Protein molecules differ in flexibility across their three-dimensional configurations. The phi ($\varphi$) and psi ($\psi$) torsion angles primarily define the protein backbone. The flexibility of proteins is related to the fluctuation of the torsion angle. The fluctuation of torsion angles is caused by the differences in backbone torsion angles between various NMR models. The angle fluctuations in the cartesian coordinate space are utilized to define the structural flexibility of proteins and help predict protein function and structure when the torsion angles are employed as constraints. This research attempts to develop a machine-learning method for directly predicting fluctuations in the torsion angle of protein sequences. We collect a number of helpful characteristics of proteins, including disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigrams, position-specific estimated energy, half-sphere exposures. Similarly, we explore well-known machine learning algorithms and present an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle variations using the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of0.746 and 0.737, as well as mean absolute errors of 0.114 and 0.123, for the angle fluctuation of $\varphi$ and $\psi$, respectively, and an improvement of 6.59% in MAE, 24.50% in PCC in the phi angle, and 6.09% in MAE, 21.84% in PCC in the psi angle, compared to the state-of-the-art method proposed by Zhang et al.

MOTIVATION: Transposable Elements (TEs) or jumping genes are DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even mediate duplications and large insertions and deletions in the genome, promoting gross genetic rearrangements. The proper classification of identified jumping genes is important for analyzing their genetic and evolutionary effects. An effective classifier, which can explain the role of TEs in germline and somatic evolution more accurately, is needed. In this study, we examine the performance of a variety of machine learning (ML) techniques and propose a robust method, ClassifyTE, for the hierarchical classification of TEs with high accuracy, using a stacking-based ML method. RESULTS: We propose a stacking-based approach for the hierarchical classification of TEs. When trained on three different benchmark datasets, our proposed system achieved 4%, 10.68% and 10.13% average percentage improvement (using the hF measure) compared to several state-of-the-art methods. We developed an end-to-end automated hierarchical classification tool based on the proposed approach, ClassifyTE, to classify TEs up to the super-family level. We further evaluated our method on a new TE library generated by a homology-based classification method and found relatively high concordance at higher taxonomic levels. Thus, ClassifyTE paves the way for a more accurate analysis of the role of TEs. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/manisa/ClassifyTE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Identification of DNA-binding proteins from only sequence information is one of the most challenging problems in the field of genome annotation. DNA-binding proteins play an important role in various biological processes such as DNA replication, repair, transcription and splicing. Existing experimental techniques for identifying DNA-binding proteins are time-consuming and expensive. Thus, prediction of DNA-binding proteins from sequences alone using computational methods can be useful to quickly annotate and guide the experimental process. Most of the methods developed for predicting DNA-binding proteins use the information from the evolutionary profile, called the position-specific scoring matrix (PSSM) profile, alone and the accuracies of such methods have been limited. Here, we propose a method, called StackDPPred, which utilizes features extracted from PSSM and residue specific contact-energy to help train a stacking based machine learning method for the effective prediction of DNA-binding proteins. Results: Based on benchmark sequences of 1063 (518 DNA-binding and 545 non DNA-binding) proteins and using jackknife validation, StackDPPred achieved an ACC of 89.96%, MCC of 0.799 and AUC of 94.50%. This outcome outperforms several state-of-the-art approaches. Furthermore, when tested on recently designed two independent test datasets, StackDPPred outperforms existing approaches consistently. The proposed StackDPPred can be used for effective prediction of DNA-binding proteins from sequence alone. Availability and implementation: Online server is at http://bmll.cs.uno.edu/add and code-data is at http://cs.uno.edu/∼tamjid/Software/StackDPPred/code_data.zip. Supplementary information: Supplementary data are available at Bioinformatics online.