Space Based Architecture

本文介绍了在一次技术研讨会上了解到的空间架构概念。该架构融合了REST、SOA、EDA及网格计算等特性,并提及了一些相关产品如GigaSpaces XAP、Oracle Coherence和Hazelcast。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

I was in a technical seminar today, one speaker gave a brief talk on this topic in his presentation. This is my first time to get a feel of such concept.  My instinct tells me it's a stuff of interesting.

 

My first impression with it is the concept bears a combination of following characteristics:

  1. Representational State Transfer (REST),
  2. Service-oriented architecture (SOA)
  3. Event-driven architecture (EDA)
  4. Grid computing .
  5. Shared-Nothing Architecture ,

Introduction on wikipedia can be found at: http://en.wikipedia.org/wiki/Space-based_architecture

 

Products of this regard include GigaSpaces XAP, Oracle Coherence, Hazelcast.

 

Just leave a note here to remind me to get a thorough read later.

 

-losingant

翻译:3D Binding model analysis(FdGOGAT in pink and ACR11 in green) . The key residues are shown as sticks. H-bonds are shown as yellow dashed lines. Binding energy (Docking score: -5.7 kcal/mol) We studied the binding modes and interactions between the target proteins through molecular docking. As shown in the figure, the proteins were represented in cartoon, and the key residues were shown as sticks. Through docking, we found that target proteins has excellent binding energies. In addition, GLU-1264, PRO-1103, ASP-1608 of FdGOGAT can form 4 hydrogen bonds ARG-255, TYR-254, ASN-269, ARG-272 on ACR11. These interactions demonstrate the existence of ubiquitination binding between them. Molecular docking The sequences of the target proteins were obtained from Uniport and subsequently modeled using AlphaFold3, an advanced deep-learning-based protein structure prediction tool. AlphaFold3 employs an end-to-end transformer-based architecture, leveraging both evolutionary multiple sequence alignments (MSAs) and physical constraints to predict highly accurate three-dimensional protein structures. The model integrates an attention-based deep neural network with structural templates to refine its predictions, enabling the accurate determination of protein folding and domain organization. Following structural prediction, the modeled target protein was prepared for docking studies using AutoDockTools 1.5.6 (ADT). The protein structure refinement included: 1. Hydrogenation – Addition of polar hydrogens to optimize hydrogen bonding interactions. 2. Charge Distribution – Assignment of Gasteiger partial atomic charges, ensuring accurate electrostatic modeling. 3. Atomic Type Specification – Defining atomic parameters according to the AutoDock force field, which is essential for molecular docking simulations.For molecular docking, AutoDock Vina was employed, a Monte Carlo-based genetic algorithm that efficiently searches the conformational space of the ligand and evaluates bin
03-23
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值