SGER: A Distributed Hybrid Optimization Technique for Protein Structure Prediction

SGER:一种用于蛋白质结构预测的分布式混合优化技术

基本信息

  • 批准号:
    9730053
  • 负责人:
  • 金额:
    $ 10万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    1998
  • 资助国家:
    美国
  • 起止时间:
    1998-08-01 至 2000-07-31
  • 项目状态:
    已结题

项目摘要

Abstract Optimization problems arise naturally in many areas of science, engineering, and busi-ness. Some of these problems are discrete, where the solution involves finding the best configuration over a finite number of possible conformations (for example, production scheduling problems in operations research or circuit minimization problems in computer engineering). Other optimization problems are inherently continuous, where the solution involves finding the best solution in some infinite space (for example, entropy minimiza-tion in physics, portfolio allocation problems in finance, or elasticity studies in civil engi-neering). Often, useful abstractions exist which convert one type of problem into a simpler problem of the other type. Some of the most compelling optimization problems in the biological sciences also dis-play this same kind of duality. An obvious example is the problem of predicting a protein's (continuous) three-dimensional shape- and, consequently, its biological function-from its (discrete) primary structure, expressed as the sequence of constituent amino acids { Friesne~6 . Curren~y, protein structure is most accurately determined by experimental means, such as X-Ray crystallography or NMR spectroscopy. A primary goal for compu- tational biologists is to be able to predict the tertiary structure without resorting to experi-mental observation. This project takes a new approach to the problem of predicting protein structure. In partic-ular, the merger of a novel distributed search technique for discrete (combinatorial) opti-mization, and an efficient, polynomial time, interior-point algorithm for solving continuous optimization problems. Each approach will operate using separate, albeit related, energy models, with the discrete system "proposing" conformations for the con-tinuous model to evaluate. The goal is to increase the efficiency of the computation by exchanging information between the two approaches while exploiting parallelism in order to solve realistically-sized proteins. Specifically the project will construct a prototype hybrid computational tool, apply the prototype to a protein conformation problem, and evaluate the solution by comparison to both existing computational approaches and physi-cal reality.
摘要 优化问题自然出现在科学、工程和商业的许多领域。其中一些问题是离散的,解决方案涉及在有限数量的可能构象中找到最佳配置(例如,运筹学中的生产调度问题或计算机工程中的电路最小化问题)。其他优化问题本质上是连续的,其中解决方案涉及在某些无限空间中找到最佳解决方案(例如,物理学中的熵最小化,金融学中的投资组合分配问题或土木工程中的弹性研究)。通常,存在有用的抽象,将一种类型的问题转换为另一种类型的更简单的问题。 生物科学中一些最引人注目的优化问题也显示了这种对偶性。 一个明显的例子是预测蛋白质的(连续的)三维形状的问题,并且因此预测其生物学功能,从其(离散的)一级结构,表示为组成氨基酸的序列(Friesne~6 .目前,蛋白质结构最精确的测定方法是实验方法,如X射线晶体学或核磁共振光谱学。计算生物学家的一个主要目标是能够预测三级结构,而不诉诸于实验观察。 该项目采用了一种新的方法来预测蛋白质结构。特别是,一个新的分布式搜索技术的离散(组合)优化,和一个有效的,多项式时间,连续优化问题的解决途径点算法的合并。每种方法将使用单独的,虽然相关的,能量模型,与离散系统“建议”构象的连续模型进行评估。我们的目标是通过在两种方法之间交换信息来提高计算效率,同时利用并行性来解决实际大小的蛋白质。具体而言,该项目将构建一个原型混合计算工具,将原型应用于蛋白质构象问题,并通过与现有计算方法和物理现实的比较来评估解决方案。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Alberto Segre其他文献

Reduced spinal bone density in young women with amenorrhoea.
闭经的年轻女性脊柱骨密度降低。
A critical look at experimental evaluations of EBL
  • DOI:
    10.1007/bf00114163
  • 发表时间:
    1991-03-01
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Alberto Segre;Charles Elkan;Alexander Russell
  • 通讯作者:
    Alexander Russell

Alberto Segre的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Alberto Segre', 18)}}的其他基金

Pair Programming as a Pedagogical Approach for Promoting Success and Equity in Computer Science Coursework
结对编程作为促进计算机科学课程成功和公平的教学方法
  • 批准号:
    1611908
  • 财政年份:
    2016
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
ITR: Distributed Hybrid Optimization Techniques with Applications to Proteomics and Genomics
ITR:分布式混合优化技术及其在蛋白质组学和基因组学中的应用
  • 批准号:
    0218491
  • 财政年份:
    2002
  • 资助金额:
    $ 10万
  • 项目类别:
    Continuing Grant
The Sixth International Workshop on Machine Learning
第六届机器学习国际研讨会
  • 批准号:
    8903715
  • 财政年份:
    1989
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant

相似国自然基金

Graphon mean field games with partial observation and application to failure detection in distributed systems
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目

相似海外基金

On Principles of Distributed Computing for Message-Passing, Shared-Memory, and Hybrid Systems
消息传递、共享内存和混合系统的分布式计算原理
  • 批准号:
    RGPIN-2022-03304
  • 财政年份:
    2022
  • 资助金额:
    $ 10万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization of Concrete Structures using Hybrid Simulation and Distributed Fibre Optic Sensing
使用混合仿真和分布式光纤传感优化混凝土结构
  • 批准号:
    551193-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 10万
  • 项目类别:
    University Undergraduate Student Research Awards
Mean Field, Distributed and Hybrid Control Systems
平均场、分布式和混合控制系统
  • 批准号:
    RGPIN-2014-04373
  • 财政年份:
    2018
  • 资助金额:
    $ 10万
  • 项目类别:
    Discovery Grants Program - Individual
Research on distributed algorithms on hybrid dynamic networks
混合动态网络分布式算法研究
  • 批准号:
    17K19977
  • 财政年份:
    2017
  • 资助金额:
    $ 10万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Exploratory)
Hybrid Predictive Control for Distributed Multi-agent Systems
分布式多智能体系统的混合预测控制
  • 批准号:
    1710621
  • 财政年份:
    2017
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Mean Field, Distributed and Hybrid Control Systems
平均场、分布式和混合控制系统
  • 批准号:
    RGPIN-2014-04373
  • 财政年份:
    2017
  • 资助金额:
    $ 10万
  • 项目类别:
    Discovery Grants Program - Individual
MRI: SusChEM: Acquisition of a hybrid distributed computer system to enhance integration of chemical theory, computation, and experimental research at Duquesne University
MRI:SusChEM:购买混合分布式计算机系统,以增强杜肯大学化学理论、计算和实验研究的集成
  • 批准号:
    1726824
  • 财政年份:
    2017
  • 资助金额:
    $ 10万
  • 项目类别:
    Standard Grant
Mean Field, Distributed and Hybrid Control Systems
平均场、分布式和混合控制系统
  • 批准号:
    RGPIN-2014-04373
  • 财政年份:
    2016
  • 资助金额:
    $ 10万
  • 项目类别:
    Discovery Grants Program - Individual
Research on distributed big data processing for IoT with hybrid cloud
混合云物联网分布式大数据处理研究
  • 批准号:
    16K16053
  • 财政年份:
    2016
  • 资助金额:
    $ 10万
  • 项目类别:
    Grant-in-Aid for Young Scientists (B)
Mean Field, Distributed and Hybrid Control Systems
平均场、分布式和混合控制系统
  • 批准号:
    RGPIN-2014-04373
  • 财政年份:
    2015
  • 资助金额:
    $ 10万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了