glycoengineering

name glycoengineering description Analyze and engineer protein glycosylation. Scan sequences for N-glycosylation sequons (N-X-S/T), predict O-glycosylation hotspots, and access curated glycoengineering tools (NetOGlyc, GlycoShield, GlycoWorkbench). For glycoprotein engineering, therapeutic antibody optimization, and vaccine design. license Unknown metadata {"version":"1.1","skill-author":"Kuan-lin Huang"} Glycoengineering Overview Glycosylation is the most common and complex post-translational modification (PTM) of proteins, affecting over 50% of all human proteins. Glycans regulate protein folding, stability, immune recognition, receptor interactions, and pharmacokinetics of therapeutic proteins. Glycoengineering involves rational modification of glycosylation patterns for improved therapeutic efficacy, stability, or immune evasion. Two major glycosylation types: N-glycosylation : Attached to asparagine (N) in the sequon N-X-[S/T] where X ≠ Proline; occurs in the ER/Golgi O-glycosylation : Attached to serine (S) or threonine (T); no strict consensus motif; primarily GalNAc initiation When to Use This Skill Use this skill when: Antibody engineering : Optimize Fc glycosylation for enhanced ADCC, CDC, or reduced immunogenicity Therapeutic protein design : Identify glycosylation sites that affect half-life, stability, or immunogenicity Vaccine antigen design : Engineer glycan shields to focus immune responses on conserved epitopes Biosimilar characterization : Compare glycan patterns between reference and biosimilar Drug target analysis : Does glycosylation affect target engagement for a receptor? Protein stability : N-glycans often stabilize proteins; identify sites for stabilizing mutations N-Glycosylation Sequon Analysis Scanning for N-Glycosylation Sites N-glycosylation occurs at the sequon N-X-[S/T] where X ≠ Proline. import re from typing import List , Tuple def find_n_glycosylation_sequons ( sequence: str ) -> List [ dict ]: """ Scan a protein sequence for canonical N-linked glycosylation sequons. Motif: N-X-[S/T], where X ≠ Proline. Args: sequence: Single-letter amino acid sequence Returns: List of dicts with position (1-based), motif, and context """ seq = sequence.upper() results = [] i = 0 while i <= len (seq) - 3 : triplet = seq[i:i+ 3 ] if triplet[ 0 ] == 'N' and triplet[ 1 ] != 'P' and triplet[ 2 ] in { 'S' , 'T' }: context = seq[ max ( 0 , i- 3 ):i+ 6 ] # ±3 residue context results.append({ 'position' : i + 1 , # 1-based 'motif' : triplet, 'context' : context, 'sequon_type' : 'NXS' if triplet[ 2 ] == 'S' else 'NXT' }) i += 3 else : i += 1 return results def summarize_glycosylation_sites ( sequence: str , protein_name: str = "" ) -> str : """Generate a research log summary of N-glycosylation sites.""" sequons = find_n_glycosylation_sequons(sequence) lines = [ f"# N-Glycosylation Sequon Analysis: {protein_name or 'Protein' } " ] lines.append( f"Sequence length: { len (sequence)} " ) lines.append( f"Total N-glycosylation sequons: { len (sequons)} " ) if sequons: lines.append( f"\nN-X-S sites: { sum ( 1 for s in sequons if s[ 'sequon_type' ] == 'NXS' )} " ) lines.append( f"N-X-T sites: { sum ( 1 for s in sequons if s[ 'sequon_type' ] == 'NXT' )} " ) lines.append( f"\nSite details:" ) for s in sequons: lines.append( f" Position {s[ 'position' ]} : {s[ 'motif' ]} (context: ... {s[ 'context' ]} ...)" ) else : lines.append( "No canonical N-glycosylation sequons detected." ) return "\n" .join(lines) # Example: IgG1 Fc region fc_sequence = "APELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEVHNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSREEMTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVMHEALHNHYTQKSLSLSPGK" print (summarize_glycosylation_sites(fc_sequence, "IgG1 Fc" )) Mutating N-Glycosylation Sites def eliminate_glycosite ( sequence: str , position: int , replacement: str = "Q" ) -> str : """ Eliminate an N-glycosylation site by substituting Asn → Gln (conservative). Args: sequence: Protein sequence position: 1-based position of the Asn to mutate replacement: Amino acid to substitute (default Q = Gln; similar size, not glycosylated) Returns: Mutated sequence """ seq = list (sequence.upper()) idx = position - 1 assert seq[idx] == 'N' , f"Position {position} is ' {seq[idx]} ', not 'N'" seq[idx] = replacement.upper() return '' .join(seq) def add_glycosite ( sequence: str , position: int , flanking_context: str = "S" ) -> str : """ Introduce an N-glycosylation site by mutating a residue to Asn, and ensuring X ≠ Pro and +2 = S/T. Args: position: 1-based position to introduce Asn flanking_context: 'S' or 'T' at position+2 (if modification needed) """ seq = list (sequence.upper()) idx = position - 1 # Mutate to Asn seq[idx] = 'N' # Ensure X+1 != Pro (mutate to Ala if needed) if idx + 1 < len (seq) and seq[idx + 1 ] == 'P' : seq[idx + 1 ] = 'A' # Ensure X+2 = S or T if idx + 2 < len (seq) and seq[idx + 2 ] not in ( 'S' , 'T' ): seq[idx + 2 ] = flanking_context return '' .join(seq) O-Glycosylation Analysis Heuristic O-Glycosylation Hotspot Prediction def predict_o_glycosylation_hotspots ( sequence: str , window: int = 7 , min_st_fraction: float = 0.4 , disallow_proline_next: bool = True ) -> List [ dict ]: """ Heuristic O-glycosylation hotspot scoring based on local S/T density. Not a substitute for NetOGlyc; use as fast baseline. Rules: - O-GalNAc glycosylation clusters on Ser/Thr-rich segments - Flag Ser/Thr residues in windows enriched for S/T - Avoid S/T immediately followed by Pro (TP/SP motifs inhibit GalNAc-T) Args: window: Odd window size for local S/T density min_st_fraction: Minimum fraction of S/T in window to flag site """ if window % 2 == 0 : window = 7 seq = sequence.upper() half = window // 2 candidates = [] for i, aa in enumerate (seq): if aa not in ( 'S' , 'T' ): continue if disallow_proline_next and i + 1 < len (seq) and seq[i+ 1 ] == 'P' : continue start = max ( 0 , i - half) end = min ( len (seq), i + half + 1 ) segment = seq[start:end] st_count = sum ( 1 for c in segment if c in ( 'S' , 'T' )) frac = st_count / len (segment) if frac >= min_st_fraction: candidates.append({ 'position' : i + 1 , 'residue' : aa, 'st_fraction' : round (frac, 3 ), 'window' : f" {start+ 1 } - {end} " , 'segment' : segment }) return candidates External Glycoengineering Tools 1. NetOGlyc 4.0 (O-glycosylation prediction) Web service for high-accuracy O-GalNAc site prediction: URL : https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/ Input : FASTA protein sequence Output : Per-residue O-glycosylation probability scores Method : Neural network trained on experimentally verified O-GalNAc sites import requests def submit_netoglycv4 ( fasta_sequence: str ) -> str : """ Submit sequence to NetOGlyc 4.0 web service. Returns the job URL for result retrieval. Note: This uses the DTU Health Tech web service. Results take ~1-5 min. """ url = "https://services.healthtech.dtu.dk/cgi-bin/webface2.cgi" # NetOGlyc submission (parameters may vary with web service version) # Recommend using the web interface directly for most use cases print ( "Submit sequence at: https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/" ) return url # Also: NetNGlyc for N-glycosylation prediction # URL: https://services.healthtech.dtu.dk/services/NetNGlyc-1.0/ 2. GlycoShield-MD (Glycan Shielding Analysis) GlycoShield-MD analyzes how glycans shield protein surfaces during MD simulations: URL : https://gitlab.mpcdf.mpg.de/dioscuri-biophysics/glycoshield-md/ Use : Map glycan shielding on protein surface over MD trajectory Output : Per-residue shielding fraction, visualization # Installation uv pip install glycoshield # Basic usage: analyze glycan shielding from glycosylated protein MD trajectory glycoshield \ --topology glycoprotein.pdb \ --trajectory glycoprotein.xtc \ --glycan_resnames BGLCNA FUC \ --output shielding_analysis/ 3. GlycoWorkbench (Glycan Structure Drawing/Analysis) URL : https://www.eurocarbdb.org/project/glycoworkbench Use : Draw glycan structures, calculate masses, annotate MS spectra Format : GlycoCT, IUPAC condensed glycan notation 4. GlyConnect (Glycan-Protein Database) URL : https://glyconnect.expasy.org/ Use : Find experimentally verified glycoproteins and glycosylation sites Query : By protein (UniProt ID), glycan structure, or tissue import requests def query_glyconnect ( uniprot_id: str ) -> dict : """Query GlyConnect for glycosylation data for a protein.""" url = f"https://glyconnect.expasy.org/api/proteins/uniprot/ {uniprot_id} " response = requests.get(url, headers={ "Accept" : "application/json" }) if response.status_code == 200 : return response.json() return {} # Example: query EGFR glycosylation egfr_glyco = query_glyconnect( "P00533" ) 5. UniCarbKB (Glycan Structure Database) URL : https://unicarbkb.org/ Use : Browse glycan structures, search by mass or composition Format : GlycoCT or IUPAC notation Key Glycoengineering Strategies For Therapeutic Antibodies Goal Strategy Notes Enhance ADCC Defucosylation at Fc Asn297 Afucosylated IgG1 has ~50× better FcγRIIIa binding Reduce immunogenicity Remove non-human glycans Eliminate α-Gal, NGNA epitopes Improve PK half-life Sialylation Sialylated glycans extend half-life Reduce inflammation Hypersialylation IVIG anti-inflammatory mechanism Create glycan shield Add N-glycosites to surface Masks vulnerable epitopes (vaccine design) Common Mutations Used Mutation Effect N297A/Q (IgG1) Removes Fc glycosylation (aglycosyl) N297D (IgG1) Removes Fc glycosylation S298A/E333A/K334A Increases FcγRIIIa binding F243L (IgG1) Increases defucosylation T299A Removes Fc glycosylation Glycan Notation IUPAC Condensed Notation (Monosaccharide abbreviations) Symbol Full Name Type Glc Glucose Hexose GlcNAc N-Acetylglucosamine HexNAc Man Mannose Hexose Gal Galactose Hexose Fuc Fucose Deoxyhexose Neu5Ac N-Acetylneuraminic acid (Sialic acid) Sialic acid GalNAc N-Acetylgalactosamine HexNAc Complex N-Glycan Structure Typical complex biantennary N-glycan: Neu5Ac-Gal-GlcNAc-Man\ Man-GlcNAc-GlcNAc-[Asn] Neu5Ac-Gal-GlcNAc-Man/ (±Core Fuc at innermost GlcNAc) Best Practices Start with NetNGlyc/NetOGlyc for computational prediction before experimental validation Verify with mass spectrometry : Glycoproteomics (Byonic, Mascot) for site-specific glycan profiling Consider site context : Not all predicted sequons are actually glycosylated (accessibility, cell type, protein conformation) For antibodies : Fc N297 glycan is critical — always characterize this site first Use GlyConnect to check if your protein of interest has experimentally verified glycosylation data Additional Resources GlyTouCan (glycan structure repository): https://glytoucan.org/ GlyConnect : https://glyconnect.expasy.org/ CFG Functional Glycomics : http://www.functionalglycomics.org/ DTU Health Tech servers (NetNGlyc, NetOGlyc): https://services.healthtech.dtu.dk/ GlycoWorkbench : https://glycoworkbench.software.informer.com/ Review : Apweiler R et al. (1999) Biochim Biophys Acta. PMID: 10564035 Therapeutic glycoengineering review : Jefferis R (2009) Nature Reviews Drug Discovery. PMID: 19448661