请不使用Biopython

如果你不想使用 Biopython，我们也可以手写一个简单的算法来计算蛋白质序列的局部对齐得分，比如 Smith-Waterman 算法。这个算法会考虑每个位置的匹配、插入和删除操作，以及BLOSUM62矩阵的值。

首先，你需要手动创建BLOSUM62矩阵，然后实现核心的Smith-Waterman函数。这里是一个基本的框架示例：

# 假设你有一个预先加载好的BLOSUM62矩阵，或者从网上下载数据自行构建
BLOSUM62 = ...  # 你需要填充具体的矩阵数据

# 定义函数计算得分
def match_score(a, b, matrix=BLOSUM62):
    if a == b:
        return matrix[a, b]
    else:
        return 0

def insertion_score(gap_penalty):
    return gap_penalty

def deletion_score(gap_penalty):
    return gap_penalty

def smith_waterman(seq1, seq2, gap_penalty=-11, extend_gap_penalty=-1):
    m, n = len(seq1) + 1, len(seq2) + 1
    dp = [[0] * n for _ in range(m)]
    traceback = [[''] * n for _ in range(m)]

    for i in range(1, m):
        dp[i][0] = i * deletion_score(gap_penalty)
        traceback[i][0] = 'D'

    for j in range(1, n):
        dp[0][j] = j * insertion_score(gap_penalty)
        traceback[0][j] = 'I'

    for i in range(1, m):
        for j in range(1, n):
            match = dp[i-1][j-1] + match_score(seq1[i-1], seq2[j-1])
            delete = dp[i][j-1] + deletion_score(gap_penalty)
            insert = dp[i-1][j] + insertion_score(gap_penalty)

            max_score = max(match, delete, insert)
            best_move = 'M' if match == max_score else ['D', 'I'][match < max_score]

            dp[i][j] = max_score
            traceback[i][j] = best_move

    alignment_score = dp[-1][-1]
    aligned_seq1, aligned_seq2 = '', ''

    i, j = m - 1, n - 1
    while i > 0 and j > 0:
        if traceback[i][j] == 'M':
            aligned_seq1 += seq1[i-1]
            aligned_seq2 += seq2[j-1]
            i -= 1
            j -= 1
        elif traceback[i][j] == 'D':
            aligned_seq1 += '-'
            i -= 1
        else:
            aligned_seq2 += '-'
            j -= 1

    return aligned_seq1[::-1], aligned_seq2[::-1], alignment_score

# 示例用法
seq1 = "YOUR_PROTEIN_SEQ_1"
seq2 = "YOUR_PROTEIN_SEQ_2"

alignment, score = smith_waterman(seq1, seq2)
print(f"Alignment: {alignment}")
print(f"Score: {score}")