STEM与日常科技·英语30篇（3）
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Protein Folding Prediction and Its Impact on Drug Discovery
蛋白质折叠预测及其对药物研发的影响
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Proteins fold into precise 3D shapes that determine how they interact with other molecules in our bodies.蛋白质折叠成精确的三维结构，决定其在人体内如何与其他分子相互作用。
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Misfolded proteins cause diseases like Alzheimer’s, cystic fibrosis, and some cancers.错误折叠的蛋白质会导致阿尔茨海默病、囊性纤维化及某些癌症等疾病。
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AI tools like AlphaFold now predict protein structures from amino acid sequences with atomic accuracy.AlphaFold等AI工具如今能根据氨基酸序列以原子级精度预测蛋白质结构。
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These predictions let scientists model how potential drug molecules might bind to disease targets.这些预测使科学家能够模拟潜在药物分子与疾病靶点的结合方式。
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Virtual screening replaces months of lab trials by simulating millions of chemical interactions digitally.虚拟筛选通过数字化模拟数百万种化学相互作用，取代了长达数月的实验室试验。
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Researchers use predicted structures to design small molecules that stabilize healthy folds or block harmful ones.研究人员利用预测结构设计小分子，以稳定健康构象或阻断有害构象。
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Accurate folding models also help engineer enzymes for green chemistry and biodegradable plastics.精准的折叠模型还有助于设计用于绿色化学和可降解塑料的工程酶。
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Before AI, solving one protein structure could take years; now it takes minutes on modern hardware.AI出现前，解析一个蛋白质结构可能耗时数年；如今在现代硬件上仅需数分钟。
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This speed-up accelerates personalized medicine, especially for rare genetic disorders with unique mutations.这种提速加速了个性化医疗的发展，尤其有利于携带独特突变的罕见遗传病。
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Understanding folding isn’t just biology—it’s computational physics, chemistry, and therapeutic design combined.理解蛋白质折叠不仅是生物学问题，更是计算物理、化学与治疗设计的融合。