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@@ -288,5 +288,7 @@ RNA可以用Amber的Parmbsc1的力场，是amber中最新的适合模拟核酸
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单纯的常规MD就用水中溶菌酶那个教程就行。力场用我上面提到的就行。
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不过你模拟两个碱基的RNA有什么意义吗？是要模拟碱基配对过程（比如不同碱基之间的配对，这个貌似有人研究过了）吗还是怎么样。
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你可以看下这篇文章，今晚才看见的，
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Assessment of AMBER Force Fields for Simulations of ssDNA（DOI: 10.1021/acs.jctc.0c0093）
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你可以看下这篇文章
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Assessment of AMBER Force Fields for Simulations of ssDNA（DOI: 10.1021/acs.jctc.0c0093）
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1
README_QMMM.md
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README_QMMM.md
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# [ASH - A QM/MM packages](https://mp.weixin.qq.com/s/jjVe81BwBjUWeNSEGymsAw)
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73
bioinfo/MHH.py
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bioinfo/MHH.py
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import numpy as np
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from hmmlearn import hmm
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# 定义状态和观察集合
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# 状态集合代表不同的基因型: MM, MZ, ZZ
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states = ["MM", "MZ", "ZZ"]
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n_states = len(states)
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# 观测集合为 0 和 1，其中 0 表示 ZZ，1 表示 MM
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observations = [0, 1]
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n_observations = len(observations)
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# 初始概率分布 (initial state probabilities)
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# 定义每个状态的初始概率: MM: 0.4, MZ: 0.2, ZZ: 0.4
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start_probability = np.array([0.4, 0.2, 0.4])
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# 转移概率矩阵 (state transition matrix)
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# 每个状态到其他状态的转移概率，基于重组率，假设重组率为 0.01
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# 例如，从 MM 状态保持不变的概率为 0.99，转移到 MZ 或 ZZ 的概率为 0.005
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transition_probability = np.array([
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[0.99, 0.005, 0.005], # MM -> [MM, MZ, ZZ]
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[0.005, 0.99, 0.005], # MZ -> [MM, MZ, ZZ]
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[0.005, 0.005, 0.99] # ZZ -> [MM, MZ, ZZ]
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])
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# 输出概率矩阵 (emission probability matrix)
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# 定义每个隐藏状态 (MM, MZ, ZZ) 生成观测值 (0, 1) 的概率
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# 基于测序错误率 3%
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# 例如，MM 状态下观测到 1 (MM) 的概率为 0.97，观测到 0 (ZZ) 的概率为 0.03
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emission_probability = np.array([
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[0.97, 0.03], # MM -> [观测到 0 (ZZ), 观测到 1 (MM)]
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[0.5, 0.5], # MZ -> [观测到 0 (ZZ), 观测到 1 (MM)]
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[0.03, 0.97] # ZZ -> [观测到 0 (ZZ), 观测到 1 (MM)]
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])
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# 建立 HMM 模型
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# 使用 hmmlearn 库的 CategoricalHMM 来表示隐马尔可夫模型
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# CategoricalHMM 用于离散观测值的建模，与之前的 MultinomialHMM 有所不同
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# MultinomialHMM 新版本中进行了重大变动，要求明确设置 n_trials，且现在遵循标准的多项式分布定义
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model = hmm.CategoricalHMM(n_components=n_states, n_iter=100, tol=0.01)
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model.startprob_ = start_probability # 设置初始状态概率
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model.transmat_ = transition_probability # 设置状态转移概率矩阵
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model.emissionprob_ = emission_probability # 设置输出概率矩阵
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# 观测序列（给定的 109 个位点的测序结果）
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# 将观测结果调整为二维数组，其中每一行为一个观测值
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observation_sequence = np.array([[1], [1], [1], [1], [1], [1], [1], [0], [1], [1], [1], [1], [1], [1], [0], [0], [0], [0], [0], [0],
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[1], [0], [0], [0], [0], [0], [0], [0], [1], [0], [1], [0], [1], [0], [1], [1], [1], [1], [1], [1],
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[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [0], [1], [0], [1], [0], [1],
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[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1],
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[1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1],
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[1], [1], [1], [1], [1], [1], [1], [1], [1]]).reshape(-1, 1) # 调整为二维数组
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# 使用维特比算法解码最可能的状态序列
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# 使用维特比算法计算最可能的隐藏状态序列（即最可能的基因型序列）
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logprob, hidden_states = model.decode(observation_sequence, algorithm="viterbi")
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# 输出结果
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# 打印最可能的基因型序列
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print("最可能的基因型序列:")
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results = ",".join([states[state] for state in hidden_states])
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print(" ".join([states[state] for state in hidden_states]))
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new_list= []
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for i in [states[state] for state in hidden_states]:
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if i == 'ZZ':
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new_list.append('0')
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elif i == 'MM':
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new_list.append('1')
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elif i == 'MZ':
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new_list.append('2')
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else:
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pass
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print("".join(new_list))
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54
bioinfo/sum.py
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bioinfo/sum.py
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import numpy as np
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from jaxtyping import Float, Array
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def needleman_wunsch(seq1: str, seq2: str,
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match_score: int = 1,
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mismatch_penalty: int = -1,
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gap_penalty: int = -2) -> tuple[str, str, Float[Array, "len_seq1+1 len_seq2+1"]]:
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# 初始化矩阵
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len_seq1, len_seq2 = len(seq1), len(seq2)
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score_matrix: Float[Array, "len_seq1+1 len_seq2+1"] = np.zeros((len_seq1 + 1, len_seq2 + 1))
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# 初始化第一行和第一列
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for i in range(len_seq1 + 1):
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score_matrix[i][0] = i * gap_penalty
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for j in range(len_seq2 + 1):
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score_matrix[0][j] = j * gap_penalty
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# 填充得分矩阵
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for i in range(1, len_seq1 + 1):
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for j in range(1, len_seq2 + 1):
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match = score_matrix[i - 1][j - 1] + (match_score if seq1[i - 1] == seq2[j - 1] else mismatch_penalty)
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delete = score_matrix[i - 1][j] + gap_penalty
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insert = score_matrix[i][j - 1] + gap_penalty
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score_matrix[i][j] = max(match, delete, insert)
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# 回溯得到比对结果
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align1, align2 = '', ''
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i, j = len_seq1, len_seq2
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while i > 0 or j > 0:
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current_score = score_matrix[i][j]
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if i > 0 and j > 0 and (current_score == score_matrix[i - 1][j - 1] + (match_score if seq1[i - 1] == seq2[j - 1] else mismatch_penalty)):
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align1 = seq1[i - 1] + align1
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align2 = seq2[j - 1] + align2
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i -= 1
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j -= 1
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elif i > 0 and (current_score == score_matrix[i - 1][j] + gap_penalty):
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align1 = seq1[i - 1] + align1
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align2 = '-' + align2
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i -= 1
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else:
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align1 = '-' + align1
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align2 = seq2[j - 1] + align2
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j -= 1
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return align1, align2, score_matrix
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# 测试示例
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seq1 = "GATTACA"
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seq2 = "GCATGCU"
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alignment = needleman_wunsch(seq1, seq2)
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print("Aligned Sequences:")
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print(alignment[0])
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print(alignment[1])
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print("Alignment Score:", alignment[2])
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