National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi, India.
BMC Bioinformatics. 2010 Jan 27;11:57. doi: 10.1186/1471-2105-11-57.
Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference.
We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool http://www.nii.res.in/pred_acs_substr.html has been developed for automated functional classification of ACS enzymes.
We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities.
属于酰基辅酶 A 合成酶 (ACS) 超家族的酶激活了广泛的底物，并在微生物和植物中各种次级代谢物的结构和功能多样性的增加中发挥了主要作用。然而，由于该超家族内的序列差异较大，通过从最接近的同源物进行注释转移来预测其底物偏好性是困难的。因此，公共数据库中存在的大量 ACS 序列在底物特异性水平上缺乏任何功能注释。最近，有几个例子报告了与荧光素酶或香豆酰辅酶 A 连接酶具有高度序列相似性的酶在实验研究中出乎意料地发现可以激活脂肪酸酰基底物。在这项工作中，我们研究了 ACS 的底物特异性与其序列/结构特征之间的关系，并开发了一种新的计算协议，用于在计算机中分配底物偏好。
我们使用了一种基于知识的方法，该方法涉及为各种经过实验表征的 ACS 编译底物特异性信息，并为每个亚家族推导 HMM 轮廓。这些 HMM 轮廓可以以高特异性 (Sp) 和敏感性 (Sn)（Sn = 0.91-1.0，Sp = 0.96-1.0）值准确地区分可能的同源底物与非同源可能性。使用同源晶体结构，我们确定了对底物识别至关重要的少数接触残基，即特异性决定残基 (SDR)。来自不同亚家族的 SDR 模式已被用于得出相关规则，以将其与底物偏好相关联。SDR 方法的有效性已通过对显示明显异常底物偏好的酶进行正确预测底物来证明。此外，在活性位点进行了底物的分子建模，以了解底物选择的结构基础。已经开发了一个基于网络的预测工具 http://www.nii.res.in/pred_acs_substr.html，用于 ACS 酶的自动功能分类。
我们开发了一种使用少数 SDR 预测 ACS 超家族酶底物偏好的新计算协议。使用这种方法，可以将大量存在于各种基因组中的 ACS 酶的底物偏好分配。它有可能有助于合理设计具有改变的底物特异性的新型蛋白质。
