestyle="--", label="50% GC")
plt.title(f"GC Content Profile (Window={window}bp)")
plt.ylabel("GC Fraction")
plt.legend()
plt.grid(True, alpha=0.3)
# Plot Local CAI (Codon Adaptation Index) - approximated by matching frequency
# (This is a simplified view for the report)
plt.subplot(2, 1, 2)
plt.plot([0]*len(gc_before), color="white") # Spacer
plt.text(0.5, 0.5,
f"Optimization Summary:\n\n"
f"Species: Spodoptera frugiperda (Sf9)\n"
f"Total Length: {len(problem.sequence)} bp\n"
f"Constraints Passed: {problem.all_constraints_pass()}\n"
f"Restriction Sites Removed: BamHI, XhoI\n",
ha='center', va='center', transform=plt.gca().transAxes, fontsize=12,
bbox=dict(facecolor='white', alpha=0.8, edgecolor='black'))
plt.axis('off')
plt.tight_layout()
plt.show()
# --- 3. GenBank Export ---
# This file can be opened in SnapGene or ApE to see exact mutations
output_gb = "Target-X_Optimized.gb"
record = problem.to_record(filepath=output_gb)
print(f"\n[SUCCESS] Detailed GenBank report saved to: {output_gb}")
print("You can open this file in SnapGene/ApE to see the exact mutations.")
============================================================
DNA CHISEL OPTIMIZATION REPORT
============================================================
--- [A] Constraints Check ---
===> SUCCESS - all constraints evaluations pass
✔PASS ┍ EnforceTranslation[0-465]
│ Enforced by nucleotides restrictions
✔PASS ┍ AvoidPattern[0-465](pattern:GGATCC)
│ Passed. Pattern not found !
✔PASS ┍ AvoidPattern[0-465](pattern:CTCGAG)
│ Passed. Pattern not found !
✔PASS ┍ EnforceGCContent[0-465](mini:0.30, maxi:0.70, window:50)
│ Passed !
--- [B] Objectives Check (Codon Adaptation) ---
===> TOTAL OBJECTIVES SCORE: 0
✔ 0 ┍ MaximizeCAI[0-465](7108)
│ Codon opt. on window 0-465 scored -0.00E+00
--- [C] Generating Visual Report... ---
[SUCCESS] Detailed GenBank report saved to: Target-X_Optimized.gb You can open this file in SnapGene/ApE to see the exact mutations.
Strategy & Constraints:
We utilized DNA Chisel with the following parameters:
- Host: Spodoptera frugiperda (TaxID 7108).
- Restriction Sites: Enforced avoidance of BamHI and XhoI internally (to reserve them for cloning).
- GC Content: Constrained between 30% and 70% to prevent synthesis issues.
- Codon Adaptation Index (CAI): Maximized for the host organism.
Discussion of Results:
The optimization successfully resolved all constraints. The algorithm replaced rare codons with those preferred by the insect host tRNA pool. The GC Content Profile (visualized in the generated plot) shifts from the erratic red line (naive) to the stable blue line (optimized), ensuring a balanced GC content suitable for stable expression.
4. Signal Peptide Prediction & Removal¶
Objective: To express the protein in the secretory pathway of silkworms/insect cells, we must first identify and remove the native human signal peptide, as it may not process efficiently in insect cells.
In [ ]:
import biolib
print("Loading SignalP-6...")
signalp_6 = biolib.load('DTU/SignalP-6')
input_filename = "Target-X.fasta"
output_dir = "output"
# Define the arguments string
# --organism eukarya: Best for human/insect proteins
# --format png: Ensures we get the plot image
# --mode fast: Quicker results
cli_args = f"--fastafile {input_filename} --output_dir {output_dir} --format all --organism eukarya --mode slow"
print(f"Running SignalP job... (Results will be in '{output_dir}')")
job = signalp_6.cli(args=cli_args)
job.save_files(output_dir) # Downloads the result files to your local folder
print("Job complete!")
Loading SignalP-6... 2026-02-08 14:18:44,805 | INFO : Loaded project DTU/SignalP-6:0.0.56 Running SignalP job... (Results will be in 'output') 2026-02-08 14:18:46,881 | INFO : Job "31e85d36-955f-4a9f-8bbb-d58333896733" is starting... 2026-02-08 14:18:55,856 | INFO : Cloud: The job has been queued. Please wait... 2026-02-08 14:19:07,992 | INFO : Cloud: Initializing 2026-02-08 14:19:07,994 | INFO : Cloud: Downloading Source Files... 2026-02-08 14:19:07,995 | INFO : Cloud: Pulling images... 2026-02-08 14:19:07,996 | INFO : Cloud: Computing... Predicting 1/6: 100% 1/1 [00:03<00:00, 3.36s/sequences] Predicting 2/6: 100% 1/1 [00:03<00:00, 3.23s/sequences] Predicting 3/6: 100% 1/1 [00:03<00:00, 3.32s/sequences] Predicting 4/6: 100% 1/1 [00:03<00:00, 3.20s/sequences] Predicting 5/6: 100% 1/1 [00:03<00:00, 3.17s/sequences] Predicting 6/6: 100% 1/1 [00:03<00:00, 3.20s/sequences] Writing files: 100% 1/1 [00:00<00:00, 67.08it/s] 2026-02-08 14:20:26,498 | INFO : Cloud: Computation finished 2026-02-08 14:20:26,498 | INFO : Cloud: Result Ready 2026-02-08 14:20:30,661 | INFO : Saving 9 files to output... 2026-02-08 14:20:31,530 | INFO : - output/output.md 2026-02-08 14:20:32,471 | INFO : - output/prediction_results.txt 2026-02-08 14:20:33,407 | INFO : - output/output.gff3 2026-02-08 14:20:34,329 | INFO : - output/region_output.gff3 2026-02-08 14:20:35,180 | INFO : - output/processed_entries.fasta 2026-02-08 14:20:36,077 | INFO : - output/output_Target_X____Human_IL_2_Precursor_plot.txt 2026-02-08 14:20:37,465 | INFO : - output/output_Target_X____Human_IL_2_Precursor_plot.png 2026-02-08 14:20:38,800 | INFO : - output/output_Target_X____Human_IL_2_Precursor_plot.eps 2026-02-08 14:20:39,698 | INFO : - output/output.json Job complete!
In [ ]:
import os
import glob
from IPython.display import Image, display
from Bio import SeqIO
# --- Configuration ---
# Set this to the directory where your output files are located
# If they are in the same folder as your notebook, use "."
output_dir = "output"
# --- Step 1: Find and Display the SignalP Plot ---
print("--- [1] SignalP 6.0 Prediction Plot ---")
# Search for the PNG file dynamically to avoid typing the long filename
png_files = glob.glob(os.path.join(output_dir, "*_plot.png"))
if png_files:
plot_path = png_files[0] # Pick the first match
print(f"Displaying plot: {os.path.basename(plot_path)}")
display(Image(filename=plot_path))
else:
print("No .png plot found in the directory.")
# --- Step 2: Read and Print the Mature Sequence ---
print("\n--- [2] Mature Sequence Extraction ---")
mature_fasta_path = os.path.join(output_dir, "processed_entries.fasta")
if os.path.exists(mature_fasta_path):
# Read the FASTA file generated by SignalP
record = SeqIO.read(mature_fasta_path, "fasta")
print(f"Header: {record.description}")
print(f"Mature Length: {len(record.seq)} amino acids")
print(f"Sequence:\n{record.seq}")
# Optional: Verify if the signal peptide (first 20 AA) was removed
# The full precursor starts with "MYRM...", mature should start with "APTS..."
if str(record.seq).startswith("APTS"):
print("\n[SUCCESS] Signal peptide (1-20) successfully removed.")
else:
print("\n[WARNING] Sequence start does not match expected mature N-terminus.")
else:
print(f"Error: Could not find '{mature_fasta_path}'")
--- [1] SignalP 6.0 Prediction Plot --- Displaying plot: output_Target_X____Human_IL_2_Precursor_plot.png
--- [2] Mature Sequence Extraction --- Header: Target_X = Human IL-2 Precursor Mature Length: 135 amino acids Sequence: APTSSSTKKTQLQLEHLLLDLQMVILNGINNYKNPKLTRMLTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGSETTFMCEYADEKTATIVEFLNRWITFCQSIISTLT [SUCCESS] Signal peptide (1-20) successfully removed.
Discussion of Results:
SignalP 6.0 analysis identified a strong signal peptide at the N-terminus.
- Cleavage Site: Between position 20 and 21.
- Action: The first 20 amino acids were removed.
- Mature Sequence: The remaining 135 amino acids constitute the mature IL-2 protein, ready for the addition of an insect-specific leader.
5. Vector Construction: Fusion Design¶
Objective: Assemble the final construct for the shuttle vector
In [ ]:
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from dnachisel import DnaOptimizationProblem, EnforceTranslation, CodonOptimize, AvoidPattern, EnforceGCContent
# --- Configuration ---
# 1. Define the Parts (Amino Acid Level)
# Honeybee Melittin Signal Peptide (Highly efficient for Sf9 secretion)
signal_peptide_aa = "MKFLVNVALVFMVVYISYIYA"
# Linker + 6xHis Tag (C-terminal)
tag_aa = "GSGHHHHHH"
# 2. Load the Mature Protein from signal-IP output results
input_fasta = "output/processed_entries.fasta"
try:
record = SeqIO.read(input_fasta, "fasta")
mature_protein_aa = str(record.seq)
print(f"Loaded Mature Protein: {len(mature_protein_aa)} aa")
except FileNotFoundError:
print(f"Error: '{input_fasta}' not found. Please run the SignalP parsing step first.")
# 3. Assemble the Full Fusion Protein
full_fusion_aa = signal_peptide_aa + mature_protein_aa + tag_aa
print("\n--- Constructed Fusion Protein ---")
print(f"Structure: [HBM Signal] - [Mature IL-2] - [Linker] - [6xHis]")
print(f"Total Length: {len(full_fusion_aa)} aa")
print(f"Sequence: {full_fusion_aa}")
# --- Optimization Step ---
# 4. Generate Naive DNA (Starting point)
# Simple back-translation map (will be overwritten by optimizer)
back_trans = {
'A': 'GCT', 'C': 'TGT', 'D': 'GAT', 'E': 'GAA', 'F': 'TTC',
'G': 'GGT', 'H': 'CAT', 'I': 'ATT', 'K': 'AAA', 'L': 'TTA',
'M': 'ATG', 'N': 'AAT', 'P': 'CCT', 'Q': 'CAA', 'R': 'CGT',
'S': 'TCT', 'T': 'ACT', 'V': 'GTT', 'W': 'TGG', 'Y': 'TAT'
}
naive_dna = "".join([back_trans.get(aa, 'NNN') for aa in full_fusion_aa])
# 5. Define Optimization Problem
print("\n--- Starting Optimization for Spodoptera frugiperda (TaxID: 7108) ---")
# Flanking sites to avoid INTERNALLY (we add them manually at the ends later)
site_5_prime = "GGATCC" # BamHI
site_3_prime = "CTCGAG" # XhoI
problem = DnaOptimizationProblem(
sequence=naive_dna,
constraints=[
EnforceTranslation(), # Lock the Amino Acid sequence
AvoidPattern(site_5_prime), # No BamHI inside
AvoidPattern(site_3_prime), # No XhoI inside
EnforceGCContent(mini=0.3, maxi=0.7, window=50) # Balanced GC
],
objectives=[
CodonOptimize(species="7108") # Sf9 Codon Table
]
)
# 6. Run Optimization
print("Optimizing... (Matches host tRNA pool & removes restriction sites)")
problem.resolve_constraints()
problem.optimize()
optimized_core_dna = problem.sequence
# --- Final Assembly & Export ---
# 7. Add Cloning Sites
# Final Structure: BamHI - KOZAK - ORF - Stop - XhoI
# Note: "GCCGCC" is a strong Kozak sequence for initiation in insects
kozak = "GCCGCC"
stop_codon = "TAA"
final_dna = site_5_prime + kozak + optimized_core_dna + stop_codon + site_3_prime
print("\n[SUCCESS] Optimization Complete!")
print("-" * 60)
print(f"Final Construct Logic: [BamHI]-[{kozak}]-[Fusion Gene]-[Stop]-[XhoI]")
print(f"Total Length: {len(final_dna)} bp")
print(f"GC Content: {(final_dna.count('G') + final_dna.count('C')) / len(final_dna) * 100:.1f}%")
print("-" * 60)
# --- Corrected Step 8: Save Files ---
output_fasta = "Target-X_Full_Construct.fasta"
with open(output_fasta, "w") as f:
f.write(f">Target-X_Sf9_Expression_Vector\n{final_dna}\n")
# --- Fix for GenBank Error ---
from Bio.SeqFeature import SeqFeature, FeatureLocation
output_gb = "Target-X_Full_Construct.gb"
# 1. Create the SeqRecord
final_record = SeqRecord(
Seq(final_dna),
id="Target-X_Sf9",
name="TargetX", # GenBank requires a 'name' (max 16 chars)
description="Optimized IL-2 with HBM Signal and His-Tag"
)
# 2. Add the MISSING 'molecule_type' annotation (Crucial Fix)
final_record.annotations["molecule_type"] = "DNA"
# 3. Add Feature Location (CDS)
# Note: FeatureLocation start/end must be integers
start_pos = len(site_5_prime) + len(kozak)
end_pos = start_pos + len(optimized_core_dna)
final_record.features.append(SeqFeature(
FeatureLocation(start_pos, end_pos),
type="CDS",
qualifiers={
"translation": full_fusion_aa,
"codon_start": 1,
"product": "IL-2 Fusion Protein"
}
))
# 4. Write to File
with open(output_gb, "w") as f:
SeqIO.write(final_record, f, "genbank")
print(f"Saved FASTA to: {output_fasta}")
print(f"Saved GenBank to: {output_gb}")
Loaded Mature Protein: 135 aa --- Constructed Fusion Protein --- Structure: [HBM Signal] - [Mature IL-2] - [Linker] - [6xHis] Total Length: 165 aa Sequence: MKFLVNVALVFMVVYISYIYAAPTSSSTKKTQLQLEHLLLDLQMVILNGINNYKNPKLTRMLTFKFYMPKKATELKHLQCLEEELKPLEEVLNLAQSKNFHLRPRDLISNINVIVLELKGSETTFMCEYADEKTATIVEFLNRWITFCQSIISTLTGSGHHHHHH --- Starting Optimization for Spodoptera frugiperda (TaxID: 7108) --- Optimizing... (Matches host tRNA pool & removes restriction sites)
[SUCCESS] Optimization Complete! ------------------------------------------------------------ Final Construct Logic: [BamHI]-[GCCGCC]-[Fusion Gene]-[Stop]-[XhoI] Total Length: 516 bp GC Content: 56.4% ------------------------------------------------------------ Saved FASTA to: Target-X_Full_Construct.fasta Saved GenBank to: Target-X_Full_Construct.gb
Construct Design:
- Secretion Signal: Honeybee Melittin (HBM) signal peptide (MKFLVNVALVFMVVYISYIYA...). This is a "gold standard" leader for high-level secretion in Baculovirus systems.
- Target Protein: Mature Human IL-2 (135 aa).
- Linker & Tag: A C-terminal GSG linker followed by a 6xHis Tag for Nickel affinity purification.
Discussion of Results:
The components were successfully assembled in silico.
- Full Fusion Length: 165 amino acids.
- Structure: [HBM Signal] - [Mature IL-2] - [Linker] - [6xHis]
- Final Construct Optimization & Export Objective: Optimize the fully assembled fusion gene (Signal + Gene + Tag) and add the cloning sites for insertion into the Baculovirus shuttle vector.
In [ ]:
import matplotlib.pyplot as plt
from dnachisel import DnaOptimizationProblem
# --- 1. Text-Based Report ---
print("="*60)
print(" FULL CONSTRUCT OPTIMIZATION REPORT ")
print("="*60)
# Check if 'problem' exists from the previous cell
if 'problem' not in locals():
print("Error: The variable 'problem' is missing.")
print("Please run the 'Full Construct Optimization' code block first.")
else:
print("\n--- [A] Constraints Check ---")
print(problem.constraints_text_summary())
print("\n--- [B] Objectives Check (Codon Adaptation) ---")
print(problem.objectives_text_summary())
# --- 2. Visual Report (GC Content) ---
print("\n--- [C] Generating Visual Report... ---")
# Helper function for sliding window GC calculation
def get_gc_window(seq, win_size):
return [
(seq[i : i + win_size].count("G") + seq[i : i + win_size].count("C")) / win_size
for i in range(len(seq) - win_size)
]
# Calculate GC profiles
window = 50
gc_before = get_gc_window(problem.sequence_before, window)
gc_after = get_gc_window(problem.sequence, window)
# Create Plot
plt.figure(figsize=(12, 8))
# Subplot 1: GC Content Comparison
plt.subplot(2, 1, 1)
plt.plot(gc_before, label="Before (Naive Reverse Translation)", color="red", alpha=0.5)
plt.plot(gc_after, label="After (Sf9 Optimized)", color="blue", linewidth=2)
plt.axhline(0.5, color="gray", linestyle="--", label="50% GC Reference")
plt.title(f"GC Content Profile: Full Construct (Signal + Gene + Tag)")
plt.ylabel("GC Fraction")
plt.legend(loc="upper right")
plt.grid(True, alpha=0.3)
# Subplot 2: Optimization Summary Box
plt.subplot(2, 1, 2)
plt.plot([0]*len(gc_before), color="white") # Invisible line to set axis
plt.axis('off') # Hide axis
summary_text = (
f"OPTIMIZATION SUMMARY\n"
f"-----------------------------------\n"
f"Host Species: Spodoptera frugiperda (Sf9)\n"
f"Input Length: {len(problem.sequence)} bp\n"
f"Optimization Method: DNA Chisel (CodonOptimize)\n"
f"Constraints Passed: {problem.all_constraints_pass()}\n"
f"Restricted Sites Removed: BamHI, XhoI\n"
f"Included Elements: HBM Signal, Mature IL-2, 6xHis Tag"
)
plt.text(0.5, 0.5, summary_text,
ha='center', va='center', transform=plt.gca().transAxes,
fontsize=14, fontfamily='monospace',
bbox=dict(facecolor='#f0f0f0', alpha=1.0, edgecolor='black', boxstyle='round,pad=1'))
plt.tight_layout()
plt.show()
# --- 3. GenBank Export ---
# This GenBank file contains the optimization history (which codons were changed)
output_gb = "Target-X_Full_Construct_Optimized.gb"
try:
problem.to_record(filepath=output_gb)
print(f"\n[SUCCESS] Detailed GenBank report saved to: {output_gb}")
print("Note: This file highlights exactly which codons were changed during optimization.")
except Exception as e:
print(f"Error saving GenBank file: {e}")
============================================================
FULL CONSTRUCT OPTIMIZATION REPORT
============================================================
--- [A] Constraints Check ---
===> SUCCESS - all constraints evaluations pass
✔PASS ┍ EnforceTranslation[0-495]
│ Enforced by nucleotides restrictions
✔PASS ┍ AvoidPattern[0-495](pattern:GGATCC)
│ Passed. Pattern not found !
✔PASS ┍ AvoidPattern[0-495](pattern:CTCGAG)
│ Passed. Pattern not found !
✔PASS ┍ EnforceGCContent[0-495](mini:0.30, maxi:0.70, window:50)
│ Passed !
--- [B] Objectives Check (Codon Adaptation) ---
===> TOTAL OBJECTIVES SCORE: 0
✔ 0 ┍ MaximizeCAI[0-495](7108)
│ Codon opt. on window 0-495 scored -0.00E+00
--- [C] Generating Visual Report... ---
[SUCCESS] Detailed GenBank report saved to: Target-X_Full_Construct_Optimized.gb Note: This file highlights exactly which codons were changed during optimization.
Discussion of Results:
The final DNA sequence was generated and optimized for Sf9.
- Cloning Sites: BamHI (5') and XhoI (3') were added.
- Kozak Sequence: A GCCGCC Kozak consensus was inserted before the start codon to enhance translation initiation.
- Final stats: 516 bp length, 56.4% GC content.
- Output: The file Target-X_Full_Construct_Optimized.gb was saved, containing all feature annotations for cloning.
Part 2: Computational Analysis of Target-X¶
7. Physicochemical Profiling¶
Objective: Calculate key physical parameters to inform buffer selection and purification strategies.
In [34]:
import sys
from Bio import SeqIO
from Bio.SeqUtils.ProtParam import ProteinAnalysis
from Bio.SeqUtils import ProtParamData
import matplotlib.pyplot as plt
# Ensure plots appear inline
%matplotlib inline
print(f"Python Version: {sys.version}")
print("Libraries loaded successfully.")
Python Version: 3.9.23 | packaged by conda-forge | (main, Jun 4 2025, 18:02:02) [Clang 18.1.8 ] Libraries loaded successfully.
In [35]:
from Bio import SeqIO
from Bio.SeqUtils.ProtParam import ProteinAnalysis
import os
# --- Step 1: Load the Mature Sequence from File ---
input_file = "output/processed_entries.fasta"
if os.path.exists(input_file):
# Read the first record in the fasta file
record = SeqIO.read(input_file, "fasta")
# Convert Seq object to string for ProteinAnalysis
mature_seq = str(record.seq)
print(f"--- Loaded Sequence ---")
print(f"Source: {input_file}")
print(f"Header: {record.description}")
print(f"Length: {len(mature_seq)} amino acids")
print(f"Sequence Preview: {mature_seq[:20]}...")
# --- Step 2: Initialize Biopython Analysis ---
analysed_seq = ProteinAnalysis(mature_seq)
# --- Step 3: Calculate Parameters ---
mw = analysed_seq.molecular_weight()
pi = analysed_seq.isoelectric_point()
gravy = analysed_seq.gravy()
instability = analysed_seq.instability_index()
# --- Step 4: Display Results ---
print(f"\n--- Physicochemical Profile (Mature IL-2) ---")
print(f"Molecular Weight: {mw/1000:.2f} kDa")
print(f"Isoelectric Point: {pi:.2f}")
print(f"GRAVY Score: {gravy:.2f} (Positive = Hydrophobic)")
print(f"Instability Index: {instability:.2f}")
# Interpretation logic
stability_status = "Unstable (In Vitro)" if instability > 40 else "Stable"
print(f"Stability Prediction: {stability_status}")
else:
print(f"Error: File '{input_file}' not found. Please check the path or run the SignalP step first.")
--- Loaded Sequence --- Source: output/processed_entries.fasta Header: Target_X = Human IL-2 Precursor Length: 135 amino acids Sequence Preview: APTSSSTKKTQLQLEHLLLD... --- Physicochemical Profile (Mature IL-2) --- Molecular Weight: 15.65 kDa Isoelectric Point: 7.92 GRAVY Score: -0.17 (Positive = Hydrophobic) Instability Index: 52.12 Stability Prediction: Unstable (In Vitro)
Discussion of Results:
- Molecular Weight: 15.65 kDa. This is a small protein, suitable for size exclusion chromatography if needed.
- Isoelectric Point (pI): 7.92. The protein is slightly basic.
- Purification Note: At physiological pH (7.4), the protein will have a near-neutral to slightly positive charge. For Ion Exchange, Cation Exchange (CEX) would be effective if the buffer is pH < 7.5, or Anion Exchange (AEX) if buffer pH > 8.5.
- GRAVY: -0.17. Negative value indicates the protein is overall Hydrophilic (soluble), which is good for secretory expression.
- Instability Index: 52.12 (>40). This classifies the protein as Unstable in vitro, suggesting we may need stabilizing additives (e.g., glycerol, salt) or rapid purification protocols to prevent degradation.
8. Aggregation Propensity Scan¶
Objective: Identify local hydrophobic patche
In [36]:
def scan_aggregation_prone_regions(sequence, window_size=7, threshold=1.5):
kd_scale = ProtParamData.kd # Kyte-Doolittle hydrophobicity scale
prone_regions = []
scores = []
hydrophobic_aas = set("VILMFWA")
print(f"{'Pos':<5} | {'Sequence':<10} | {'Score':<5} | {'Issue Detected'}")
print("-" * 50)
for i in range(len(sequence) - window_size + 1):
segment = sequence[i : i + window_size]
# Calculate Average Hydrophobicity
avg_score = sum(kd_scale.get(aa, 0) for aa in segment) / window_size
scores.append(avg_score)
# Count consecutive hydrophobic residues
hydrophobic_count = sum(1 for aa in segment if aa in hydrophobic_aas)
is_prone = False
reason = ""
if avg_score > threshold:
is_prone = True
reason = f"High KD Score ({avg_score:.2f})"
elif hydrophobic_count >= 5:
is_prone = True
reason = f"Dense Hydrophobic Cluster ({hydrophobic_count}/7)"
if is_prone:
prone_regions.append((i+1, segment, avg_score, reason))
print(f"{i+1:<5} | {segment:<10} | {avg_score:.2f} | {reason}")
return scores, prone_regions
# Run the scan
print("--- Aggregation Prone Regions (Mature Seq) ---")
kd_scores, flagged_regions = scan_aggregation_prone_regions(mature_seq)
# Plot the Hydrophobicity Profile
plt.figure(figsize=(10, 4))
plt.plot(kd_scores, color='purple', label='Hydrophobicity (Window=7)')
plt.axhline(y=1.5, color='r', linestyle='--', label='Threshold')
plt.title("Hydrophobicity Profile (Aggregation Risk)")
plt.xlabel("Residue Position")
plt.ylabel("Kyte-Doolittle Score")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
--- Aggregation Prone Regions (Mature Seq) --- Pos | Sequence | Score | Issue Detected -------------------------------------------------- 17 | LLLDLQM | 1.44 | Dense Hydrophobic Cluster (5/7) 18 | LLDLQMV | 1.50 | Dense Hydrophobic Cluster (5/7) 19 | LDLQMVI | 1.60 | High KD Score (1.60) 20 | DLQMVIL | 1.60 | High KD Score (1.60) 21 | LQMVILN | 1.60 | High KD Score (1.60) 23 | MVILNGI | 2.14 | High KD Score (2.14) 89 | NINVIVL | 2.03 | High KD Score (2.03) 90 | INVIVLE | 2.03 | High KD Score (2.03) 91 | NVIVLEL | 1.93 | High KD Score (1.93) 92 | VIVLELK | 1.87 | High KD Score (1.87) 114 | ATIVEFL | 1.84 | High KD Score (1.84)
Discussion of Results:
The Kyte-Doolittle scan (Window Size = 7) flagged several regions with high hydrophobicity scores (>1.5).
- Critical Region Identified: A dense cluster was detected around residues 90-92 (Sequence: NVIVLEL).
- Risk: These surface-exposed hydrophobic residues are high-risk candidates for driving protein aggregation.
9. Structural Evaluation (Boltz/AlphaFold)¶
Objective: Generate a 3D structural model to visualize the folding and surface properties.
In [ ]:
!pip install boltz -U
In [58]:
import subprocess
import os
import yaml
from Bio import SeqIO
# --- Configuration ---
input_fasta = "output/processed_entries.fasta"
output_dir = "boltz_prediction"
model_type = "boltz-2" # Default model
# Ensure output directory exists
os.makedirs(output_dir, exist_ok=True)
# --- Step 1: Parse FASTA and Create Input YAML ---
print(f"--- [1] Preparing Input for Boltz ---")
# Read the mature sequence
record = SeqIO.read(input_fasta, "fasta")
protein_seq = str(record.seq)
# Create the YAML data structure required by Boltz
# Boltz expects a list of sequences or a specific target format
# (Adjusting based on standard Boltz input schema)
boltz_input = {
"sequences": [
{
"protein": {
"id": "A",
"sequence": protein_seq
}
}
]
}
yaml_path = os.path.join(output_dir, "input.yaml")
with open(yaml_path, "w") as f:
yaml.dump(boltz_input, f, default_flow_style=False)
print(f"Input YAML saved to: {yaml_path}")
print(f"Sequence Length: {len(protein_seq)} residues")
# --- Step 2: Run Boltz Prediction ---
print(f"\n--- [2] Running Boltz Prediction ---")
print("Note: This may take several minutes depending on GPU availability.")
# Construct the command
# cmd: boltz predict input.yaml --out_dir output_dir --use_gpu
cmd = [
"boltz", "predict",
yaml_path,
"--out_dir", output_dir,
"--use_msa_server" # Ensure you have a GPU, otherwise this will fail or be very slow
]
try:
# Run the command and stream output
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
)
# Print output in real-time
for line in process.stdout:
print(line.strip())
process.wait()
if process.returncode == 0:
print(f"\n[SUCCESS] Prediction complete.")
print(f"Results saved in: {output_dir}")
else:
print(f"\n[ERROR] Boltz failed with error:")
print(process.stderr.read())
except FileNotFoundError:
print("\n[ERROR] 'boltz' command not found.")
print("Please ensure Boltz is installed: 'pip install boltz' (and dependencies)")
--- [1] Preparing Input for Boltz --- Input YAML saved to: boltz_prediction/input.yaml Sequence Length: 135 residues --- [2] Running Boltz Prediction --- Note: This may take several minutes depending on GPU availability. Checking input data. Running predictions for 1 structure Processing input data. Generating MSA for boltz_prediction/input.yaml with 1 protein entities. Predicting: | | 0/? [00:00<?, ?it/s] Predicting: 0%| | 0/1 [00:00<?, ?it/s] Predicting DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s] Predicting DataLoader 0: 100%|██████████| 1/1 [00:18<00:00, 0.05it/s]Number of failed examples: 0 Predicting DataLoader 0: 100%|██████████| 1/1 [00:18<00:00, 0.05it/s] [SUCCESS] Prediction complete. Results saved in: boltz_prediction
In [61]:
import py3Dmol
from Bio import PDB
# --- Configuration ---
pdb_filename = "boltz_prediction/boltz_results_input/predictions/input/input_model_0.cif" # Replace with your file
# --- Step 1: Calculate B-factor Range ---
parser = PDB.MMCIFParser(QUIET=True)
try:
structure = parser.get_structure("protein", pdb_filename)
b_factors = [atom.get_bfactor() for atom in structure.get_atoms()]
min_b = min(b_factors) if b_factors else 0
max_b = max(b_factors) if b_factors else 100
print(f"B-factor Range: {min_b:.2f} - {max_b:.2f}")
except FileNotFoundError:
print(f"Error: '{pdb_filename}' not found.")
min_b, max_b = 0, 100
# --- Step 2: Visualisation with Transparent Surface ---
view = py3Dmol.view(width=800, height=600)
with open(pdb_filename, 'r') as f:
pdb_content = f.read()
view.addModel(pdb_content, 'cif')
# Define the color scheme (Red-Orange-Yellow-Green-Blue)
color_style = {
'prop': 'b',
'gradient': 'roygb',
'min': min_b,
'max': max_b
}
# Style 1: The Internal Cartoon (Opaque)
view.setStyle({'cartoon': {'colorscheme': color_style}})
# Style 2: The Surface (Transparent)
# 'opacity': 0.5 makes it 50% see-through
view.addSurface(py3Dmol.VDW, {'opacity': 0.5, 'colorscheme': color_style})
view.zoomTo()
view.show()
B-factor Range: 49.94 - 97.54
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
In [66]:
import numpy as np
import json
import matplotlib.pyplot as plt
# --- Load Data ---
# 1. Global Metrics (JSON)
with open('boltz_prediction/boltz_results_input/predictions/input/confidence_input_model_0.json', 'r') as f:
conf_data = json.load(f)
# 2. Per-Residue pLDDT (NPZ)
plddt_data = np.load('boltz_prediction/boltz_results_input/predictions/input/plddt_input_model_0.npz')
# Extract the array (key is usually 'plddt')
scores = plddt_data['plddt']
# Convert 0-1 scale to 0-100 if necessary
if np.max(scores) <= 1.0:
scores = scores * 100
# --- Plotting ---
plt.figure(figsize=(10, 5))
# Plot the main score line
plt.plot(scores, label='pLDDT (Local Confidence)', color='#0052cc', linewidth=2)
# Add Confidence Bands (AlphaFold Standard)
plt.axhspan(90, 100, color='green', alpha=0.1, label='Very High (>90)')
plt.axhspan(70, 90, color='cyan', alpha=0.1, label='Confident (70-90)')
plt.axhspan(50, 70, color='yellow', alpha=0.1, label='Low (50-70)')
plt.axhspan(0, 50, color='red', alpha=0.1, label='Unreliable (<50)')
# Styling
plt.title(f"Per-Residue Confidence (pLDDT)\nGlobal Confidence: {conf_data.get('confidence_score', 0):.1%}")
plt.xlabel("Residue Index")
plt.ylabel("Score (0-100)")
plt.ylim(0, 100)
plt.legend(loc='lower left')
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()
# Print Summary Table
print(f"{'Metric':<20} | {'Value':<10}")
print("-" * 35)
for key, val in conf_data.items():
if isinstance(val, (int, float)):
print(f"{key:<20} | {val:.4f}")
Metric | Value ----------------------------------- confidence_score | 0.8647 ptm | 0.8450 iptm | 0.0000 ligand_iptm | 0.0000 protein_iptm | 0.0000 complex_plddt | 0.8696 complex_iplddt | 0.0000 complex_pde | 0.6173 complex_ipde | 0.0000
Discussion of Results:
- Model Generation: A 3D structure was successfully generated using the Boltz model.
- Confidence (pLDDT): The global confidence score is 86.5%, which is "Confident" to "Very High". The core of the protein is well-folded (alpha-helical bundle), typical for cytokines like IL-2. The termini show slightly lower confidence, which is expected for flexible regions.
10. Rational Design & Surface Hotspot Analysis¶
Objective: Map hydrophobicity onto the 3D surface to distinguish buried hydrophobic cores (good) from exposed sticky patches (bad), and select a mutation target.
In [91]:
import os
import numpy as np
from Bio import PDB
from Bio.SeqUtils import ProtParamData
from Bio.PDB import PDBIO, MMCIFParser
import py3Dmol
# --- Configuration ---
# Ensure this path is 100% correct relative to your notebook file
cif_path = "boltz_prediction/boltz_results_input/predictions/input/input_model_0.cif"
output_pdb = "Target-X_Hydrophobicity.pdb"
window_size = 7
# --- Step 1: Load Structure & Calculate Hydrophobicity ---
print(f"--- [1] Processing Structure: {cif_path} ---")
# Check if input exists before trying to parse
if not os.path.exists(cif_path):
print(f"ERROR: The file '{cif_path}' was not found. Please check your path.")
output_pdb = None
else:
parser = MMCIFParser(QUIET=True)
try:
structure = parser.get_structure("Target-X", cif_path)
# 1. Extract Residues (Standard AA only)
model = structure[0]
residues = [r for r in model.get_residues() if PDB.is_aa(r)]
if not residues:
raise ValueError("No amino acid residues found in the structure.")
# 2. Get Sequence safely (handling non-standard AAs)
d3to1 = {'ALA':'A', 'CYS':'C', 'ASP':'D', 'GLU':'E', 'PHE':'F', 'GLY':'G', 'HIS':'H',
'ILE':'I', 'LYS':'K', 'LEU':'L', 'MET':'M', 'ASN':'N', 'PRO':'P', 'GLN':'Q',
'ARG':'R', 'SER':'S', 'THR':'T', 'VAL':'V', 'TRP':'W', 'TYR':'Y'}
sequence = [d3to1.get(r.get_resname(), 'X') for r in residues]
# 3. Calculate Sliding Window Scores (Kyte-Doolittle)
kd_scale = ProtParamData.kd
scores = []
for i in range(len(sequence)):
start = max(0, i - window_size // 2)
end = min(len(sequence), i + window_size // 2 + 1)
chunk = sequence[start:end]
# Handle empty chunks or unknown AAs
if chunk:
avg_score = sum(kd_scale.get(aa, 0.0) for aa in chunk) / len(chunk)
else:
avg_score = 0.0
scores.append(avg_score)
# 4. Map Scores to B-factors
min_b, max_b = min(scores), max(scores)
print(f"Hydrophobicity Range: {min_b:.2f} (Hydrophilic) to {max_b:.2f} (Hydrophobic)")
for residue, score in zip(residues, scores):
for atom in residue:
atom.set_bfactor(score)
# 5. Save as PDB
io = PDBIO()
io.set_structure(structure)
io.save(output_pdb)
print(f"SUCCESS: Saved modified structure to: {output_pdb}")
except Exception as e:
print(f"CRITICAL ERROR processing structure: {e}")
output_pdb = None
# --- Step 2: Visualisation ---
print("--- [2] Attempting Visualization ---")
i# --- Debug Step 2: Visualisation ---
import py3Dmol
# 1. Read the content we just saved
with open("Target-X_Hydrophobicity.pdb", 'r') as f:
pdb_content = f.read()
# --- Step 2: Visualisation with Transparent Surface ---
view = py3Dmol.view(width=800, height=600)
view.addModel(pdb_content, 'pdb')
# Define the color scheme (Red-Orange-Yellow-Green-Blue)
color_style = {
'prop': 'b',
'gradient': 'rwb',
'min': -3.0, # Fixed range based on your data
'max': 2.1
}
# Style 1: The Internal Cartoon (Opaque)
view.setStyle({'cartoon': {'colorscheme': color_style}})
# Style 2: The Surface (Transparent)
# 'opacity': 0.5 makes it 50% see-through
view.addSurface(py3Dmol.VDW, {'opacity': 1, 'colorscheme': color_style})
view.zoomTo()
view.show()
--- [1] Processing Structure: boltz_prediction/boltz_results_input/predictions/input/input_model_0.cif --- Hydrophobicity Range: -3.03 (Hydrophilic) to 2.14 (Hydrophobic) SUCCESS: Saved modified structure to: Target-X_Hydrophobicity.pdb --- [2] Attempting Visualization ---
3Dmol.js failed to load for some reason. Please check your browser console for error messages.
In [96]:
from Bio.PDB import PDBParser
from Bio.PDB.SASA import ShrakeRupley
import numpy as np
import py3Dmol
# --- Configuration ---
pdb_filename = "Target-X_Hydrophobicity.pdb"
# Thresholds
hydrophobicity_threshold = 1.0 # Positive = Hydrophobic (GRAVY scale)
rsa_threshold_percent = 0.25 # >25% Exposed = Surface
# Theoretical Max SASA (Tien et al., 2013)
MAX_SASA = {
'ALA': 121.0, 'ARG': 265.0, 'ASN': 187.0, 'ASP': 187.0, 'CYS': 148.0,
'GLU': 214.0, 'GLN': 214.0, 'GLY': 97.0, 'HIS': 216.0, 'ILE': 195.0,
'LEU': 191.0, 'LYS': 230.0, 'MET': 203.0, 'PHE': 228.0, 'PRO': 154.0,
'SER': 143.0, 'THR': 163.0, 'TRP': 264.0, 'TYR': 255.0, 'VAL': 165.0
}
# --- Step 1: Load Structure ---
print(f"--- Analyzing Surface Hydrophobicity (RSA > {rsa_threshold_percent*100}%) ---")
parser = PDBParser(QUIET=True)
structure = parser.get_structure("Target-X", pdb_filename)
# --- Step 2: Calculate Surface Area (SASA) ---
sr = ShrakeRupley()
sr.compute(structure, level="R")
# --- Step 3: Identify Hotspots ---
hotspots = []
print(f"\n{'Residue':<10} | {'Hydrophobicity':<15} | {'RSA %':<10} | {'Status'}")
print("-" * 65)
for residue in structure.get_residues():
res_name = residue.get_resname()
if res_name not in MAX_SASA: continue # Skip non-standard
# Get Hydrophobicity (from B-factor)
b_factors = [atom.get_bfactor() for atom in residue]
hydrophobicity = np.mean(b_factors) if b_factors else 0
# Calculate RSA
sasa = residue.sasa
max_sasa = MAX_SASA[res_name]
rsa = sasa / max_sasa
# Check Condition: Hydrophobic AND Exposed
if hydrophobicity > hydrophobicity_threshold and rsa > rsa_threshold_percent:
hotspots.append(residue)
print(f"{res_name}{residue.id[1]:<5} | {hydrophobicity:<15.2f} | {rsa:<10.1%} | AGGREGATION RISK")
print("-" * 65)
print(f"Total Aggregation Hotspots: {len(hotspots)}")
# --- Step 4: Visualize with Labels ---
view = py3Dmol.view(width=800, height=600)
with open(pdb_filename, 'r') as f:
view.addModel(f.read(), 'pdb')
# Style 1: Transparent White Cartoon (Ghost Mode)
view.setStyle({'cartoon': {'color': 'white', 'opacity': 0.6}})
# Style 2: Highlight Hotspots (Red Spheres)
hotspot_ids = [r.id[1] for r in hotspots]
if hotspot_ids:
# Add Red Spheres
view.addStyle(
{'resi': hotspot_ids},
{'sphere': {'color': 'red', 'radius': 1.0}} # Smaller radius for clarity
)
# Add Labels Loop
for res in hotspots:
label_text = f"{res.get_resname()}{res.id[1]}"
# Add label at the residue position
view.addLabel(label_text, {
'fontSize': 10, # Keep text small
'fontColor': 'black',
'backgroundColor': 'white', # White box for readability
'backgroundOpacity': 0.8,
'borderThickness': 0.5,
'borderColor': 'black',
'inFront': True # Ensure label is always visible
}, {'resi': res.id[1]}) # Anchor to the specific residue ID
view.zoomTo()
view.show()
--- Analyzing Surface Hydrophobicity (RSA > 25.0%) --- Residue | Hydrophobicity | RSA % | Status ----------------------------------------------------------------- MET23 | 1.60 | 43.1% | AGGREGATION RISK VAL24 | 1.60 | 32.6% | AGGREGATION RISK ASN27 | 1.37 | 32.1% | AGGREGATION RISK ASN89 | 1.31 | 28.6% | AGGREGATION RISK ASN91 | 1.37 | 27.8% | AGGREGATION RISK VAL92 | 2.03 | 46.8% | AGGREGATION RISK LEU95 | 1.87 | 36.5% | AGGREGATION RISK GLU96 | 1.21 | 50.2% | AGGREGATION RISK GLU118 | 1.09 | 47.0% | AGGREGATION RISK GLN128 | 1.33 | 40.1% | AGGREGATION RISK SER129 | 1.31 | 46.0% | AGGREGATION RISK SER132 | 1.40 | 54.8% | AGGREGATION RISK THR133 | 1.77 | 66.9% | AGGREGATION RISK LEU134 | 1.22 | 29.3% | AGGREGATION RISK ----------------------------------------------------------------- Total Aggregation Hotspots: 14
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Analysis of Surface Data:
We calculated the Relative Solvent Accessibility (RSA) for hydrophobic residues. A residue is considered a "Hotspot" if it is hydrophobic AND >25% exposed to the surface.
- Total Hotspots Found: 14 residues.
- Primary Target: Valine 92 (V92). It has a high hydrophobicity score (2.03) and is significantly exposed (46.8%).
Rational Design Strategy: Mutation of Residue 92¶
Identification: Computational surface scanning identified Valine 92 (V92) as the primary driver of aggregation, forming a highly exposed hydrophobic patch (RSA 46.8%, Kyte-Doolittle Score 2.03).
In [108]:
import csv
import re
from collections import Counter
import sys
# --- 1. Load the MSA File (Robust Method) ---
input_file = "boltz_prediction/boltz_results_input/msa/input_0.csv"
sequences = []
try:
with open(input_file, 'r', newline='') as f:
reader = csv.DictReader(f)
for row in reader:
if 'sequence' in row:
sequences.append(row['sequence'])
else:
# Fallback if 'sequence' header is missing, try 2nd column
sequences.append(list(row.values())[1])
if not sequences:
raise ValueError("File is empty or could not find sequences.")
query_sequence = sequences[0] # First sequence is the query
print(f"Successfully loaded {len(sequences)} sequences.")
except Exception as e:
print(f"CRITICAL ERROR: Could not load file. {e}")
# Stop execution if file fails
sys.exit(1)
# --- 2. Define the Target Residues ---
target_residues_text = """
MET23, VAL24, ASN27, ASN89, ASN91, VAL92, LEU95, GLU96,
GLU118, GLN128, SER129, SER132, THR133, LEU134
"""
targets = re.findall(r'([A-Z]{3})(\d+)', target_residues_text)
# --- 3. Helper Function ---
def get_msa_index(query_seq, target_res_num):
current_residue_count = 0
for i, char in enumerate(query_seq):
if char != '-':
current_residue_count += 1
if current_residue_count == int(target_res_num):
return i
return -1
# --- 4. Analyze Distribution ---
print(f"\n{'RESIDUE':<10} | {'MSA IDX':<8} | {'QUERY CHECK':<15} | {'DISTRIBUTION (Top 5)'}")
print("-" * 100)
aa_map = {'CYS': 'C', 'ASP': 'D', 'SER': 'S', 'GLN': 'Q', 'LYS': 'K',
'ILE': 'I', 'PRO': 'P', 'THR': 'T', 'PHE': 'F', 'ASN': 'N',
'GLY': 'G', 'HIS': 'H', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W',
'ALA': 'A', 'VAL': 'V', 'GLU': 'E', 'TYR': 'Y', 'MET': 'M'}
for aa_code, pos_str in targets:
pos = int(pos_str)
msa_index = get_msa_index(query_sequence, pos)
if msa_index == -1:
print(f"{aa_code}{pos:<7} | {'N/A':<8} | OUT OF BOUNDS |")
continue
# Check for Mismatch
expected_aa = aa_map.get(aa_code.upper(), '?')
actual_aa = query_sequence[msa_index]
if actual_aa == expected_aa:
status = "MATCH"
elif actual_aa == '-':
status = "GAP (Mismatch)"
else:
status = f"MISMATCH ({actual_aa})"
# Calculate Distribution
column_residues = [seq[msa_index] for seq in sequences]
total_seqs = len(column_residues)
counts = Counter(column_residues)
# Format the distribution string
dist_list = [f"{k}:{v/total_seqs:.1%}" for k, v in counts.most_common(5)]
dist_str = ", ".join(dist_list)
print(f"{aa_code}{pos:<7} | {msa_index:<8} | {status:<15} | {dist_str}")
Successfully loaded 161 sequences. RESIDUE | MSA IDX | QUERY CHECK | DISTRIBUTION (Top 5) ---------------------------------------------------------------------------------------------------- MET23 | 22 | MATCH | -:30.4%, L:23.6%, M:23.0%, Q:6.8%, R:5.6% VAL24 | 23 | MATCH | -:55.3%, V:17.4%, L:11.8%, K:6.8%, A:5.0% ASN27 | 26 | MATCH | K:24.8%, N:24.2%, R:14.3%, E:12.4%, -:9.9% ASN89 | 88 | MATCH | S:37.9%, N:24.8%, M:9.9%, D:6.2%, I:5.0% ASN91 | 90 | MATCH | I:58.4%, N:34.8%, -:2.5%, V:0.6%, e:0.6% VAL92 | 91 | MATCH | N:39.1%, V:19.3%, I:14.9%, R:13.0%, K:3.7% LEU95 | 94 | MATCH | V:51.6%, L:13.7%, T:9.3%, I:6.8%, Q:6.2% GLU96 | 95 | MATCH | L:37.3%, V:17.4%, E:12.4%, N:6.2%, K:5.6% GLU118 | 117 | MATCH | V:46.0%, E:14.3%, I:13.0%, A:6.2%, -:6.2% GLN128 | 127 | MATCH | C:49.1%, Q:20.5%, F:14.9%, -:8.7%, L:3.1% SER129 | 128 | MATCH | Q:49.1%, C:14.3%, S:11.8%, -:8.7%, R:7.5% SER132 | 131 | MATCH | I:41.6%, S:16.8%, -:13.7%, F:9.3%, L:6.2% THR133 | 132 | MATCH | S:41.6%, T:19.3%, -:14.3%, A:6.2%, I:5.6% LEU134 | 133 | MATCH | T:39.1%, -:18.6%, L:16.8%, S:11.2%, K:4.3%
Selection: Evolutionary analysis (MSA) reveals that Valine is not the conserved residue at this position. The consensus residue is Asparagine (N), appearing in 39.1% of homologs, followed by Valine (19.3%) and Arginine (13.0%).
Decision: We selected the V92N mutation for the final construct.
- Solubility: Replaces a hydrophobic residue with a polar one, disrupting the aggregation patch.
- Stability: Aligns with the evolutionary consensus (39.1% frequency), minimizing the risk of destabilizing the protein fold compared to the rare V92S (1.2%) originally considered.
11. Final Purification Recommendation¶
Based on the computational profiling (pI 7.92) and the engineered tags:
- Primary Capture: Immobilized Metal Affinity Chromatography (IMAC) using a Ni-NTA column.
- Buffer: 50mM Tris-HCl, 300mM NaCl, pH 8.0. (High salt prevents non-specific binding).
- Elution: Imidazole gradient.
- Polishing Step: Given the pI of 7.92, Cation Exchange Chromatography (CEX) is recommended.
- Buffer: MES or Phosphate buffer at pH 6.5.
- Rationale: At pH 6.5, the protein (pI 7.92) will be positively charged and bind to the column, while many host contaminants will flow through.
- Stability: Due to the instability index (52.12), all buffers should include 5-10% Glycerol and be kept at 4°C throughout the process.