通过Hopfield模式多重性进行蛋白质生成
Conditioning Protein Generation via Hopfield Pattern Multiplicity
AI总结：
利用随机注意力生成蛋白质序列，可从小比对中生成合理成员，但无法定向生成特定功能子集。通过添加偏置参数调整注意力logits，可连续改变生成方向，无需重新训练或改变模型结构。
摘要：
通过随机注意力生成蛋白质序列可以基于小比对生成合理的家族成员，但无法区分特定功能子集。我们证明，通过在采样器的注意力logits中添加一个标量参数作为偏置，可以连续地将生成从整个家族转向用户指定的子集，而无需重新训练或改变模型架构。实践者提供一个小的序列集（例如结合筛选的命中）和一个控制生成倾向的多重比值。该方法对子集代表的内容不敏感：结合、稳定性、特异性或其他属性。我们发现，条件在采样器内部表示层面是精确的，但解码后的序列表型可能不达标，因为用于编码序列的维度缩减不总是保留定义功能分裂的残基级变化。我们称这种差异为校准间隙，并证明它可以通过简单的几何度量来预测，该度量衡量编码如何将功能子集与其他家族部分分开。在五个Pfam家族（Kunitz、SH3、WW、Homeobox和Forkhead结构域）上的实验确认了分离与间隙之间的单调关系，范围在四倍几何范围内。应用于靶向钙通道的omega-锥虫毒素肽，从23个已知结合者中筛选出的定制种子生成超过千个候选物，保留了主要药效基团和所有实验确定的结合决定因素。这些结果表明，随机注意力使实践者能够将少量实验验证的序列扩展到多样化的候选库中，而无需重新训练生成模型。
英文摘要：
Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator. We condition a training-free stochastic-attention sampler by adding one multiplicity ratio to its logits. Increasing this ratio shifts generation from the full family toward the designated subset. For unit-norm memories, the resulting Boltzmann distribution is exactly a Gaussian mixture whose component weights are set by the multiplicities. This result separates exact conditioning in latent space from losses caused by sampling, PCA reconstruction, and sequence decoding. Across five Pfam families, attention followed the analytic target, but recovery of single-residue markers depended on how well PCA separated the designated and background sequences. A matched weighted profile HMM reproduced these markers more directly, while stochastic attention gave lower ESM2 pseudo-perplexity in the Kunitz comparison. Using a curated set of 23 omega-conotoxin sequences as the target subset produced diverse sequences that preserved the cysteine scaffold and Tyr13 and shifted other residues toward the designated set. These sequences are candidates for experimental testing; they do not establish binding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
