Ensembling methods for protein-ligand binding affinity prediction.

Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features.

Distance plus attention for binding affinity prediction.

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge.

Real-to-bin conversion for protein residue distances.

Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. Real values than bin probabilities could more naturally represent inter-residue distances, while the latter, via spline curves more naturally helps obtain differentiable objective functions than the former.

Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction.

Protein structure prediction (PSP) is a crucial issue in Bioinformatics. PSP has its important use in many vital research areas that include drug discovery. One of the important intermediate steps in PSP is predicting a protein's beta-sheet structures.

A stacked meta-ensemble for protein inter-residue distance prediction.

Predicted inter-residue distances are a key behind recent success in high quality protein structure prediction (PSP). However, prediction of both short and long distance values together is challenging. Consequently, predicted short distances are mostly used by existing PSP methods.

Enhancing protein contact map prediction accuracy via ensembles of inter-residue distance predictors.

Protein contact maps capture coevolutionary interactions between amino acid residue pairs that are spatially within certain proximity threshold. Predicted contact maps are used in many protein related problems that include drug design, protein design, protein function prediction, and protein structure prediction. Contact map prediction has achieved significant progress lately but still further challenges remain with prediction of contacts between residues that are separated in the amino acid residue sequence by large numbers of other residues.

Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.

Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the protein sequence remain challenging. To deal with these challenges, state-of-the-art inter-residue distance prediction algorithms have used large sets of coevolutionary and non-coevolutionary features.

predML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization.

Identifying of post-translational modifications (PTM) is crucial in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Computational methods for predicting multiple PTM at the same lysine residues, often referred to as K-PTM, is still evolving. This paper presents a novel computational tool, abbreviated as predML-Site, for predicting KPTM, such as acetylation, crotonylation, methylation, succinylation from an uncategorized peptide sample involving single, multiple, or no modification.

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance.

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites.

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance.

Post-translational modification (PTM) involves covalent modification after the biosynthesis process and plays an essential role in the study of cell biology. Lysine phosphoglycerylation, a newly discovered reversible type of PTM that affects glycolytic enzyme activities, and is responsible for a wide variety of diseases, such as heart failure, arthritis, and degeneration of the nervous system. Our goal is to computationally characterize potential phosphoglycerylation sites to understand the functionality and causality more accurately.

Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction.

For the importance of protein subcellular localization in different branches of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays a most vital role in protein subcellular localization prediction specially in case of machine learning techniques. Single feature representation-like pseudo amino acid composition (PseAAC), physiochemical property models (PPM), and amino acid index distribution (AAID) contains insufficient information from protein sequences.