DIProT:A deep learning based interactive toolkit for efficient and effective Protein design

The protein inverse folding problem,designing amino acid sequences that fold into desired protein structures,is a critical challenge in biological sciences.Despite numerous data-driven and knowledge-driven methods,there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design.In this paper,we present DIProT,an interactive protein design toolkit.DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem,combined with a protein structure prediction model.This integration allows users to incorporate prior knowledge into the design process,evaluate designs in silico,and form a virtual design loop with human feedback.Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets,with promising sequence recovery and inference time.Case studies further illustrate how DIProT can facilitate user-guided protein design.

Computational design of proteins with novel structure and functions

Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence-structure-function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein-protein interactions. Challenges and future prospects of this field are also discussed.

Functional tuning and expanding of myoglobin by rational protein design

Rational protein design is a powerful strategy,not only for revealing the structure and function relationship of natural metalloproteins,but also for creating artificial metalloproteins with improved properties and functions.Myoglobin(Mb),a small heme protein created by nature with diverse functions,has been shown to be an ideal scaffold for rational protein design.The progress reviewed herein includes fine-tuning its native functions of O2binding and transport,peroxidase activity and nitrite reductase(NIR)activity,and rational expanding its functionalities to peroxygenase,heme-copper oxidase(HCO),nitric oxide reductase(NOR),as well as hydroxylamine reductase.These studies have enhanced our understanding of how metalloproteins work in nature,and provided insights for rational design of functional metalloproteins for practical applications in the future.

A Novel Design Method for Protein-Like Molecules from the Perspective of Sheaf Theory

Proteins perform a variety of functions in living organisms and their functions are largely determined by their shape. In this paper, we propose a novel mathematical method for designing protein-like molecules of a given shape. In the mathematical model, molecules are represented as loops of n-simplices (2-simplices are triangles and 3-simplices are tetrahedra). We design a new molecule of a given shape by patching together a set of smaller molecules that cover the shape. The covering set of small molecules is defined using a binary relation between sets of molecules. A new molecule is then obtained as a sum of the smaller molecules, where addition of molecules is defined using transformations acting on a set of (n + 1)-dimensional cones. Due to page limitations, only the two-dimensional case (i.e., loops of triangles) is considered. No prior knowledge of Sheaf Theory, Category Theory, or Protein Science is required. The author hopes that this paper will encourage further collaboration between Mathematics and Protein Science.

A new approach for protein design based on the relative entropy

A new effective and fast minimization approach completely based on the physical theory is proposed for protein design. The sequence space is essentially searched according to the Boltzmann distribution. In this approach, the relative entropy is used as a minimization object function. The method has been tested on an off-lattice model of proteins and the results are better than those obtained from other similar work. Therefore, it can be applied as a uniform frame for both folding and inverse folding of proteins.

A generalized approach for protein design based on the relative entropy

In the present study, we have developed the method brought forward recently for protein design based on the relative entropy. The new approach can be used in more common situation other than the special limits in the anterior method. The results indicate that our generalized method has increased the prediction precision for protein sequence and will be in favor of the study for protein design.

Structural Insight into the Design on Oleanolic Acid Derivatives as Potent Protein Tyrosine Phosphatase 1B Inhibitors

Oleanolic acid derivatives act as newer protein tyrosine phosphatase 1B （PTP-1B） inhibitors for type 2 diabetes mellitus （T2DM）. In order to understand the structural requirement of PTP-1B inhibitors, 52 oleanolic acid derivatives were divided into a training set （34 compounds） and a test set （18 compounds）. The highly reliable and predictive 3D-QSAR models were constructed by CoMFA, CoMSIA and topomer CoMFA methods, respectively. The results showed that the cross validated coefficient （q2） and non-cross-validated coefficient （R2） were 0.554 and 0.999 in the CoMFA model, 0.675 and 0.971 in the CoMSIA model, and 0.628 and 0.939 in the topomer CoMFA model, which suggests that three models are robust and have good exterior predictive capabilities. Furthermore, ten novel inhibitors with much higher inhibitory potency were designed. Our design strategy was that （i） the electronegative substituents （Cl, -CH2OH, OH and -CH2Cl） were introduced into the double bond of ring C, （ii） the hydrogen bond acceptor groups （C≡N and N atom）, electronegative groups （C≡N, N atom, -COOH and -COOCH3） and bulky substituents （C6H5N） were connected to the C-3 position, which would result in generating potent and selective PTP-1B inhibitors. We expect that the results in this paper have the potential to facilitate the process of design and to develop new potent PTP-1B inhibitors.

真菌毒素降解酶及其结构和功能研究进展

真菌毒素作为真菌次级代谢产生的一类化合物,其化学结构稳定并可在食物链中积累,严重威胁人类和禽畜健康并造成重大经济损失。生物酶降解法因安全性高、环境友好等优点而在真菌毒素脱毒方面展现出极大的应用前景。本文系统总结黄曲霉毒素、玉米赤霉烯酮、脱氧雪腐镰刀菌烯醇、赭曲霉毒素和伏马菌素等常见真菌毒素降解酶的挖掘和功能分析,重点关注利用X射线晶体学等方法解析降解酶三维结构、揭示其催化机制的研究进展,并比较分析计算方法预测的结合口袋与试验确定的活性中心;阐述基于蛋白质结构的理性设计策略在真菌毒素降解酶改造中的应用,并讨论利用计算生物学方法预测蛋白质结构和功能、解析酶的作用机制并指导降解酶设计的潜力。本文可为解决食品和饲料中的真菌毒素污染问题提供新思路。

Insights into the structural biology of G-protein coupled receptors impacts drug design for central nervous system neurodegenerative processes

In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selec- tivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and or- thosteric binding sites on dopamine receptors for the treatment of Parkinson＇s disease, and on muscarinic receptors for Alzheimer＇s disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopa- mine receptor holds promise as a relevant therapeutic strategy for Parkinson＇s disease. Regarding the treatment of Alzheimer＇s disease, the design of dualsteric ligands for mono-oligomeric mus- carinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.

On the Defining Equations of Protein’s Shape from a Category Theoretical Point of View

This paper proposes a novel category theoretic approach to describe protein’s shape, i.e., a description of their shape by a set of algebraic equations. The focus of the approach is on the relations between proteins, rather than on the proteins themselves. Knowledge of category theory is not required as mathematical notions are defined concretely. In this paper, proteins are represented as closed trajectories (i.e., loops) of flows of triangles. The relations between proteins are defined using the fusion and fission of loops of triangles, where allostery occurs naturally. The shape of a protein is then described with quantities that are measurable with unity elements called “unit loops”. That is, protein’s shape is described with the loops that are obtained by the fusion of unit loops. Measurable loops are called “integral”. In the approach, the unit loops play a role similar to the role “1” plays in the set Z of integers. In particular, the author considers two categories of loops, the “integral” loops and the “rational” loops. Rational loops are then defined using algebraic equations with “integral loop” coefficients. Because of the approach, our theory has some similarities to quantum mechanics, where only observable quantities are admitted in physical theory. The author believes that this paper not only provides a new perspective on protein engineering, but also promotes further collaboration between biology and other disciplines.

基于核磁共振饱和转移差谱技术筛选Protein A仿生多肽配基

核磁共振饱和转移差谱(STD-NMR)技术能够用于检测小分子配基与大分子蛋白之间的亲和性。利用STD⁃NMR技术评价了基于Protein A设计的仿生肽段与人抗体(hIgG)的亲和力以及结合机制,从中筛选出了与抗体亲和性最强的仿生六肽FYEILH,利用静态吸附试验确定了此六肽与抗体的亲和力(Kd)为0.8×10^-6 mol/L,静态结合载量为61.22 mg/mL,与目前文献中报道的许多多肽配基相比,具有明显的优势。在本研究中成功将Protein A最小化,获得了与抗体具有高亲和性的Protein A仿生多肽配基。

Investigation of Highly Designable Dented Structures in HP Model with Hydrogen Bond Energy

Some highly designable protein structures have dented on the surface of their native structures, and are not full compactly folded. According to hydrophobic-polar （HP） model the most designable structures are full compactly folded. To investigate the designability of the dented structures, we introduce the hydrogen bond energy in the secondary structures by using the secondary-structure-favored HP model proposed by Ou-yang etc. The result shows that the average designability increases with the strength of the hydrogen bond. The designabilities of the structures with same dented shape increase exponentially with the number of secondary structure sites. The dented structures can have the highest designabilities for a certain value of hydrogen bond energy density.

生物统合加工嵌合纤维小体组装模块反应机制探究

为探究生物统合加工嵌合纤维小体中重组蛋白粘连模块与对接模块的分子相互作用及其对嵌合纤维小体组装效率的影响,采用酿酒酵母细胞分泌表达支架蛋白和酶分子催化模块,在胞外通过非变性蛋白凝胶电泳和等温滴定量热法测定分析二级支架蛋白、纤维素酶分别与一级支架蛋白结合时的相互作用反应。结果显示,分别连接二级支架蛋白和纤维素酶蛋白的对接模块与粘连模块的亲和力常数增大,亲和力减小,组装反应为焓变驱动的放热反应并产生氢键,证明这些蛋白分子间亲和力减小是导致二级支架蛋白与一级支架蛋白组装效率较低的主要影响因素。

火麻仁黄嘌呤氧化酶抑制肽的酶解制备工艺研究

为寻找开发火麻仁蛋白,提高火麻应用价值的最佳条件,以火麻蛋白为原料,通过酶解法制备火麻仁黄嘌呤氧化酶抑制肽,采用正交试验法优化中性蛋白酶酶解火麻蛋白制备多肽的最佳工艺条件。以中性蛋白酶用量、酶解时间、酶解温度进行单因素试验,通过正交试验分析获得制备火麻仁黄嘌呤氧化酶抑制肽的最佳酶解条件为pH值6.5,料液比1∶5,酶用量6000 U/g,酶解温度50℃,酶解时间5 h。在此条件下进行的验证试验中,多肽抑制率为78.98%,表明该工艺稳定、可行,具有较高的应用价值。

蛋白质工程提升糖酶热稳定性研究进展

糖酶是制糖工业中不可或缺的一种酶制剂。天然糖酶在长时间高温转化制糖时难以保持较好的稳定性,制约了其在工业生产中的应用。大量研究表明蛋白质工程手段是提升糖酶热稳定性的重要手段和策略。本文系统地综述利用蛋白质工程手段提高糖酶热稳定性的最新进展,详尽分析蛋白质工程中的定向进化、理性设计和半理性设计策略。同时评估这些策略在提高糖酶热稳定性方面的效果及其潜在的工业应用价值,并指出各策略的优势、不足和面临的挑战。最后提出应结合多种方法策略提高糖酶热稳定性的建议,并展望未来研究的方向,以期实现糖酶热稳定性和酶活性的协同提高。

基于生成对抗网络的噬菌体尾部蛋白序列设计

生成对抗网络(GAN)在图像生成、蛋白质设计领域有着广泛应用,但是对于噬菌体尾部蛋白的生成少有研究。提出一种基于GAN的噬菌体尾部蛋白序列生成方法。首先使用Wasserstein距离作为模型的目标函数。其次采用多层感知机(MLP)作为模型的基本结构。然后将MLP扩展为多路径结构。实验结果表明,上述方法取得了0.9241的质量得分、0.8498的多样性得分和1.7739的总得分,优于其它常用的生成方法。相较于单路径MLP,多路径MLP提高了序列的生成效果。所提方法能够生成高质量噬菌体尾部蛋白序列,同时保证生成序列的多样性。

人工Cu-TM1459金属酶催化不对称迈克尔加成反应

不对称迈克尔加成反应是合成手性化合物的重要反应,手性的构建一般由传统手性金属络合物催化完成,催化剂结构复杂,制备困难。人工金属酶可以利用生物大分子替代过渡金属手性催化剂,成为研究的热点。使用TM1459蛋白质支架,在原有的金属结合基序上理性引入两个组氨酸和一个羧酸盐面部三联基序,配位Cu2+,制备了人工Cu-TM1459金属酶。将其用于催化不对称迈克尔加成反应研究,Cu-H52A/H58E变体金属酶具有中等反应活性和较高对映选择性(e.e.值达58%)。进一步通过分子对接和催化机理研究对金属结合位点附近关键残基进行定点突变,I108A/C106V/K24E突变体催化该反应,产率99%,e.e.值93%。

确定性筛选设计在Purcise Q膜纯化抗体的应用

目的:通过确定性筛选设计(DSD)方法建立单抗的Purcise Q膜阴离子交换工艺,进一步降低残留宿主细胞蛋白(HCP)含量。方法:将亲和层析洗脱过滤液作为样品,进行Purcise Q膜层析,选取上样pH、上样电导、载量、流速、峰收集条件5个因子为输入因子,以回收率、HCP去除率、多聚体含量为输出响应值,使用确定性筛选设计DOE模型进行实验。结果:在实验参数范围内,多聚体含量没有明显的变化,HCP含量显著降低。上样电导和载量显著影响HCP去除率,而载量、流速和峰收集条件则显著影响层析回收率。利用JMP的模拟实验功能获得了稳健工艺参数组合(上样电导3 ms/cm、载量300 g/L、流速25 MV/min、峰收集50 mAU/mm),进一步利用决策树机器学习方法自动获得设计空间(上样电导3.0~3.6 ms/cm、载量300~460 g/L、流速5~25 MV/min、峰收集50~180 mAU/mm)。结论:确定性筛选设计实验方法适用于膜层析的工艺开发,能够一段式快速获得稳健工艺参数组合和设计空间,对于工艺放大、工艺转移和降低生产风险极具指导意义。

智能蛋白质工程

蛋白质是生命活动的重要基础物质,在生物体内扮演着多种关键角色,包括构建细胞结构、参与代谢和能量转化、调节生理功能、提供免疫保护、传递信号等。蛋白质多样化的功能是通过其特定的氨基酸序列,以及相应的三维结构来实现的。蛋白质工程通过改变或设计蛋白质的序列与结构来实现特定的功能,从而扩展对蛋白质的理解,并为生物医学、生物材料、生物工程及其他领域的研究提供强大的工具和技术支持。近年来,随着算法的进步、大数据的积累,以及硬件计算能力的提升,人工智能技术得到了快速发展,并逐渐应用于蛋白质工程领域,形成了智能蛋白质工程。通过运用基因组、蛋白质组、蛋白质结构数据库等生物学大数据,以及在数据基础上建立各类先进的深度学习模型,智能蛋白质工程能够实现高效、精准、可预测的蛋白质设计和改造。本文主要侧重于智能蛋白质工程的四个方面,即结构设计、无骨架的序列设计、基于骨架的序列设计,以及其他辅助设计方法,总结了人工智能技术在这些领域取得的最新进展,并汇总了近年来采用智能蛋白质工程技术取得的实践成果。智能蛋白质工程作为一种新兴的技术和方法,展示了巨大的潜力和前景,为未来科学研究和技术创新带来深远的影响,并为解决全球性挑战提供新的解决方案和工具。

2024年诺贝尔化学奖:计算蛋白质设计与蛋白质结构预测的新时代

2024年诺贝尔化学奖授予了三位科学家:David Baker,Demis Hassabis和John Jumper,表彰他们在计算蛋白质设计和蛋白质结构预测方面的开创性工作.这一成就不仅突破了人类对蛋白质这一生命基本分子的理解,还在生物技术、药物开发、材料科学等领域引发了革命性变革.