Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods

Abstract

Background Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel protein mechanisms, improving the prediction of protein functions, and gaining information about protein evolution are the main reasons to study MPs. They also play an important role in disease pathways and drug-target discovery. Since detecting MPs experimentally is quite a challenge, most of them are detected randomly. Therefore, introducing an appropriate computational approach to predict MPs seems reasonable. Results In this study, we introduced a competent model for detecting moonlighting and non-MPs through extracted features from protein sequences. We attempted to set up a well-judged scheme for detecting outlier proteins. Consequently, 37 distinct feature vectors were utilized to study each protein’s impact on detecting MPs. Furthermore, 8 different classification methods were assessed to find the best performance. To detect outliers, each one of the classifications was executed 100 times by tenfold cross-validation on feature vectors; proteins which misclassified 90 times or more were grouped. This process was applied to every single feature vector and eventually the intersection of these groups was determined as the outlier proteins. The results of tenfold cross-validation on a dataset of 351 samples (containing 215 moonlighting and 136 non-moonlighting proteins) reveal that the SVM method on all feature vectors has the highest performance among all methods in this study and other available methods. Besides, the study of outliers showed that 57 of 351 proteins in the dataset could be an appropriate candidate for the outlier. Among the outlier proteins, there were non-MPs (such as P69797) that have been misclassified in 8 different classification methods with 16 different feature vectors. Because these proteins have been obtained by computational methods, the results of this study could reduce the likelihood of hypothesizing whether these proteins are non-moonlighting at all. Conclusions MPs are difficult to be identified through experimentation. Using distinct feature vectors, our method enabled identification of novel moonlighting proteins. The study also pinpointed that a number of non-MPs are likely to be moonlighting.

The Minimal Translation Machinery: What We Can Learn From Naturally and Experimentally Reduced Genomes

“…We have worked with the concept one gene-one function, which is known to be inaccurate. It cannot be ruled out that proteins with low substrate specificity could replace the function of others (e.g., a specific methylase could be able to methylate non-specifically other substrates), while it is known that many proteins can be involved in more than one, sometimes unrelated, functions (moonlighting proteins; Shirafkan et al, 2021 ). In addition to experimental validation of genome reductions such as the one achieved with M. mycoides JCVI-syn3.0, a better delineation of what should be the minimal number of genes necessary to obtain a simplified but still efficient bacterial translational apparatus can be achieved by using machine-learning methods to detect replacements and moonlighting scenarios.…”

Moonlighting in Rickettsiales: Expanding Virulence Landscape

“…Due to this, it is difficult to identify and predict if a protein presents moonlight activity. However, bioinformatics approaches have been in development to aid in this process [25][26][27].…”

Correction to: Moonlighting protein prediction using physico‑chemical and evolutional properties via machine learning methods

“…Following the publication of the original article [ 1 ], the authors identified that Dr. Javad Zahiri was not assigned as the co-corresponding author in the published article.…”