背景:从头蛋白编码基因在基因组的非编码区从无到有,根据定义,与其他基因无同源性。因此,它们编码的从头蛋白质属于所谓的“暗蛋白质空间”。到目前为止,只有四种从头蛋白质结构被实验近似。同源性低,在大多数情况下,假定的高度无序和有限的结构会导致从头蛋白的低置信度结构预测。这里,我们研究最广泛使用的结构和疾病预测因子,并评估它们对从头出现的蛋白质的适用性.由于AlphaFold2是基于多个序列比对的生成,并且在很大程度上保守和球状蛋白质的解析结构上进行了训练,其在从头蛋白上的性能仍然未知。最近,蛋白质的自然语言模型已用于无对齐结构预测,可能使它们比AlphaFold2更适合从头蛋白。方法:我们应用不同的疾病预测因子(IUPred3短/长,flDPnn)和结构预测因子,一方面是AlphaFold2和基于语言的模型(Omegafold,ESMfold,RGN2)另一方面,四种具有结构实验证据的从头蛋白质。我们比较了不同预测因子之间的结果预测以及现有的实验证据。结果:IUPred的结果,最广泛使用的疾病预测因子,在很大程度上取决于参数的选择,并且与最近在比较评估研究中发现优于大多数其他预测因子的flDPnn存在显着差异。同样,不同的结构预测因子对从头蛋白产生不同的结果和置信度评分.结论:我们建议,虽然在某些情况下,基于蛋白质语言模型的方法可能比AlphaFold2更准确,但从头出现蛋白质的结构预测对于任何预测因子来说仍然是一项艰巨的任务。无论是无序还是结构。
Background: De novo protein coding genes emerge from scratch in the non-coding regions of the genome and have, per definition, no homology to other genes. Therefore, their encoded de novo proteins belong to the so-called \"dark protein space\". So far, only four de novo protein structures have been experimentally approximated. Low homology, presumed high disorder and limited structures result in low confidence structural predictions for de novo proteins in most cases. Here, we look at the most widely used structure and disorder predictors and assess their applicability for de novo emerged proteins. Since AlphaFold2 is based on the generation of multiple sequence alignments and was trained on solved structures of largely conserved and globular proteins, its performance on de novo proteins remains unknown. More recently, natural language models of proteins have been used for alignment-free structure predictions, potentially making them more suitable for de novo proteins than AlphaFold2. Methods: We applied different disorder predictors (IUPred3 short/long, flDPnn) and structure predictors, AlphaFold2 on the one hand and language-based models (Omegafold, ESMfold, RGN2) on the other hand, to four de novo proteins with experimental evidence on structure. We compared the resulting predictions between the different predictors as well as to the existing experimental evidence. Results: Results from IUPred, the most widely used disorder predictor, depend heavily on the choice of parameters and differ significantly from flDPnn which has been found to outperform most other predictors in a comparative assessment study recently. Similarly, different structure predictors yielded varying results and confidence scores for de novo proteins. Conclusions: We suggest that, while in some cases protein language model based approaches might be more accurate than AlphaFold2, the structure prediction of de novo emerged proteins remains a difficult task for any predictor, be it disorder or structure.