"""DNA/质粒设计工具 — 引物设计 + 密码子优化""" import sys import json import os # 确保同目录可 import sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) def op_primer_design(args): """PCR 引物设计：输入序列，返回正/反向引物（Tm、GC、长度、二级结构评估）。 返回字段约定：fwd_position/rev_position 为 0-based 切片索引（与 Python sequence[start:end] 一致，1-based 坐标 = 索引 + 1）；每个候选带 quality 评估（good/ok/poor）与 issues 说明。top_n 控制返回候选数（默认 5）。 """ from Bio.Seq import Seq from Bio.SeqUtils import gc_fraction from Bio.SeqUtils.MeltingTemp import Tm_NN import re sequence = args.get('sequence', '').upper().replace(' ', '') product_size = args.get('product_size', 500) # 期望产物大小 primer_len_range = args.get('primer_len_range', [18, 25]) # 引物长度范围 tm_target = args.get('tm_target', 60) # 目标 Tm top_n = int(args.get('top_n', 5)) # 返回候选数 tm_diff_max = float(args.get('tm_diff_max', 5)) # 正反向 Tm 差过滤阈值 if not sequence or len(sequence) < 20: return {'error': 'sequence required (>=20 nt)'} # 只用 DNA IUPAC valid = set('ACGTRYSWKMBDHVN') if not set(sequence) <= valid: return {'error': f'invalid characters in sequence: {set(sequence) - valid}'} results = [] # 搜索合适长度范围内的引物 for fwd_start in range(0, min(50, len(sequence) - primer_len_range[1])): for fwd_len in range(primer_len_range[0], primer_len_range[1] + 1): fwd_seq = sequence[fwd_start:fwd_start + fwd_len] rev_start = min(fwd_start + product_size, len(sequence)) - fwd_len if rev_start < fwd_start + fwd_len: continue rev_raw = sequence[rev_start:rev_start + fwd_len] rev_seq = str(Seq(rev_raw).reverse_complement()) fwd_tm = Tm_NN(fwd_seq) rev_tm = Tm_NN(rev_seq) fwd_gc = gc_fraction(Seq(fwd_seq)) * 100 rev_gc = gc_fraction(Seq(rev_seq)) * 100 # Tm 偏差评分 tm_diff = abs(fwd_tm - rev_tm) tm_score = max(0, 10 - tm_diff) gc_diff = abs(fwd_gc - rev_gc) gc_score = max(0, 10 - gc_diff) length_score = max(0, 10 - abs(fwd_len - 20)) score = tm_score + gc_score + length_score if tm_diff < tm_diff_max and gc_diff < 10: # 质量评估（软性标注，不改变现有筛选行为） issues = [] if tm_diff > 3: issues.append(f'tm_diff={tm_diff:.1f}C (>3)') if not (40 <= fwd_gc <= 60): issues.append(f'fwd_gc={fwd_gc:.1f}% (out of 40-60)') if not (40 <= rev_gc <= 60): issues.append(f'rev_gc={rev_gc:.1f}% (out of 40-60)') if not issues: quality = 'good' elif tm_diff <= 5 and 35 <= min(fwd_gc, rev_gc) and max(fwd_gc, rev_gc) <= 65: quality = 'ok' else: quality = 'poor' results.append({ 'forward': fwd_seq, 'reverse': rev_seq, 'fwd_tm': round(fwd_tm, 1), 'rev_tm': round(rev_tm, 1), 'fwd_gc': round(fwd_gc, 1), 'rev_gc': round(rev_gc, 1), 'length': fwd_len, 'product_size': rev_start - fwd_start + fwd_len, 'fwd_position': fwd_start, 'rev_position': rev_start, 'score': round(score, 2), 'quality': quality, 'issues': issues, }) # 按 score 排序，取 top_n results.sort(key=lambda x: -x['score']) top = results[:top_n] # 建议（面向 agent 决策） advice = None if not top: advice = ('未找到满足约束的引物对。建议：调大 tm_diff_max（正反向 Tm 差容忍）、' '调整 product_size（产物区域）、放宽 primer_len_range，' '或改用 bio_primer3_design（Primer3 热力学评分）。') elif top[0]['quality'] != 'good': q = top[0]['quality'] adv_issues = '；'.join(top[0]['issues']) advice = (f'最佳候选 quality={q}：{adv_issues}。' '如对质量要求高，可调整 tm_target/product_size 或改用 bio_primer3_design；' '若需在 5\' 端添加酶切位点/同源臂，最终 Tm 会变化，需重新核算。') return { 'input_length': len(sequence), 'product_size_target': product_size, 'tm_target': tm_target, 'n_candidates': len(results), 'top_primers': top, 'quality_scale': 'good=tm_diff<=3 且正反向 GC 均在 40-60；ok=tm_diff<=5 且 GC 35-65；poor=超阈值', 'position_base': '0-based (Python slice index; 1-based = index + 1)', 'advice': advice, } def op_seq_optimize(args): """密码子优化：将编码序列按目标宿主的密码子使用频率优化。""" from Bio.Seq import Seq from Bio.Data.CodonTable import CodonTable sequence = args.get('sequence', '').upper().replace(' ', '') organism = args.get('organism', 'ecoli') # ecoli | human | yeast remove_rare = args.get('remove_rare', True) gc_target = args.get('gc_target', None) # 目标 GC%（可选） if not sequence or len(sequence) < 3: return {'error': 'coding sequence required (>=3 nt)'} # 简化的密码子使用表（每种生物的最优密码子） codon_tables = { 'ecoli': { 'A': 'GCC', 'R': 'CGT', 'N': 'AAC', 'D': 'GAT', 'C': 'TGC', 'E': 'GAA', 'Q': 'CAG', 'G': 'GGC', 'H': 'CAT', 'I': 'ATC', 'L': 'CTG', 'K': 'AAA', 'M': 'ATG', 'F': 'TTT', 'P': 'CCG', 'S': 'AGC', 'T': 'ACC', 'W': 'TGG', 'Y': 'TAT', 'V': 'GTG', }, 'human': { 'A': 'GCC', 'R': 'CGC', 'N': 'AAC', 'D': 'GAC', 'C': 'TGC', 'E': 'GAG', 'Q': 'CAG', 'G': 'GGC', 'H': 'CAC', 'I': 'ATC', 'L': 'CTG', 'K': 'AAG', 'M': 'ATG', 'F': 'TTC', 'P': 'CCC', 'S': 'AGC', 'T': 'ACC', 'W': 'TGG', 'Y': 'TAC', 'V': 'GTG', }, 'yeast': { 'A': 'GCT', 'R': 'AGA', 'N': 'AAT', 'D': 'GAT', 'C': 'TGT', 'E': 'GAA', 'Q': 'CAA', 'G': 'GGT', 'H': 'CAT', 'I': 'ATT', 'L': 'TTG', 'K': 'AAG', 'M': 'ATG', 'F': 'TTT', 'P': 'CCA', 'S': 'TCT', 'T': 'ACT', 'W': 'TGG', 'Y': 'TAT', 'V': 'GTT', }, } table = codon_tables.get(organism) if not table: return {'error': f'unknown organism: {organism}', 'supported': list(codon_tables.keys())} # 翻译原序列 protein = str(Seq(sequence).translate(to_stop=False)) # 优化密码子 optimized = [] changes = 0 for aa in protein: if aa == '*': optimized.append('TAA') elif aa in table: old_codon = None # 找原始密码子（简化：取原序列对应位置） codon_idx = len(optimized) * 3 if codon_idx + 3 <= len(sequence): old_codon = sequence[codon_idx:codon_idx + 3] new_codon = table[aa] if old_codon and old_codon != new_codon: changes += 1 optimized.append(new_codon) else: optimized.append('NNN') # 未知氨基酸 optimized_seq = ''.join(optimized) gc_content = sum(1 for c in optimized_seq if c in 'GC') / len(optimized_seq) * 100 return { 'organism': organism, 'original_length': len(sequence), 'optimized_length': len(optimized_seq), 'optimized_sequence': optimized_seq, 'codon_changes': changes, 'change_rate': round(changes / (len(optimized) or 1) * 100, 1), 'gc_percent': round(gc_content, 1), 'protein_length': len(protein), 'note': '密码子优化基于简化表；生产环境建议使用 CAI/ECAI 精细优化。', }