CN118027152A - 一种具有抗炎生物活性的多肽 - Google Patents
一种具有抗炎生物活性的多肽 Download PDFInfo
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Classifications
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- A23L33/00—Modifying nutritive qualities of foods; Dietetic products; Preparation or treatment thereof
- A23L33/10—Modifying nutritive qualities of foods; Dietetic products; Preparation or treatment thereof using additives
- A23L33/17—Amino acids, peptides or proteins
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- A61K8/18—Cosmetics or similar toiletry preparations characterised by the composition
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- A—HUMAN NECESSITIES
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Abstract
本发明涉及生物医药领域,具体涉及一种具有抗炎生物活性的多肽及其相关用途。本申请获得的多肽具有明确的抗炎功效,并且由于其天然来源,因此无毒副作用。
Description
技术领域
本发明涉及生物医药领域,具体涉及一种具有抗炎生物活性的多肽及其相关用途。
背景技术
炎症是机体对感染、伤害或化学刺激引起的防御反应。慢性炎症会反复破坏细胞和组织,从而诱发多种人类疾病。而目前常用的炎症相关化学药物例如:地塞米松存在引起急性过敏反应的风险,例如:严重的皮肤反应,如发痒,皮疹或水泡,在一定程度上限制了使用的人群。
生物活性肽被定义为蛋白质中的肽序列,大多含有2-20个氨基酸,通常富含疏水性氨基酸,其对身体功能产生有益影响或是对人体健康产生积极影响,超出其已知的营养价值。在过去几年中,人们越来越关注寻找不同的生物活性肽序列,这些肽序列可以降低或预防慢性疾病的风险并提供免疫保护。它们通过其多种活性调节重要的身体功能,包括抗高血压、抗菌、抗血栓、免疫调节、阿片类、抗氧化剂和矿物质结合功能。该类活性肽具有副作用小,吸收速度快、安全性高等优点,目前获得研究人员越来越广泛的关注。
生物活性肽来源广泛,目前研究人员已从动物、植物及海洋生物中分离出多种具有抗炎活性的食源性生物活性肽;例如:动物源抗炎活性肽的来源有鹿茸蛋白、昆虫蛋白、乳清蛋白、鲟鱼蛋白等;植物源抗炎活性肽的来源则包括玉米蛋白、小米谷醇溶蛋白、油菜籽蛋白、榛子蛋白等;海洋源抗炎活性肽的来源蛋白有极大螺旋藻、鲱鱼、蟹腿肌肉等。海洋生物占全球生物总量的一半左右,海洋生物资源已成为药物和营养保健应用中新型化合物的重要来源。
为了解决现有技术的缺陷,本申请从基于裸藻来源的原始多肽数据集,采用大数据人工智能结合生物学功效确证实验,筛选获得一条具有明确的抗炎活性的生物活性多肽。
发明内容
本发明的目的在于提供一种具有抗炎生物活性的多肽及其相关用途。
本发明的目的可以通过以下技术方案来实现:
本发明第一方面,涉及一种具有抗炎生物活性的多肽,其氨基酸序列如:LLVGSGNPEEDALRLGAP所示。
在一些实施方案中,所述具有抗炎生物活性的多肽,其氨基端进行乙酰化修饰,羧基端酰胺化修饰。
本发明第二方面,提供了所述生物活性多肽的制备方法,可以通过基因工程的方法人工合成,可以从细胞中通过分离纯化的方法直接获得,更直接通过化学合成制备。
本发明第三方面,提供了所述具有抗炎生物活性的多肽在制备具有抗炎功能的食品、保健品、药物或化妆品中的应用。
在一些实施方案中,本发明涉及所述具有抗炎生物活性的多肽在制备预防和/或治疗炎症性疾病的组合物中的应用。
在一些优选的实施方案中,所述的炎症性疾病包括感染和/或非感染性炎症性疾病。
在一些更优选的实施方案中,所述的炎症因子包括COX-2、TNF-α、IL-1β、IL-6、IL-8、MCP-1、或TGF-β。
本发明第四方面,提供了一种产品,包括所述具有抗炎生物活性的多肽的衍生物;所述衍生物是指在生物活性多肽的氨基酸侧链基团上、氨基端或羧基端进行羟基化、羧基化、羰基化、甲基化、乙酰化、磷酸化、酯化或糖基化等修饰,得到的多肽衍生物,同时不影响其生物学活性。
本发明的第五方面,涉及一种组合物,其含有前述的多肽,以及药学上或食品学或化妆品学上可接受的载体。
在一些实施方案中,所述的组合物包括药物组合物、食品组合物或化妆品组合物。
在一些实施方案中,所述组合物的剂型包括口服剂型、胃肠外剂型,或外用剂型。
在一些实施方案中,所述组合物的剂型还包括膏霜、乳液、水剂、凝胶、油剂、粉剂、泥类、蜡基类、贴类、膜类或冻干类。
在一些优选的实施方案中,所述的组合物还包括赋形剂和佐剂。
在一些实施方案中,所述赋形剂包括:羟乙基纤维素、羟丙基纤维素、羟丙基甲基纤维素、聚乙二醇、聚丙烯酰胺、聚丙烯酸、聚乙烯醇、葡聚糖、乳酸、柠檬酸、吡咯烷酮羧酸、甘油、尿素、山梨糖醇、氨基酸、羊毛脂、矿脂。
在一些实施方案中,所述佐剂包括:增溶剂,增稠剂,胶凝剂,柔软剂,抗氧化剂,悬浮剂,稳定剂,发泡剂,调味剂,表面活性剂,水,离子或非离子乳化剂,螯合剂,防腐剂,维生素,阻滞剂,润湿剂,精油。
本发明的第八方面,本申请的生物活性肽可与另外成分联用,所述另外成分包括但不限于:抗氧化剂、反应性羰基物类清除剂、抗糖化剂、抗组织胺剂、抗病毒剂、抗寄生剂、乳化剂、润肤剂、防皱剂和/或抗老化剂等。
在本发明的第一到第八个方面,所述的具有抗炎生物活性的多肽的浓度为:0.1-100ppm,优选1-50ppm,例如:0.1ppm、1ppm、5ppm、10ppm、50ppm、100ppm;
本申请的发明人通过基于CAMP模型的进一步多肽-蛋白质互作可能性预测模型,,获得潜在具有抗炎功能的多肽LP-18-1,其序列为LP-18-1(LLVGSGNPEEDALRLGAP),并通过LPS诱导的RAW264.7炎症细胞模型实验,证实该生物活性肽能够有效抑制炎症反应中的关键信号分子NO的分泌,并降低多种炎症因子的水平,具有显著的抗炎功效。
附图说明:
图1:LP-18-1对LPS诱导的RAW264.7炎症细胞分泌NO的影响。
图2:LP-18-1对LPS诱导的RAW264.7炎症细胞分泌TNF-α的影响。
图3:LP-18-1对LPS诱导的RAW264.7炎症细胞分泌IL-6的影响。
实施例:
定义
尽管本发明的广义范围所示的数字范围和参数近似值,但是具体实施例中所示的数值尽可能准确的进行记载。然而,任何数值本来就必然含有一定的误差,其是由它们各自的测量中存在的标准偏差所致。另外,本文公开的所有范围应理解为涵盖其中包含的任何和所有子范围。例如记载的“1至10”的范围应认为包含最小值1和最大值10之间(包含端点)的任何和所有子范围;也就是说,所有以最小值1或更大起始的子范围,例如1至6.1,以及以最大值10或更小终止的子范围,例如5.5至10。另外,任何称为“并入本文”的参考文献应理解为以其整体并入。
本申请涉及的氨基酸在此用标准三字母或单字母缩写表示。
实施例1:具有抗炎活性肽AI模型的筛选
针对抗炎活性肽的筛选,主要采用以下步骤:
1.对自有来源裸藻样品通过质谱鉴定得到天然多肽序列并形成多肽数据集,利用分级统计力学模型(HSM模型)进行一系列多肽-蛋白质结合能力预测,从而得到特定多肽与多个皮肤问题相关蛋白质靶点的定量结合亲和力预测打分,根据潜在的相关靶点蛋白进行靶向筛选高结合能力多肽。P_value<0.05的组合表示有一定的结合能力。
2.从这些组合中筛选出与相关靶点蛋白结合的多肽。随后,对筛选获得的阳性多肽进行基于CAMP模型的进一步多肽-蛋白质互作可能性预测,从而得到特定多肽与多个皮肤问题相关蛋白质靶点的定量互作可能性预测打分,即根据潜在的相关靶点蛋白进行靶向筛选具有高互作可能性的多肽。具体而言:CAMP模型对输入的每一对多肽-蛋白质组合进行预测打分,Score>0.5的组合表示有互作可能性,然后从这些组合中筛选出与相关靶点蛋白结合的多肽。
该模型利用两个多通道特征提取器通过卷积神经网络(CNN)来分别处理输入的多肽-蛋白对的特征,包括序列信息,位置特异性得分矩阵(PSSM)(仅蛋白质),二级结构(secondary structure)和内在无序倾向(intrinsic disorder tendency),并采用自注意力机制(self-attention)来学习各残基之间的长期依赖关系以及蛋白质和多肽的单个残基对最终相互作用的贡献,最后使用三个全连接层来预测给定的多肽-蛋白对之间是否存在相互作用。
目前已经有44502对多肽-蛋白对的数据对CAMP模型进行了训练,性能优于现有的机器学习模型。
使用CAMP模型进行预测的流程主要包括:
提取待测多肽-蛋白质对的特征信息,通过Uniprot获取蛋白的序列信息,多肽序列采用经过多肽鉴定的天然来源序列;通过SCRATCH-1D release 1.2(2018,linuxversion,6.3GB)分别获取多肽和蛋白质;通过Psi-blast计算蛋白质的位置特异性得分矩阵;通过IUPred(https://iupred2a.elte.hu/)分别获取多肽和蛋白质的内在无序倾向;运行模型进行预测,根据预测得分筛选与靶点蛋白可能互作的多肽(>0.5)。最终,将预测得分>0.5的多肽-蛋白互作对进行手动分析,确认相应蛋白功效类型,并按功效类型对潜在高分多肽序列进行归类、化学合成及细胞水平的功效验证。通过上述筛选,获得潜在具有抗炎功能的多肽LP-18-1,其序列为LP-18-1(LLVGSGNPEEDALRLGAP),表1为其通过不同筛选模型的打分结果,其中UniprotID为理论预测与该多肽序列形成相互作用的蛋白ID:
表1.候选肽序列及HSM及CAMP模型打分结果
实施例2:LP-18-1抗炎生物学活性实验
具体方法如下:
(1)铺板:细胞消化重悬后用countstar细胞计数仪计数细胞数量,最终将含有细胞的培养基加入到12孔板中,细胞最终密度为1.8×105个/孔。铺板结束后放入细胞培养箱内培养24h;
(2)诱导加样:细胞培养至融合度70%左右时,弃去原培养基,加入含有LPS(0.2μg/mL)的DMEM高糖培养基(Wisent,319-005-CL),同时加入一定浓度的受试物,阳性对照为100μg/mL地塞米松;空白对照对不含LPS的完全培养基;阴性对照为仅含相同浓度LPS的完全培养基。加样完毕后孵育24h。
(3)收样:收集细胞培养液于1.5ml离心管中,-80℃冰箱保存。或根据NO检测说明书(一氧化氮检测试剂盒,碧云天S0021M),检测NO浓度。
实验结果显示(图1):LP-18-1表现一定抗炎作用,且50ppm LP-18-1(抑制率66.55%),较10ppm LP-18-1抗炎效果更显著(抑制率19.18%),因此,结果表明:LP-18-1具有一定的抗炎效果,其符合浓度依赖的基本规律。
实施例3:LP-18-1降低炎症因子水平效果实验
具体方法如下:
(1)铺板:细胞消化重悬后用countstar细胞计数仪计数细胞数量,最终将含有细胞的培养基加入到12孔板中,细胞最终密度为1.8×105个/孔。铺板结束后放入细胞培养箱内培养24h;
(2)诱导加样:细胞培养至融合度70%左右时,弃去原培养基,加入含有LPS(0.2μg/mL)的DMEM高糖培养基(Wisent,319-005-CL),同时加入一定浓度的受试物,阳性对照为100μg/mL地塞米松(DEX);空白对照对不含LPS的完全培养基;阴性对照为仅含相同浓度LPS的完全培养基。加样完毕后孵育24h。
(3)收样:收集细胞培养液于1.5ml离心管中,-80℃冰箱保存。或根据ELISA试剂盒说明书(小鼠TNF-αELISA检测试剂盒,联科生物,K282HS/3-96;小鼠IL-6ELISA检测试剂盒,联科生物,EK206/3-96)检测炎症因子TNF-α以及IL-6释放量。
实验结果显示:LP-18-1表现出一定抑制炎症因子TNF-α和IL-6释放的能力。在浓度为10ppm时,LP-18-1对炎症细胞分泌TNF-α和IL-6的抑制率分别为29.84%和33.59%(图2、图3)。
以上描述地仅是优选实施方案,其只作为示例而不限制实施本发明所必需特征的组合。所提供的标题并不意指限制本发明的多种实施方案。术语例如“包含”、“含”和“包括”不意在限制。此外,除非另有说明,没有数词修饰时包括复数形式,以及“或”、“或者”意指“和/或”。除非本文另有定义,本文使用的所有技术和科学术语的意思与本领域技术人员通常理解的相同。
Claims (10)
1.一种具有抗炎生物活性的多肽,其氨基酸序列如:LLVGSGNPEEDALRLGAP所示。
2.权利要求1所述的具有抗炎生物活性的多肽,其氨基端进行乙酰化修饰,羧基端酰胺化修饰。
3.权利要求1-2任一项所述的具有抗炎生物活性的多肽在制备预防和/或治疗炎症性疾病的组合物中的应用。
4.如权利要求3所述的用途,其特征在于,所述的炎症性疾病包括感染和/或非感染性炎症性疾病。
5.如权利要求4所述的用途,其特征在于,所述的炎症因子包括COX-2、TNF-α或IL-1β。
6.一种组合物,其特征在于,含有权利要求1-2任一项所述的多肽,以及药学上或食品学或化妆品学上可接受的载体。
7.如权利要求6所述的组合物,其特征在于,所述的组合物包括药物组合物、食品组合物或化妆品组合物。
8.如权利要求7所述的组合物,其特征在于,所述组合物的剂型包括口服剂型、胃肠外剂型,或外用剂型。
9.权利要求6-8任一项所述的组合物,其还包括赋形剂和佐剂。
10.权利要求9所述的组合物,所述赋形剂包括:羟乙基纤维素、羟丙基纤维素、羟丙基甲基纤维素、聚乙二醇、聚丙烯酰胺、聚丙烯酸、聚乙烯醇、葡聚糖、乳酸、柠檬酸、吡咯烷酮羧酸、甘油、尿素、山梨糖醇、氨基酸、羊毛脂、矿脂。
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