Multiscale simulations of protein folding: application to formation of secondary structures

A multiscale simulation method of protein folding is proposed, using atomic representation of protein and solvent, combing genetic algorithms to determine the key protein structures from a global view, with molecular dynamic simulations to reveal the local folding pathways, thus providing an integrated landscape of protein folding. The method is found to be superior to previously investigated global search algorithms or dynamic simulations alone. For secondary structure formation of a selected peptide, RN24, the structures and dynamics produced by this method agree well with corresponding experimental results. Three most populated conformations are observed, including hairpin, β-sheet and α-helix. The energetic barriers separating these three structures are comparable to the kinetic energy of the atoms of the peptide, implying that the transition between these states can be easily triggered by kinetic perturbations, mainly through electrostatic interactions between charged atoms. Transitions between α-helix and β-sheet should jump over at least two energy barriers and may stay in the energetic trap of hairpin. It is proposed that the structure of proteins should be jointly governed by thermodynamic and dynamic factors; free energy is not the exclusive dominant for stability of proteins.     

The design of a peptide sequence to inhibit HIV replication: a search algorithm combining Monte Carlo and self-consistent mean field techniques

We developed a search algorithm combining Monte Carlo (MC) and self-consistent mean field techniques to evolve a peptide sequence that has good binding capability to the anticodon stem and loop (ASL) of human lysine tRNA species, tRNA^Lys3, with the ultimate purpose of breaking the replication cycle of human immunodeficiency virus-1. The starting point is the 15-amino-acid sequence, RVTHHAFLGAHRTVG, found experimentally by Agris and co-workers to bind selectively to hypermodified tRNA^Lys3. The peptide backbone conformation is determined via atomistic simulation of the peptide-ASL^Lys3 complex and then held fixed throughout the search. The proportion of amino acids of various types (hydrophobic, polar, charged, etc.) is varied to mimic different peptide hydration properties. Three different sets of hydration properties were examined in the search algorithm to see how this affects evolution to the best-binding peptide sequences. Certain amino acids are commonly found at fixed sites for all three hydration states, some necessary for binding affinity and some necessary for binding specificity. Analysis of the binding structure and the various contributions to the binding energy shows that: 1) two hydrophilic residues (asparagine at site 11 and the cysteine at site 12) “recognize” the ASL^Lys3 due to the VDW energy, and thereby contribute to its binding specificity and 2) the positively charged arginines at sites 4 and 13 preferentially attract the negatively charged sugar rings and the phosphate linkages, and thereby contribute to the binding affinity.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

细菌性肝脓肿临床特点的回顾性分析

目的:总结细菌性肝脓肿(pyogenic liver abscesses,PLA)的临床特点、诊断及治疗经验,提高诊疗水平。方法:回顾性分析哈尔滨医科大学第二临床医学院2013年1月-2014年12月收治的75例PLA患者的临床资料。结果:75例患者的平均年龄57岁,最大78岁,最小34岁。其中,男45例,女30例,男:女为1.5:1。75例患者中表现为发热者69例(92.0%)、寒战28例(37.3%)、右上腹痛66例(88.0%),伴恶心呕吐者28例(37.3%),黄疸15例(20.0%),肝肿大8例(10.7%)。有糖尿病病史患者28例(37.3%)。胆系疾病(78.7%)是本组资料主要的易患因素,其中53例患有胆囊炎(70.7%)。外周血白细胞增高67例(89.3%),血红蛋白降低41例(54.7%),白蛋白降低46例(61.3%);75例患者转氨酶升高58例(77.3%)。肝右叶单发脓肿多见(75例患者中36例,占48%)。51例B超确诊,24例行B超+CT或CT检查。48例行穿刺液细菌培养及25例血培养,其中肺炎克雷伯氏菌为主要病原菌。平均住院天数为19天,多数患者经皮下肝脓肿穿刺或置管引流而好转,1例漏诊为肺炎,2例患者反复入院,1例因菌血症死亡。结论:PLA的临床表现多样,以发热最为常见;多为肝右叶单发,部分患者血红蛋白及血清白蛋白降低。糖尿病为PLA最常见基础疾病,胆系疾病是主要的易患因素。肺炎克雷伯杆菌是本组PLA患者的主要致病菌。     

不同性别急性心梗患者的临床特征及院内治疗预后分析

目的:观察首次确诊为急性心肌梗死(AMI)患者的临床特征并分析不同性别人群的在入院治疗及院内短期预后的差异。方法:回顾性分析2010年2月至2012年4月在沈阳军区总医院心内科初次确诊为AMI的患者271例,并按性别分为两组,其中男性组180例,女性组91例,统计其临床特征、入院后的药物和手术治疗情况,以及院内并发症及预后情况,分析总结两组患者各自的特点和差异。结果:女性患者的年龄要大于男性患者(63±14vs71±11,P0.001),且同时更易患有高血压等心血管疾病合并症(73.3%vs 58.2%,P0.05)。女性患者的发病至入院治疗时间长于男性(P0.001),且入院后行PCI治疗的比例要明显低于男性患者(67%vs 79.4%,P0.05)。初步分析结果表明,女性患者的院内死亡率高于男性(11%vs 3.3%,OR:3.11,95%CI:1.53-7.15),但排除不均衡因素的影响后,男女患者的院内死亡率无明显差异(OR:2.11,95%CI:0.68-5.12)。结论:两性AMI患者的临床特征、入院后治疗及院内短期预后均存在一定差异。根据女性患者的临床特征和院内治疗及预后的现状,应进一步加强对女性高危人群的冠心病诊治知识普及和教育,且对入院后的女性患者应采取更为积极的药物和介入手术治疗手段,以改善预后。     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.