研究抑制剂与蛋白质结合模式的区别，对于精准药物设计非常有帮助。本文介绍了DFG-in或DFG-out的构象区别，并且按照上述规则对一些经典的药物进行分类。

DFG是三个保守残基的缩写，其中D参与活化状态Mg离子的结合，F也参与活化态R-spine的形成。 DFG in跟out也是区分激酶active、inactive构象的重要依据(详见下文)。

DFG－in构象是指当激酶被上游激活后，天冬氨酸向内旋转，暴露出ATP结合位点，允许ATP的结合进而激活激酶，一般又称DFG－in活性构象。 很多上市的抗肿瘤的激酶抑制剂药物设计成ATP竞争性抑制剂，和ATP竞争性的占据激酶的活性构象，阻止ATP进一步磷酸化该激酶，抑制该激酶的活性，进而起到抗肿瘤的活性作用（例如依鲁替尼的作用模式）。 转发请注明出处，谢谢。

To address this dilemma, we introduce GSDF, a dual-branch architecture combining 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF). Our approach leverages mutual guidance and joint supervision during the training process …

2014年11月24日 · We first grouped all 1950 crystal structures of the human protein kinases from the Protein Data Bank (PDB) into three conformational states, DFG-in, DFG-out, and intermediate, by calculating the directional vectors of the residues in the DFG-motif and comparing them to a reference DFG-in kinase structure (Figure S2; Methods). In brief, DFG-in ...

2013年6月20日 · In this issue of Chemistry & Biology, Hari and colleagues show that two positions in kinase active sites, including the well-known “gatekeeper” residue, regulate “in” versus “out” conformations of the conserved “DFG” motif. These findings suggest yet another role for the gatekeeper residue.

In the synchronous data flow (SDF) model, arcs of a DFG are permitted to hold multiple tokens, and a generalized form of firing rule is used. Moreover, the firing of actors happens at definite points in time, as if at periodic cycles defined by a system clock.

2013年1月1日 · This chapter first introduces two types of graphical representations of digital signal processing algorithms including signal flow graph (SFG) and data flow graph (DFG). Since SFG and DFG are in general used for analyzing structural properties and exploring...

DFG是和SFG对着说的，侧重强调新频率的产生。 DFG的过程中还伴随着信号光，或新产生的闲频光的放大。 所以OPA是侧重强调DFG时的耦合波能量转移，包含在DFG过程里。 高频很强，低频很弱，此时不管要产生差频（闲频），还是放大低频信号，都需要计算放大倍数、效率的问题了。 在信号增益角度看，高频低频就不切题了，所以用泵浦，信号，闲频称呼。 刚开始研究这方面的东西，我的理解是，两者的物理本质基本是一样的，但 OPA 的目的是放大原有的 信号光， …

2024年3月25日 · To address these issues, we introduce GSDF, a novel dual-branch architecture that combines the benefits of a flexible and efficient 3D Gaussian Splatting (3DGS) representation with neural Signed Distance Fields (SDF).