UndefVarError(:要构造的变量名)错误

各位大佬好:

不重要的背景:
我希望利用Julia的BioStructure包来实现对已经叠合好的结构中模板(template)与诱饵(decoy)结构中匹配序列的抓取,目前的实现思路是根据CA原子的距离来确定模板中与诱饵相匹配的元素,并进一步获取元素的序列;
在此过程中,需要考虑到匹配到的元素与诱饵结构得残基数不等的情况,因此,我希望通过以下代码来构造几个不同的“起始点和终止点”进行rmsd的计算,选取最接近的一段序列;

出问题的代码
在脚本中,我预先获得了template_Ntemplate_C这两个变量,随后利用这两个变量和预先获取的诱饵结构长度变量生成了一组用于定义序列区间的变量,随后我希望在循环比较rmsd的过程中使用这些变量,于是使用了new_var = eval(Symbol(“template_N_”*"$N"))的方法,结果报错UndefVarError(:new_var):
下面是出问题的区域:

    template_N_1 = template_N
    template_N_2 = template_C - decoy_motif_length + 1
    out_seq_N = template_N
    best_rmsd_val = 1000
    for N = 1:2
        template_N = eval(Symbol("template_N_"*"$N"))

目前可以确定问题出在 template_N = eval(Symbol(“template_N_”*"$N"))这一句,请问各位能否帮忙赶一下问题可能出在哪里?感激不尽~

下面是整个脚本的完整代码:

#!/bin/julia
using BioStructures

function get_decoy_motif(decoy_structure,decoy_motif_region,decoy_chain_id,template,out_fasta,motif_N,motif_C,error)
    try
        template_structure= read(template, PDB)
        res_id = 0
        template_N = 0
        template_C = 0
        distance_N = 0
        distance_C = 0
        for model in template_structure
            for chain in model
                for res in chain
                    res_id == 0 && (template_N = resnumber(res))
                    res_id == 0 && (distance_N = distance(decoy_structure[decoy_chain_id][motif_N], res, calphaselector))
                    res_id == 0 && (template_C = resnumber(res))
                    res_id == 0 && (distance_C = distance(decoy_structure[decoy_chain_id][motif_C], res, calphaselector))
                    res_id+=1
                    if distance(decoy_structure[decoy_chain_id][motif_N], res) <= distance_N 
                        template_N = resnumber(res)
                        distance_N = distance(decoy_structure[decoy_chain_id][motif_N], res) 
                    end
                    if distance(decoy_structure[decoy_chain_id][motif_C], res) <= distance_C
                        template_C = resnumber(res)
                        distance_C = distance(decoy_structure[decoy_chain_id][motif_C], res)
                        template_chain_id = chainid(chain) 
                    end
                end
            end
        end
        decoy_motif_length = motif_C - motif_N + 1
        template_N_1 = template_N
        template_N_2 = template_C - decoy_motif_length + 1
        template_N_3 = template_N + 1
        template_N_4 = template_N - 1
        template_C_1 = template_C
        template_C_2 = template_N + decoy_motif_length - 1
        template_C_3 = template_C + 1
        template_C_4 = template_C - 1
        out_seq_N = template_N
        out_seq_C = template_C
        best_rmsd_val = 1000
        for N = 1:4
            template_N = eval(Symbol("template_N_"*"$N"))
            for C = 1:4
                template_C = eval(Symbol("template_C_"*"$C"))
                template_motif_length = template_C - template_N + 1
                if template_motif_length == decoy_motif_length
                    motif_region = collectresidues(template_structure[template_chain_id], res -> template_N <= resnumber(res) <= template_C )
                    rmsd_val = rmsd(motif_region, decoy_motif_region, superimpose=false)
                    if rmsd_val <= best_rmsd_val
                        best_rmsd_val = rmsd_val
                        out_seq_N = template_N
                        out_seq_C = template_C
                    end
                end
            end
        end
        motif_region = collectresidues(template_structure, res -> out_seq_N <= resnumber(res) <= out_seq_C )
        fasta_seq = LongAminoAcidSeq(motif_region)
        write(out_fasta,">$template:$fasta_seq \n")
    catch e1
        write(error,"Error: $template.\n")
        write(error,"$e1\n\n")
    end
end

function main()
    decoy_pdb="decoy.pdb"
    template_library="Template_lib"
    out_fasta="Candidates.fasta"
    decoy_structure = read(decoy_pdb, PDB)
    io = open(out_fasta, "w+")
    error = open("ex_seq.error", "w+")
    res_id = 0
    motif_N = 0
    motif_C = 0
    decoy_chain_id = 0
    for model in decoy_structure
        for chain in model
            decoy_chain_id = chainid(chain)
            for res in chain
                res_id == 0 && (motif_N = resnumber(res))
                res_id == 0 && (motif_C = resnumber(res))
                res_id+=1
                resnumber(res) < motif_N && (motif_N = resnumber(res))
                resnumber(res) > motif_C && (motif_C = resnumber(res))
            end
        end
    end
    decoy_motif_region = collectresidues(decoy_structure, res -> motif_N <= resnumber(res) <= motif_C)
    Threads.@threads for template in readdir(template_library)
        get_decoy_motif(decoy_structure,decoy_motif_region,decoy_chain_id,"$template_library/$template",io,motif_N,motif_C,error)
    end
end

main()
  eval(expr)

  Evaluate an expression in the global scope of the containing module. Every
  Module (except those defined with baremodule) has its own 1-argument
  definition of eval, which evaluates expressions in that module.

你的变量是在函数里定义的,在全局没有定义。建议变量名改成数组会比较好。
如果要在原变量基础上修改,可以这样

using Base.Cartesian
for template_N in @ntuple 4 template_N
1赞

感谢 @nebula 大佬,我原来以为用数组会很复杂(小白本白了),没想到可以直接把这些不同计算方式产生的变量直接写入一个数组,然后用for循环来迭代,这样的话就简单多了:

尝试了一下改用数组,果然ok了,下面是更改之后的写法:

        N_candidates = [ template_N, template_C - decoy_motif_length + 1, template_N + 1, template_N - 1 ]
        C_candidates = [ template_C, template_N + decoy_motif_length - 1, template_C + 1, template_C - 1 ]
        out_seq_N = template_N
        out_seq_C = template_C
        best_rmsd_val = 1000
        for template_N in N_candidates
            for template_C in C_candidates
                template_motif_length = template_C - template_N + 1
                if template_motif_length == decoy_motif_length

再次感谢大佬~

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