噬菌体编码的抑菌蛋白

噬菌体是细菌的天敌,它利用宿主的细胞机制完成自身的复制。在感染过程中噬菌体基因组进入细菌细胞后立即产生调节或重新定向宿主特定功能的蛋白质(即抑菌蛋白),以逃避多种细菌的防御机制或改变宿主的分子代谢机制。研究发现,这些噬菌体编码的抑菌蛋白可抑制细菌分裂,干扰细菌遗传物质的复制、转录及降解,影响CRISPR介导的细菌免疫以及代谢。明确噬菌体编码的抑菌蛋白如何影响这些宿主的防御或分子代谢机制可以优化目前基于噬菌体的抗菌策略,找出控制细菌感染的新途径,为抑菌药物的发现和设计打开新的大门。本文就近年来发现的噬菌体编码的抑菌蛋白及其抑菌机制的研究进展进行综述。     

机器学习在微生物组宿主表型预测中的应用

随着大数据时代的到来,如何将生物组学海量数据转化为易理解及可视化的知识是当前生物信息学面临的重要挑战之一.为了处理复杂、高维的微生物组数据,目前机器学习算法已被应用于人体微生物组研究,以揭示疾病背后的复杂机制.本文首先简述了微生物组数据处理方法及常用的机器学习算法,如支持向量机(SVM)、随机森林(RF)和人工神经网络...     

宿主菌抗噬菌体机制的研究进展

噬菌体又称细菌病毒,是公认最丰富的微生物,也是最多样性的,这种多样性是适应所面对选择性压力例如普遍存在宿主菌的噬菌体抗性机制。噬菌体通过6步(吸附、注入、复制、转录翻译、组装和释放)侵入细菌并使之裂解,但是当噬菌体感染细菌,就会面临细菌抗噬菌体的机制,宿主菌能够进化出多种抗噬菌体的机制来避免噬菌体的侵染和裂解。本文就对宿主菌抗噬菌体各种机制作一综述。     

噬菌体与宿主菌间的角逐

噬菌体和它们的宿主菌组成了地球上教目最庞大的微生物种群,噬菌体靠寄生宿主菌来扩增繁衍。但在漫长的进化中,噬菌体与宿主菌间不单是捕食关系,它们间还形成了复杂的相互对抗机制,其中抵抗防御机制的抗防御机制也会促使抗．抗防御机制的产生。从生态学角度来看,噬菌体和宿主菌间的共进化保持着动态平衡。本文综述近年来这一领域的研究,为更清楚地了解噬菌体与宿主菌间的关系和应用提供了参考。     

噬菌体的"关闭宿主"功能

噬菌体基因组编码产生某些特殊的蛋白质分子,可与宿主菌生长、代谢的重要调控性蛋白质结合,并使其钝化,从而阻断宿主的生长与繁殖,将宿主菌大分子合成机制和能量装置转向噬菌体自身的复制与增殖。目前研究所获得的有关噬菌体“关闭宿主”功能的证据,主要涉及噬菌体编码的某些蛋白质分子与宿主菌的DNA复制及转录相关因子的相互作用,而这些蛋白质-蛋白质分子间的相互作用将为我们提供新的抗菌药物或抗菌药物作用的靶点,也有助于生物系统进化关系及蛋白质-蛋白质相互作用关系的研究。     

重组T4噬菌体gp37基因可扩大其在沙门氏菌中的宿主范围

[目的]将T4噬菌体WG01宿主决定区的gp37基因片段,与另一株T4噬菌体QL01的相应基因进行同源重组,从而获得嵌合噬菌体并进行宿主谱分析,为阐明T4噬菌体的宿主谱形成机制以及快速筛选针对特定病原菌的噬菌体奠定了基础。[方法]通过同源重组的方法将WG01 gp37上的8个基因片段分别替换给QL01,用沙门氏菌作为宿主菌筛选嵌合噬菌体,并对嵌合噬菌体进行宿主谱、最佳感染复数、一步生长曲线和遗传稳定性测定。[结果]本研究共获得了5株嵌合噬菌体(QWA、QWC、QWF、QWG、QWFG)。宿主谱试验结果表明,与噬菌体QL01相比,嵌合噬菌体对21株沙门宿主菌分别可以多裂解7、8、4、10和9株菌,即嵌合噬菌体都获得了相对较宽的宿主谱,其中QWG的沙门氏菌宿主菌拓宽最多。生物学特性试验结果表明,嵌合噬菌体QWG生物学特性稳定。嵌合噬菌体QWG经连续传代培养20代,测序分析第1代和第20代嵌合噬菌体尾丝蛋白基因在传代过程中的稳定性,测序结果表明,嵌合噬菌体改造部分的基因能稳定遗传。[结论]用基因改造的方法可以产生宿主谱拓宽且能稳定遗传的嵌合噬菌体,为快速筛选针对特定病原菌的噬菌体提供了可能。     

裂解或溶源-细菌遭遇噬菌体后的命运抉择

噬菌体是地球上数量最丰富的有机体,其在自然生态系统的塑造和细菌进化驱动中发挥着至关重要的作用。在与宿主的相互斗争中,噬菌体可以选择以下2种方式决定其与宿主的命运:(1)裂解:通过裂解宿主细胞最终大量释放噬菌体颗粒;(2)溶源:将其染色体整合到宿主细胞基因组中,与宿主建立一种潜在的互存关系。对于一些温和的噬菌体,这种倾向进一步受到感染多样性的调节,其中单一感染主要是裂解性的,而多重感染则多是溶源性的。溶源性的噬菌体不仅可以根据外界环境的理化因子,还可以通过细菌自身的群体感应系统来启动裂解-溶源开关,进而决定其宿主菌的命运。与此同时,宿主细菌在与噬菌体长时间的斗争中也进化出了针对噬菌体的手段。总而言之,噬菌体深刻影响着细菌的群落动态、基因组进化和生态系统等,而这一切都取决于噬菌体与宿主间的斗争模式(裂解/溶源性感染)。本文探讨了导致温和噬菌体对宿主菌进行裂解-溶源命运抉择的影响因素并系统性总结了细菌在面对噬菌体侵染时的应对策略的最新研究进展,以期能为噬菌体与宿主的研究提供建议和帮助。     

噬菌体与细菌相互作用的分子机制研究进展

噬菌体与细菌是自然界中存在最广泛的两类微生物,两者在群体水平、个体水平以及分子水平上均存在复杂的相互作用关系.细菌能够影响溶原性噬菌体的溶原-裂解决策,而被噬菌体感染的细菌基因表达谱也会受到噬菌体影响,使宿主菌的代谢、应激、抵抗力、毒性等多种性状发生改变.现从细菌和噬菌体两者的角度,分别综述细菌抵抗噬菌体感染以及噬菌体...     

噬菌体介导的病原菌-宿主相互作用的进化

微生物学作为生命科学最基础学科之一,历来都是推动生命科学整体进步的原动力.在微生物世界建立发展起来的分子遗传学、数量分类学、分子系统发育进化理论(例如Woese生命3域和系统发育树)及其研究方法,奠定了20世纪生命科学基础研究整体取得重大突破的坚实基础[1].     

噬菌体介导的病原菌—宿主相互作用的进化

微生物学作为生命科学最基础学科之一 ,历来都是推动生命科学整体进步的原动力。在微生物世界建立发展起来的分子遗传学、数量分类学、分子系统发育进化理论 (例如Ｗｏｅｓｅ生命 3域和系统发育树 )及其研究方法 ,奠定了 2 0世纪生命科学基础研究整体取得重大突破的坚实基础[1 ] 。如何认识利用和改造微生物世界奇特的生命现象和丰富的生物资源 ,一直是人类求生存斗争实践中最迫切的永恒研究主题。全球性“暴发性感染疾病”现象就是研究热点领域之一[2 ] ,近年来研究表明该现象与病原菌的“遗传因子转移”有密切关系 ,这类转移事件还加速了…     

基于机器学习的肠道菌群数据建模与分析研究综述

人体肠道菌群与人类的健康和疾病存在密切关系,对肠道菌群的宏基因组数据进行建模和分析,在疾病预测及诊断相关领域科学研究和社会应用方面均具有重要意义。本文从大数据分析和机器学习的角度,对人体肠道菌群数据的建模、分析和预测算法的原理、过程以及典型研究应用实例进行综述,以期推动肠道菌群分析相关研究发展以及探索结合机器学习算法进行肠道菌群分析的有效方式,同时也为开发基于肠道菌群数据的新型诊疗手段提供借鉴,推动我国精准医疗事业发展。     

机器学习在蛋白质功能预测领域的研究进展

蛋白质是有机生命体内不可或缺的化合物,在生命活动中发挥着多种重要作用,了解蛋白质的功能有助于医学和药物研发等领域的研究。此外,酶在绿色合成中的应用一直备受人们关注,但是由于酶的种类和功能多种多样,获取特定功能酶的成本高昂,限制了其进一步的应用。目前,蛋白质的具体功能主要通过实验表征确定,该方法实验工作繁琐且耗时耗力,同时,随着生物信息学和测序技术的高速发展,已测序得到的蛋白质序列数量远大于功能获得注释的序列数量,高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展,由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述,聚焦于机器学习在酶功能预测领域的应用,对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。     

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning-based contact prediction tool, DeepMetaPSICOV (or DMP for short), together with new methods and data sources for alignment generation. DMP evolved from MetaPSICOV and DeepCov and combines the input feature sets used by these methods as input to a deep, fully convolutional residual neural network. We also improved our method for multiple sequence alignment generation and included metagenomic sequences in the search. We discuss successes and failures of our approach and identify areas where further improvements may be possible. DMP is freely available at: https://github.com/psipred/DeepMetaPSICOV .     

利用BP神经网络方法预测西湖叶绿素a的浓度

在西湖共设了 8个采样点 ,通过主成分分析选取了最能代表西湖水质状况的 7号点 (湖心 )作为研究对象。根据 2 0 0 0年 1月至 2 0 0 1年 4月西湖常规监测的水生生态数据 ,并用插值的方法使其生成足够多的样本数 ,利用 BP人工神经网络 ,探索其用于西湖水生生态状况 (叶绿素 a的浓度 )的短期变化趋势预测的可行性 ,从中找出最能反映西湖水生生态状况变化趋势的水质因子用来建立网络。并用 3号点的数据来检验网络的泛化性能 ,发现网络输出值与实际值吻合度较高。结果表明 ,水温和叶绿素a对未来一周的叶绿素 a含量影响最大 ,以这两者作为输入变量建立的网络简单、快捷 ,比其他线性数值模拟预测有较大的优势。说明人工神经网络对叶绿素 a的预测是一种有效工具 ,可为西湖富营养化治理提供科学依据。     

SIDEpro: a novel machine learning approach for the fast and accurate prediction of side-chain conformations

Accurate protein side-chain conformation prediction is crucial for protein modeling and existing methods for the task are widely used; however, faster and more accurate methods are still required. Here we present a new machine learning approach to the problem where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acid and contacting atom types is used to compute these rotamer energies as a function of atomic contact distances. Although direct energy targets are not available for training, the neural networks can still be optimized by converting the energies to probabilities and optimizing these probabilities using Markov Chain Monte Carlo methods. The resulting predictor SIDEpro makes predictions by initially setting the rotamer probabilities for each residue from a backbone-dependent rotamer library, then iteratively updating these probabilities using the trained neural networks. After convergences of the probabilities, the side-chains are set to the highest probability rotamer. Finally, a post processing clash reduction step is applied to the models. SIDEpro represents a significant improvement in speed and a modest, but statistically significant, improvement in accuracy when compared with the state-of-the-art for rapid side-chain prediction method SCWRL4 on the following datasets: (1) 379 protein test set of SCWRL4; (2) 94 proteins from CASP9; (3) a set of seven large protein-only complexes; and (4) a ribosome with and without the RNA. Using the SCWRL4 test set, SIDEpro's accuracy (χ(1) 86.14%, χ(1+2) 74.15%) is slightly better than SCWRL4-FRM (χ(1) 85.43%, χ(1+2) 73.47%) and it is 7.0 times faster. On the same test set SIDEpro is clearly more accurate than SCWRL4-rigid rotamer model (RRM) (χ(1) 84.15%, χ(1+2) 71.24%) and 2.4 times faster. Evaluation on the additional test sets yield similar accuracy results with SIDEpro being slightly more accurate than SCWRL4-flexible rotamer model (FRM) and clearly more accurate than SCWRL4-RRM; however, the gap in CPU time is much more significant when the methods are applied to large protein complexes. SIDEpro is part of the SCRATCH suite of predictors and available from: http://scratch.proteomics.ics.uci.edu/.     

A convolution neural network for higher resolution dose prediction in prostate volumetric modulated arc therapy

PurposeThis study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.MethodsSixty-six patients were treated for prostate cancer with VMAT. We created the treatment plans using the Acuros XB algorithm with 2 mm grid size, followed by the dose calculated using the anisotropic analytical algorithm with 5 mm grid with the same plan parameters. U-net model was used to predict 2 mm grid dose from 5 mm grid dose. We investigated the two models differing for the training data used as input, one used just the low resolution dose (D model) and the other combined the low resolution dose with CT data (DC model). Dice similarity coefficient (DSC) was calculated to ascertain how well the shape of the dose-volume is matched. We conducted gamma analysis for the following: DVH from the two models and the reference DVH for all prostate structures.ResultsThe DSC values in the DC model were significantly higher than those in the D model (p < 0.01). For the CTV, PTV, and bladder, the gamma passing rates in the DC model were significantly higher than those in the D model (p < 0.002–0.02). The mean doses in the CTV and PTV for the DC model were significantly better matched to those in the reference dose (p < 0.0001).ConclusionsThe proposed U-net model with dose and CT image used as input predicted more accurate dose.     

Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins

Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising method to reinvigorate the immune response against cancer. A key step to prioritizing neoantigens for cancer vaccines is computationally predicting which neoantigens are presented on the cell surface by a human leukocyte antigen (HLA). We propose to address this challenge by training a neural network using mass spectrometry (MS) data composed of peptides presented by at least one of several HLAs of a subject. We embed the neural network within a mixture model and train the neural network by maximizing the likelihood of the mixture model. After evaluating our method using data sets where the peptide presentation status was known, we applied it to analyze somatic mutations of 60 melanoma patients and identified a group of neoantigens more immunogenic in tumor cells than in normal cells. Moreover, neoantigen burden estimated by our method was significantly associated with a measurement of the immune system activity, suggesting these neoantigens could induce an immune response.     

Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory (LSTM), with diverse input datasets, and compares their performance. The Blast_Weather_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.     

A composite score for predicting errors in protein structure models

Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.