HECBioSim: The UK High End Computing Consortium for Biomolecular Simulation.

HECBioSim：英国生物分子模拟高端计算联盟。

• 批准号: EP/X035603/1
• 负责人: Syma Khalid
• 金额: $ 66.07万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX035603%2F1
• 关键词: HECBioSim; UK; End; Computing; Consortium

Biomolecular simulations enable us to predict the behaviour of biological systems given the structures (or models) of the components. Combined with the structural biology and new advancements in machine learning methods, molecular simulations provide the scope for unprecedented insights into biomolecular systems. The level of detail afforded by these methods, along with their ability to rationalise and augment experimental data and their predictive power in generating new hypotheses are already enabling them to make significant contributions in a wide variety of areas that are crucial for healthcare, the environment, quality of life and consequently, to the economy. The UK biomolecular simulation community has a strong international reputation, with world-leading research in in drug design and development, biocatalysis, bionano-technology, chemical biology and medicine. This has recently been demonstrated by the enormous impact our work has made on research into the SARS-CoV2 virus, in helping identify target sites for vaccine and antiviral development and has lead to our members winning and being shortlisted for international prizes, establishing new collaborations with industrial partners and being key participants in large international consortia.Furthermore, in addition to our work on Covid-19, we have delivered outstanding research with impact in bionanotechology, drug design, AMR as well as in developing novel models (e.g. fluctuating finite elements models), methodologies (e.g. conversion between fine-grained and coarse-grained resolutions) and enabling technologies (e.g.tools for efficient submission of calculations) for application across the full spectrum of biophysical and biochemical sciences. We are now at a time when we can take giant leaps in terms of the scope of our work. Having access to the largest, most modern computing facilities in the UK is essential for this. Renewal of the Consortium will enable us to continue allocating time ARCHER2 and Tier 2 resources for our cutting-edge biomolecular simulations.We will place a special emphasis on reaching out to experimentalists (indeed we already have a strong reputation for doing this) and scientists working in industry in order to foster interactions between computational and experimental scientists, and academia and industry to encourage integrated multidisciplinary studies of key problems.Biomolecular simulation is an integral part of drug design and development. The pharmaceutical industry needs well-trained scientists in this area, as well as the development of new methods (e.g. for prediction of drug binding affinities, ligand selectivity and metabolism). Members of the consortium have a strong track record of collaboration with industry to deliver trained scientists and new methodologies. For example, PhD students trained by consortium members have recently taken up positions in UCB, Unilever, Oxford Nanopore Technologies and Exscientia. Many of these academic-industry collaborations have been strengthened and in some cases, been established on the basis of work done through HECBioSim allocations. The Consortium will continue to welcome new members from across the whole community including ECRs. Indeed we have an excellent track record of ECRs from within our community going on to independent academic posts. We will continue to develop computational tools and training for both experts and non-experts using biomolecular simulation on HEC resources. We propose to develop new tools that will enable more efficient simulations by harnessing tools from the machine learning field an augmenting them with our own codes to make best use of the UK HEC landscape.In summary, HECBioSim will expand our portfolio of collaborative endeavours with experimental scientists and those working in industry to harness expertise from all domains to inform our own work and thus to maintain the UK as a world-leader in biomolecular simulation.

生物分子模拟使我们能够在给定组件结构（或模型）的情况下预测生物系统的行为。分子模拟与结构生物学和机器学习方法的新进展相结合，为生物分子系统提供了前所未有的洞察力。这些方法提供的详细程度，以及它们合理化和增强实验数据的能力以及产生新假设的预测能力，已经使它们能够在对医疗保健、环境、生活质量乃至经济至关重要的各个领域做出重大贡献。英国生物分子模拟界享有很高的国际声誉，在药物设计和开发、生物催化、生物纳米技术、化学生物学和医学方面拥有世界领先的研究。最近，我们的工作对 SARS-CoV2 病毒的研究产生了巨大影响，帮助确定了疫苗和抗病毒药物开发的靶点，并导致我们的成员赢得并入围国际奖项，与工业合作伙伴建立了新的合作，并成为大型国际联盟的主要参与者。此外，除了我们在 Covid-19 方面的工作外，我们还在以下领域开展了具有影响力的杰出研究： 生物纳米技术、药物设计、AMR，以及开发新模型（例如波动有限元模型）、方法（例如细粒度和粗粒度分辨率之间的转换）和支持技术（例如有效提交计算的工具），以应用于整个生物物理和生化科学领域。我们现在正处于一个可以在工作范围上实现巨大飞跃的时期。为此，拥有英国最大、最现代化的计算设施至关重要。联盟的更新将使我们能够继续为我们的尖端生物分子模拟分配时间 ARCHER2 和 Tier 2 资源。我们将特别重视与实验学家（事实上，我们在这方面已经享有盛誉）和工业界工作的科学家进行接触，以促进计算和实验科学家以及学术界和工业界之间的互动，鼓励对关键问题进行综合多学科研究。生物分子 模拟是药物设计和开发的一个组成部分。制药行业需要该领域训练有素的科学家，以及新方法的开发（例如预测药物结合亲和力、配体选择性和代谢）。该联盟成员在与业界合作提供训练有素的科学家和新方法方面有着良好的记录。例如，由联盟成员培养的博士生最近在 UCB、联合利华、Oxford Nanopore Technologies 和 Exscientia 任职。许多学术与工业合作都得到了加强，在某些情况下，是在通过 HECBioSim 拨款完成的工作的基础上建立的。联盟将继续欢迎来自整个社区的新成员，包括 ECR。事实上，我们在社区内的 ECR 晋升为独立学术职位方面有着出色的记录。我们将继续使用 HEC 资源上的生物分子模拟来开发计算工具并为专家和非专家提供培训。我们建议开发新工具，通过利用机器学习领域的工具并用我们自己的代码增强它们，从而充分利用英国 HEC 景观，从而实现更高效的模拟。总之，HECBioSim 将扩大我们与实验科学家和工业界工作人员的合作组合，利用各个领域的专业知识为我们自己的工作提供信息，从而保持英国在生物分子模拟领域的世界领先地位。

项目成果

The liquid-to-solid transition of FUS is promoted by the condensate surface.

FUS 的液-固转变是由冷凝表面促进的。

• DOI: 10.1073/pnas.2301366120
• 发表时间: 2023
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Shen,Yi;Chen,Anqi;Wang,Wenyun;Shen,Yinan;Ruggeri,FrancescoSimone;Aime,Stefano;Wang,Zizhao;Qamar,Seema;Espinosa,JorgeR;Garaizar,Adiran;StGeorge-Hyslop,Peter;Collepardo-Guevara,Rosana;Weitz,DavidA;Vigolo,Daniele;Knowles,Tuom
• 通讯作者: Knowles,Tuom

Location and Concentration of Aromatic-Rich Segments Dictates the Percolating Inter-Molecular Network and Viscoelastic Properties of Ageing Condensates.

• DOI: 10.1002/advs.202207742
• 发表时间: 2023-09
• 期刊: ADVANCED SCIENCE
• 影响因子: 15.1
• 作者: Blazquez, Samuel;Sanchez-Burgos, Ignacio;Ramirez, Jorge;Higginbotham, Tim;Conde, Maria M.;Collepardo-Guevara, Rosana;Tejedor, Andres R.;Espinosa, Jorge R.
• 通讯作者: Espinosa, Jorge R.