Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases.
Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases.
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用于在功能上分类和表征丝氨酸β-内酰胺酶的新型计算方案。
DOI：
10.1371/journal.pcbi.1004926
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发表时间：
2016-06
影响因子：
4.3
通讯作者：
Orengo C
中科院分区：
文献类型：
--
作者：
Lee D;Das S;Dawson NL;Dobrijevic D;Ward J;Orengo C
β-内酰胺酶代表了细菌对β-内酰胺抗生素耐药的主要机制，并且是对现代医学的重大挑战。我们已经开发了一种自动化分类和分析方案，该方案利用基于结构和序列的方法，并使我们能够提出一组丝氨酸β-内酰胺酶，其更一致地捕获和合理化现有的三种分类方案：类，（A、C和D，在作用机制的实施方面各不相同）;（其主要反映了通过序列相似性测量的进化距离）;和变体组（其主要对应于通过临床组的Bush-classification）。我们的分析平台利用一套内部和公共工具来识别功能决定因素（FD），即残基位点，负责在不同类别，不同类型和不同变体之间赋予不同的表型。我们专注于A类β-内酰胺酶，这是最常见和临床相关的类别，以鉴定与不同A类类型和变体相关的不同表型中涉及的FD。我们表明，我们的FunFHMMer方法可以分离已知的β-内酰胺酶类，并确定这些位置可能是负责这些酶的作用机制的不同实现。两种新的算法，ASSP和SSPA，允许检测FD网站可能有助于扩大的基板配置文件。使用我们的方法，我们在UniProt中识别出151种A类类型。最后，我们使用我们的β-内酰胺酶FunFams和ASSP谱来检测微生物组样本中的4种新型A类类型。我们的平台已经通过文献研究、计算机模拟分析和一些有针对性的实验验证进行了验证。虽然开发的丝氨酸β-内酰胺酶，他们可以用来分类和分析任何不同的蛋白质超家族，其中亚家族在长期和短期的进化时间尺度分歧。β-内酰胺酶是细菌蛋白质，主要负责对β-内酰胺抗生素的耐药性，因此对现代医学构成重大挑战。虽然有许多研究对β-内酰胺酶进行了分类，但抗生素筛选并不总是一致或全面的，导致这些蛋白质的分类混乱，难以识别具有不同耐药性的细菌。因此，我们开发了自动和一致地分类不同类别和类型的β-内酰胺酶的策略，具有特定的抗生素耐药谱。我们的方法主要集中在β-内酰胺酶的序列上，因为对于大多数新的细菌菌株，我们只知道序列。我们已经将主要公共存储库中存储的所有测序的β-内酰胺酶分类。然后，我们主要关注A类β-内酰胺酶，因为这些酶是导致大多数临床相关抗生素耐药性的原因。我们应用方法来确定关键序列位点，其中变化导致新的抗生素耐药性。了解哪些位点赋予耐药性对于认识新的进化菌株是否可以逃避当前的抗生素方案非常重要。我们的分类方法使我们能够对151种A类丝氨酸β-内酰胺酶类型进行分类，并在排水样品中发现的细菌中识别出一种新型的A类β-内酰胺酶。
Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes, (A, C and D, which vary in their implementation of the mechanism of action); Types (that largely reflect evolutionary distance measured by sequence similarity); and Variant groups (which largely correspond with the Bush-Jacoby clinical groups). Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants (FDs), i.e. residue sites, responsible for conferring different phenotypes between different classes, different types and different variants. We focused on Class A beta-lactamases, the most highly populated and clinically relevant class, to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants. We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes. Two novel algorithms, ASSP and SSPA, allow detection of FD sites likely to contribute to the broadening of the substrate profiles. Using our approaches, we recognise 151 Class A types in UniProt. Finally, we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples. Our platforms have been validated by literature studies, in silico analysis and some targeted experimental verification. Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales. Beta-lactamases are bacterial proteins largely responsible for resistance to beta-lactam antibiotics and so pose a significant challenge to modern medicine. Whilst there are many studies cataloguing beta-lactamases, antibiotic screening has not always been consistent or comprehensive, causing confusion in the classification of these proteins and difficulty in recognising bacteria with different resistance profiles. We therefore developed strategies for automatically and consistently classifying distinct classes and types of beta-lactamases, having particular antibiotic resistance profiles. Our methods focus mainly on the sequences of the beta-lactamases, as for most new bacterial strains we will only know the sequence. We have classified all sequenced beta-lactamases stored in major public repositories into classes. We then mainly focus on the Class A beta-lactamases as these are responsible for most of the resistance to clinically relevant antibiotics. We applied methods to pinpoint key sequence sites where changes result in new antibiotic resistance properties. Understanding which sites confer resistance is important for recognizing whether new evolving strains can evade current antibiotic regimes. Our classification methods allowed us to classify 151 Class A serine beta-lactamase types and to recognize a new type of Class A beta-lactamase in a bacteria found in a drain sample.