Boosting phosphorylation site prediction with sequence feature-based machine learning.

Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequence environment, and amino acid-specific features to decipher phosphate binding sites from a protein sequence. Our method, while compared with other existing methods on 2429 protein sequences taken from standard Phospho.ELM (P.ELM) benchmark data set featuring 11 organisms reports a higher F1 score = 0.504 (harmonic mean of the precision and recall) and ROC AUC = 0.836 (area under the curve of the receiver operating characteristics). The computation time of our proposed approach is much less than that of the recently developed deep learning-based framework. Structural analysis on selected protein sequences informs that our prediction is the superset of the phosphorylation sites, as mentioned in P.ELM data set. The foundation of our scheme is manual feature engineering and a decision tree-based classification. Hence, it is intuitive, and one can interpret the final tree as a set of rules resulting in a deeper understanding of the relationships between biophysical features and phosphorylation sites. Our innovative problem transformation method permits more control over precision and recall as is demonstrated by the fact that if we incorporate output probability of the existing deep learning framework as an additional feature, then our prediction improves (F1 score = 0.546; ROC AUC = 0.849). The implementation of our method can be accessed at http://cse.iitkgp.ac.in/~pralay/resources/PPSBoost/ and is mirrored at https://cosmos.iitkgp.ac.in/PPSBoost.

Bacterial flagellar switching: a molecular mechanism directed by the logic of an electric motor.

Flagellar rotation regulates the phenomenon of chemotaxis in bacteria. The interaction between the stator unit and the rotor unit of the flagellar motors is responsible for switching the direction of bacterial flagellar rotation. However, the molecular interaction mechanism between the stator (MotA/MotB) and the rotor (FliG/FliM/FliN) proteins for the flagellar rotational direction switching was not very clear. To address this, the asymmetry in the copies of FliG, FliM, and FliN molecules was resolved by reconstructing the switch complex using a modeled rotor unit that fulfills the experimentally available geometric constraints. The diameter of our assembled switch complex supported the existing literature. Experimental evidence and the conformational spread model validates our constructed switch complex. Subsequently, normal mode analysis (NMA) on these constructed protomer units revealed that the most fluctuating molecule in the rotor unit is FliG, which interacts with the bacterial stator through its C-terminal domain. NMA also facilitates our understanding of the reorientation mechanism of FliG between the two states of its flagellar rotation, i.e., counter-clockwise to clockwise and vice versa. Our observations regarding speed regulation, the gap between rotor and stator, and the flagellar switching due to the activity of cytoplasmic proteins, indicate that the bacterial flagellar motor uses the same mechanism as that of an electric motor. Graphical abstract Molecular mechanism of the bacterial flagellar switch.