AlphaFold3 data_pipeline 模块DataPipeline类的 process_multiseq_fasta 方法用于处理多序列 FASTA 文件，生成 AlphaFold3 结构预测所需的特征，适用于多链复合物的预测。它结合了 Minkyung Baek 在 Twitter 上提出的“AlphaFold-Gap”策略，即通过在多链 MSA 中插入固定长度的 gap 以模拟多链复合物。

源代码：

    def process_multiseq_fasta(self,
                               fasta_path: str,
                               super_alignment_dir: str,
                               ri_gap: int = 200,
                               ) -> FeatureDict:
        """
            Assembles features for a multi-sequence FASTA. Uses Minkyung Baek's
            hack from Twitter (a.k.a. AlphaFold-Gap).
        """
        with open(fasta_path, 'r') as f:
            fasta_str = f.read()

        input_seqs, input_descs = parsers.parse_fasta(fasta_str)

        # No whitespace allowed
        input_descs = [i.split()[0] for i in input_descs]

        # Stitch all of the sequences together
        input_sequence = ''.join(input_seqs)
        input_description = '-'.join(input_descs)
        num_res = len(input_sequence)

        sequence_features = make_sequence_features(
            sequence=input_sequence,
            description=input_description,
            num_res=num_res,
        )

        seq_lens = [len(s) for s in input_seqs]
        total_offset = 0
        for sl in seq_lens:
            total_offset += sl
            sequence_features["residue_index"][total_offset:] += ri_gap

        msa_list = []
        deletion_mat_list = []
        for seq, desc in zip(input_seqs, input_descs):
            alignment_dir = os.path.join(
                super_alignment_dir, desc
            )
            msas = self._get_msas(
                alignment_dir, seq, None
            )
            msa_list.append([m.sequences for m in msas])
            deletion_mat_list.append([m.deletion_matrix for m in msas])

        final_msa = []
        final_deletion_mat = []
        final_msa_obj = []
        msa_it = enumerate(zip(msa_list, deletion_mat_list))
        for i, (msas, deletion_mats) in msa_it:
            prec, post = sum(seq_lens[:i]), sum(seq_lens[i + 1:])
            msas = [
                [prec * '-' + seq + post * '-' for seq in msa] for msa in msas
            ]
            deletion_mats = [
                [prec * [0] +