Therapeutic peptides occupy a valuable design space between small molecules and biologics, but their development requires satisfying several competing constraints at once: solubility, hemolytic activity, and nonspecific surface fouling are governed by overlapping sequence features, so improving one property often degrades another. Computational design addresses this by pairing generative models with sequence-based property predictors, iteratively proposing and refining candidates. However, these components are typically wired together as monolithic scripts that are difficult to inspect, extend, or reuse, and they often refine sequences by natural-language reasoning rather than by tracking the evolving multi-property state of each candidate. We present Pepti-Agent, a closed-loop, peptide-specific framework that exposes generation, property prediction, and single-residue mutation as independently inspectable Model Context Protocol (MCP) tools. A large language model controller invokes these tools and consults live predictor output between calls, so refinement is guided by each sequence's current property profile rather than by language reasoning alone. Task-specific PeptideGPT models generate candidates, ProtBERT-based classifiers score solubility, hemolysis, and non-fouling, and two interchangeable mutation operators propose sequence edits. By recording a per-step trace of controller decisions, predictor outputs, and accepted mutations, Pepti-Agent offers a reproducible substrate for benchmarking multi-objective design strategies and for prioritizing candidates for experimental validation.

翻译：治疗性多肽占据了小分子与生物制品之间宝贵的设计空间，但其开发需同时满足多个相互制约的约束条件：溶解度、溶血活性及非特异性表面污染受重叠序列特征调控，因此某一性质的改善常伴随其他性质的劣化。计算设计通过将生成模型与基于序列的性质预测器配对，迭代提出并优化候选方案来解决这一问题。然而，这些组件通常以难以检查、扩展或复用的整体脚本形式耦合，且往往通过自然语言推理而非追踪每个候选序列不断演变的多属性状态进行优化。我们提出Pepti-Agent——一个闭环式多肽专用框架，它将生成、性质预测及单残基突变暴露为可独立检查的模型上下文协议（MCP）工具。大型语言模型控制器在调用这些工具时参考实时预测器输出，使得优化过程由每个序列的当前属性轮廓指导，而非仅依赖语言推理。任务特异性PeptideGPT模型生成候选序列，基于ProtBERT的分类器预测溶解度、溶血性及抗污染性，两种可互换的突变算子提出序列编辑。通过记录每一步控制器决策、预测器输出及已接受突变的轨迹，Pepti-Agent为多目标设计策略基准测试及候选序列实验验证优先级排序提供了可复现的基座。