只保留结合位点周围残基，大幅提高大靶标蛋白的抗体设计效率。
Discontinuous Epitope Fragments as Sufficient Target Templates for Efficient Binder Design
- 仅保留结合位点附近的不连续残基作为靶标模板
- 设计成功率提升80%，单次设计耗时减少40倍
- 适合难设计的大结构域靶标，如ClpP和ALS3
基于结构的蛋白质设计近年加速了从头结合分子生成，但针对大结构域或跨多个结构域的靶标仍因计算成本高且成功率随靶标增大而下降而困难。我们假设蛋白质折叠神经网络（PFNN）以“局部优先”方式运行，更关注局部相互作用，对全局可折叠性敏感度较低。据此提出仅保留结合位点周围不连续表面残基的策略。相比完整结构域方法，该策略使体外成功率提升高达80%，单次成功设计平均耗时减少40倍，实现了对先前难以攻克靶标如ClpP和ALS3的结合分子设计。在此基础上，我们进一步开发了包含蒙特卡洛进化步骤以突破局部极小值、以及位置特异性偏差逆折叠步骤以优化序列模式的定制化流程。这些进展不仅建立了一套可推广的高效结合分子设计框架，适用于大型且以往不可达的靶标，也支持了“局部优先”作为基于PFNN设计的普遍指导原则。
原文摘要 · Abstract (English)
Recent advances in structure-based protein design have accelerated de novo binder generation, yet interfaces on large domains or spanning multiple domains remain challenging due to high computational cost and declining success with increasing target size. We hypothesized that protein folding neural networks (PFNNs) operate in a ``local-first'' manner, prioritizing local interactions while displaying limited sensitivity to global foldability. Guided by this hypothesis, we propose an epitope-only strategy that retains only the discontinuous surface residues surrounding the binding site. Compared to intact-domain workflows, this approach improves in silico success rates by up to 80% and reduces the average time per successful design by up to forty-fold, enabling binder design against previously intractable targets such as ClpP and ALS3. Building on this foundation, we further developed a tailored pipeline that incorporates a Monte Carlo-based evolution step to overcome local minima and a position-specific biased inverse folding step to refine sequence patterns. Together, these advances not only establish a generalizable framework for efficient binder design against structurally large and otherwise inaccessible targets, but also support the broader ``local-first'' hypothesis as a guiding principle for PFNN-based design.
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