生物分子的微溶剂化过程*

研究分子的微溶剂化动力学过程是一个热点课题。应用各种光谱、质谱等实验技术并与从头计算和密度泛函等计算方法相结合,通过对生物分子和溶剂分子在气相中形成的分子团簇的研究,可以使我们了解溶剂分子对生物分子的结构和构型的影响。本文首先介绍了一些先进的实验技术及其应用于溶剂化团簇的研究,综述了近年来发展的几种主要理论计算和溶剂化模型方法。文中介绍了氨基酸分子与水、甲醇等发生微溶剂化过程的最新研究进展,然后分别综述了核酸碱基和碱基对、糖类、神经传递分子的溶剂化团簇的最新研究进展。最后,对该领域的研究前景进行了展望。     

Hartree-Fock法计算氨基酸和碱基的电磁参数

利用量子化学中Hartree-Fock模型计算并讨论了氨基酸、碱基分子的偶极矩、转动频率、极化率等，结果表明氨基酸偶极矩的相对大小与分子中的特征基团R的授受电子能力一致，这些分子大多数都具有较大的偶极矩，分子的转动能谱都处在微波频段，并且这些分子在x(y、z)方向都有其本身特有的转动常数。能量与其转动能级差相同的微波有可能通过使分子被激发从而引发生物效应，该结论为寻找微波生物效应的作用位点提供了一定理论依据。     

配体-受体相互作用的计算机模拟及其在药物设计中的应用

配体-受体相互作用在许多生物过程中起重要作用, 计算机分子模拟技术和理论化学计算方法在配体-受体相互作用研究中得到了广泛的应用。本文综述了有关配体-受体相互作用分子模拟和理论计算的常用方法及其在药物设计中的应用。     

计算肽学

肽作为重要的生理活性物质一直受到相关领域的广泛关注。近年来,由于肽在细胞信号转导中所扮演的中心角色以及作为生物药物靶向蛋白质相互作用网络等特殊性质的发现,再次唤起了人们对肽的浓厚兴趣。与之相伴的是,肽的理论和计算研究工作快速增长,并取得了长足进展。本文以“计算肽学”为主题系统概括了该领域的研究范畴和研究特点,并分别从肽的数据库构建、功能活性预测、分子对接、动力学模拟、结构数据分析、分子设计修饰以及系统生物学行为等几方面分类介绍了计算肽学的主要研究方向和当前发展状况。重点在于探讨采用计算化学和生物信息学方法剖析肽与蛋白质识别和相互作用的分子机制和理化基础,进而为肽类药物设计提供理论指导。此外,本文还提出了计算肽学在肽类纳米材料及生物表面活性剂等领域的潜在应用前景。     

生物大分子体系量子化学计算方法新进展

本文就近年来报道的4 种研究生物大分子体系的量子化学计算方法(计算显微镜方法、定域分子轨道方法、线性标度半经验量子化学方法和并行算法) 作了较为详细的介绍, 并展望了该领域的研究前景。     

有机和生物晶体固态核磁共振参数的准确预测

理论计算有助于复杂的有机和生物系统光谱的鉴定.对于核磁共振光谱,固体结晶中的化学位移和四极耦合常数(QCC)受到邻近的分子和晶格的氢键和范德华作用较大的影响,从而显示出与气态单体分子不同的NMR参数.因此,在固体晶体NMR参数的理论计算中有必要将氢键和范德华作用这两个因素考虑进来.基于周期性方法,本文采用L-Ala-Gly二肽和硝基苯晶体作为模型体系来考察该方法计算NMR参数的精度.研究结果显示周期结构模型能够将分子间的氢键和范德华作用考虑进来,得到的化学位移和QCC值明显优于传统的单分子模型和超分子模型得到的结果,采用该方法计算的结果能够重现NMR实验结果.     

生物分子液-液相分离的物理化学机制

近年来,有关生物分子通过液-液相分离机制进行组织定位、功能调控的研究发展迅速。相分离产生的聚集体在众多细胞活动事件中发挥了关键作用。这些聚集体的生物功能是以相分离的物理化学性质为基础的。本文将从相分离聚集体的基本性质、相图、微观结构,相分离的统计热力学、实验和分子模拟研究等方面阐释相分离物理化学机制研究相关进展。对于生物分子相分离的重要功能体系进行了列举和归纳,收集了相分离研究的模式体系,探讨了生物分子相分离的生物功能同物理化学机制之间的关系,总结了生物分子相分离的调控机制和调控分子的设计方法,并对生物分子相分离物理化学机制研究的未来发展方向进行了展望。     

过渡金属络合催化活性顺序的量子化学研究(Ⅱ)——对氮分子的活化

化学模拟生物固氮仍然是当代化学学科的重大基础课题之一,其中心也仍然是探索过渡金属对氮分子的络合活化规律。本文试图用自己建立的以HMO为主,并揉合EHMO、CNDO的量子化学方法研究过渡金属对氮分子的活化问题。1计算方法和结果对氮分子络合活化的计算方法与乙烯分子相似。     

用诱导自洽方法计算振动力常数的研究

本文提出一种迭代方法计算振动力常数,可以比较清楚地显示基频与振动力常数之间的关系,从而能方便地选择合适的力常数限制条件,得到合理的结果.用本方法对一系列不同分子作了计算,结果比较满意,对计算中一些具体问题作了讨论.     

计算化学在分子轨道教学中的应用

分子轨道理论是理解分子电子结构与微观性质的重要理论之一,也是本科生与研究生结构化学教学中的重点与难点。学生对原子轨道组合形成分子轨道、分子轨道能级交叉混合等知识的理解缺乏形象直观、定量的认识。本文通过基于量子化学或密度泛函理论的Gaussian 03计算软件,计算、绘制并分析了F_2、O_2、N_2、HF、CO等的分子轨道能级图,将学生较难理解的内容定量、直观地呈现出来,形象地解释了分子轨道成键原则与电子填充原则等分子轨道理论中的重难点,加深了学生对分子轨道理论的理解,特别是sp轨道混杂导致的σ_(2p_z)与π_(2p)轨道能级交叉这一难点,激发了学生学习的主动性和积极性,提高了教学质量。在此基础上,利用分子轨道理论分析了CO_2的电子结构,使学生学会应用分子轨道理论解决实际问题,巩固了相关课堂理论知识。     

A splicing model‐based DNA‐computing approach on microfluidic chip

DNA computing is a new computation form based on DNA biochemical reactions, which is mainly composed of sticker and splicing computation models. In this work, a microfluidic chip‐based approach was established for splicing model‐based DNA computing. A finite automaton with two input symbols (a, b) and three states (S0, S1, and S2) was applied in the pattern recognition for isosceles triangles. The DNA computation processes of automaton were realized through DNA digestion, ligation, DNA separation, and detection on the microfluidic chip. The established approach is efficient, controllable, and easy to integrate, which paves the way for the building of complete biomolecular computers in the future.     

Boolean Logic Gates Realized with Enzyme-catalyzed Reactions – Unusual Look at Usual Chemical Reactions

Research in the area of molecular computing systems, in the general framework of unconventional computing, has received high attention and resulted in rapid progress in formulating signal-controlled switchable molecules capable to perform Boolean logic operations and basic arithmetic functions. Extension of this research to biomolecular systems allowed sophisticated computational functions much easier than using synthetic molecular and supramolecular species. The advantage of biomolecular systems comparing with synthetic molecular systems is in their complementarity and compatibility allowing easy assembling multi-component systems from various biomolecules, thus increasing their functional complexity. While DNA-based computing systems are promising faster computing than Si-based electronics, at least for solving some combinatorial problems, due to massive parallel operation, enzyme-based logic systems are less promising for computational applications in their narrow definition. However, they offer novel biosensing and bioactuation features operating in binary Yes/No format. The present review article overviews different kinds of enzyme logic gates exemplified with specific enzymatic reactions/cascades. Motivation for this research and its possible applications are discussed. The review will be helpful to researchers working in this specific area to see the comprehensive collection of logic operations performed by the enzyme reactions. The newcomers to the reviewed area will benefit from the example systems representing various logic functions systematically.     

Simulating the Monty Hall problem in a DNA sequencing machine

The Monty Hall problem is a decision problem with an answer that is surprisingly counter-intuitive yet provably correct. Here we simulate and prove this decision in a high-throughput DNA sequencing machine, using a simple encoding. All possible scenarios are represented by DNA oligonucleotides, and gameplay decisions are implemented by sequencing these oligonucleotides from specific positions, with a single run simulating more than 12,000,000 independent games. This work highlights high-throughput DNA sequencing as a new tool that could extend existing capabilities and enable new encoding schemes for problems in DNA computing.     

Biomolecular modeling: Goals, problems, perspectives

Computation based on molecular models is playing an increasingly important role in biology, biological chemistry, and biophysics. Since only a very limited number of properties of biomolecular systems is actually accessible to measurement by experimental means, computer simulation can complement experiment by providing not only averages, but also distributions and time series of any definable quantity, for example, conformational distributions or interactions between parts of systems. Present day biomolecular modeling is limited in its application by four main problems: 1) the force-field problem, 2) the search (sampling) problem, 3) the ensemble (sampling) problem, and 4) the experimental problem. These four problems are discussed and illustrated by practical examples. Perspectives are also outlined for pushing forward the limitations of biomolecular modeling.     

Signal Propagation in Multi‐Layer DNAzyme Cascades Using Structured Chimeric Substrates

Signal propagation through enzyme cascades is a critical component of information processing in cellular systems. Although such systems have potential as biomolecular computing tools, rational design of synthetic protein networks remains infeasible. DNA strands with catalytic activity (DNAzymes) are an attractive alternative, enabling rational cascade design through predictable base‐pair hybridization principles. Multi‐layered DNAzyme signaling and logic cascades are now reported. Signaling between DNAzymes was achieved using a structured chimeric substrate (SCS) that releases a downstream activator after cleavage by an upstream DNAzyme. The SCS can be activated by various upstream DNAzymes, can be coupled to DNA strand‐displacement devices, and is highly resistant to interference from background DNA. This work enables the rational design of synthetic DNAzyme regulatory networks, with potential applications in biomolecular computing, biodetection, and autonomous theranostics.     

Future in biomolecular computation

Summary Large-scale computations for biomolecules are dominated by three levels of theory: rigorous quantum mechanical calculations for molecules with up to about 30 atoms, semi-empirical quantum mechanical calculations for systems with up to several hundred atoms, and force-field molecular dynamics studies of biomacromolecules with 10,000 atoms and more including surrounding solvent molecules. It can be anticipated that increased computational power will allow the treatment of larger systems of ever growing complexity. Due to the scaling of the computational requirements with increasing number of atoms, the force-field approaches will benefit the most from increased computational power. On the other hand, progress in methodologies such as density functional theory will enable us to treat larger systems on a fully quantum mechanical level and a combination of molecular dynamics and quantum mechanics can be envisioned. One of the greatest challenges in biomolecular computation is the protein folding problem. It is unclear at this point, if an approach with current methodologies will lead to a satisfactory answer or if unconventional, new approaches will be necessary. In any event, due to the complexity of biomolecular systems, a hierarchy of approaches will have to be established and used in order to capture the wide ranges of length-scales and time-scales involved in biological processes. In terms of hardware development, speed and power of computers will increase while the price/performance ratio will become more and more favorable. Parallelism can be anticipated to become an integral architectural feature in a range of computers. It is unclear at this point, how fast massively parallel systems will become easy enough to use so that new methodological developments can be pursued on such computers. Current trends show that distributed processing such as the combination of convenient graphics workstations and powerful general-purpose supercomputers will lead to a new style of computing in which the calculations are monitored and manipulated as they proceed. The combination of a numeric approach with artificial-intelligence approaches can be expected to open up entirely new possibilities. Ultimately, the most exciding aspect of the future in biomolecular computing will be the unexpected discoveries.     

Biocomputing Based on DNA Strand Displacement Reactions

The high sequence specificity and precise base complementary pairing principle of DNA provides a rich orthogonal molecular library for molecular programming, making it one of the most promising materials for developing bio-compatible intelligence. In recent years, DNA has been extensively studied and applied in the field of biological computing. Among them, the toehold-mediated strand displacement reaction (SDR) with properties including enzyme free, flexible design and precise control, have been extensively used to construct biological computing circuits. This review provides a systemic overview of SDR design principles and the applications. Strategies for designing DNA-only, enzymes-assisted, other molecules-involved and external stimuli-controlled SDRs are described. The recently realized computing functions and the application of DNA computing in other fields are introduced. Finally, the advantages and challenges of SDR-based computing are discussed.     

Fast evaluation of polarizable forces

Polarizability is considered to be the single most significant development in the next generation of force fields for biomolecular simulations. However, the self-consistent computation of induced atomic dipoles in a polarizable force field is expensive due to the cost of solving a large dense linear system at each step of a simulation. This article introduces methods that reduce the cost of computing the electrostatic energy and force of a polarizable model from about 7.5 times the cost of computing those of a nonpolarizable model to less than twice the cost. This is probably sufficient for the routine use of polarizable forces in biomolecular simulations. The reduction in computing time is achieved by an efficient implementation of the particle-mesh Ewald method, an accurate and robust predictor based on least-squares fitting, and non-stationary iterative methods whose fast convergence is accelerated by a simple preconditioner. Furthermore, with these methods, the self-consistent approach with a larger timestep is shown to be faster than the extended Lagrangian approach. The use of dipole moments from previous timesteps to calculate an accurate initial guess for iterative methods leads to an energy drift, which can be made acceptably small. The use of a zero initial guess does not lead to perceptible energy drift if a reasonably strict convergence criterion for the iteration is imposed.     

大学化学云计算学习环境与协作学习模型的构建

"云计算"在教育教学中的应用构建了"云计算"辅助教学的概念。21世纪的教学方式是以学为主、以教为辅,学习的方式也从个人学习变成了协作学习,这正是"云计算"辅助教学的核心。本文回顾了计算机辅助教学的发展,对"云计算"学习环境与协作学习模型的构建进行了分析探讨,并以"大学化学云学堂"为案例解析了"云计算"学习环境与协作学习模型建立的实际应用,阐述了其设计思路和实现方法,以期为"云计算"辅助学习与教学研究提供一些帮助。