AlphaFold3 msa_pairing模块的pair_sequences函数的核心目标是基于 MSA（多序列比对）中的物种信息，在多条链之间建立 MSA 配对索引，从而帮助 AlphaFold3 捕捉共进化信息，提升蛋白复合物预测的准确性。函数pair_sequences 通过调用 _make_msa_df、 _create_species_dict 以及_match_rows_by_sequence_similarity实现其功能。
源代码：
def pair_sequences(examples: List[Mapping[str, np.ndarray]],
) -> Dict[int, np.ndarray]:"""Returns indices for paired MSA sequences across chains."""num_examples = len(examples)all_chain_species_dict = []common_species = set()for chain_features in examples:msa_df = _make_msa_df(chain_features)species_dict = _create_species_dict(msa_df)all_chain_species_dict.append(species_dict)common_species.update(set(species_dict))common_species = sorted(common_species)common_species.remove(b'') # Remove target sequence species.all_paired_msa_rows = [np.zeros(len(examples), int)]all_paired_msa_rows_dict = {k: [] for k in range(num_examples)}all_paired_msa_rows_dict[num_examples] = [np.zeros(len(examples), int)]for species in common_species:if not species:continuethis_species_msa_dfs = []species_dfs_present = 0for species_dict in all_chain_species_dict:if species in species_dict:this_species_msa_dfs.append(species_dict[species])species_dfs_present += 1else:this_species_msa_dfs.append(None)# Skip species that are present in only one chain.if species_dfs_present <= 1:continueif np.any(np.array([len(species_df) for species_df inthis_species_msa_dfs ifisinstance(species_df, pd.DataFrame)]) > 600):continuepaired_msa_rows = _match_rows_by_sequence_similarity(this_species_msa_dfs)all_paired_msa_rows.extend(paired_msa_rows)all_paired_msa_rows_dict[species_dfs_present].extend(paired_msa_rows)all_paired_msa_rows_dict = {num_examples: np.array(paired_msa_rows) fornum_examples, paired_msa_rows in all_paired_msa_rows_dict.items()}return all_paired_msa_rows_dict代码解读：
函数输入
def pair_sequences(examples: List[Mapping[str, np.ndarray]]) -> Dict[int, np.ndarray]
examples：包含多条链的 MSA 信息，每个元素是一个字典，代表一条蛋白链的 MSA 相关特征。
- 例如，
examples[0]可能对应链 A 的 MSA 特征，examples[1]可能对应链 B 的 MSA 特征。
代码执行过程
1️⃣ 提取所有链的 MSA 并构建物种索引
num_examples = len(examples)all_chain_species_dict = []
common_species = set()
for chain_features in examples: