Hierarchical representation for PPI sites prediction

Background: Protein–protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection. Results: We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar. Conclusions: The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.

To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points. DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively). Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making. [Abstract copyright: © 2022. The Author(s).

Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites

Proteins carry out a broad range of functions in living organisms usually by interacting with other molecules. Protein–protein interaction (PPI) is an important base for understanding disease mechanisms and for deciphering rational drug design. The identification of protein interactions using experimental methods is expensive and time-consuming. Therefore, efficient computational methods to predict PPIs are of great value to biologists. This work focuses on predicting protein interfaces and investigates the effect of different molecular representations in the prediction of such sites. We introduce a molecular representation according to its hierarchical structure. Therefore, proteins are abstracted in terms of spatial and sequential neighboring among amino acid pairs, while we use a deep learning framework, Graph Convolutional Networks, for data training. We tested the framework on two classes of proteins, Antibody–Antigen and Antigen–Bound Antibody, extracted from the Protein–Protein Docking Benchmark 5.0. The obtained results in terms of the area under the ROC curve (AU-ROC) on these classes are remarkable

ProSPs: Protein Sites Prediction Based on Sequence Fragments

Identifying interacting sites of proteins is a relevant aspect for drug and vaccine design, and it provides clues for understanding the protein function. Although such a prediction is a problem extensively addressed in the literature, just a few approaches consider the protein sequence only. The use of the protein sequences is an important issue because the three-dimensional structure of proteins could be unknown. Moreover, such a structural determination experimentally is expensive and time-consuming, and it may contain errors due to experimentation. On the other hand, sequence based method suffers when the knowledge of sequence is incomplete.In this work, we present ProSPs, a method for predicting the protein residues considering protein sequence fragments, which are obtained using sliding windows and become the samples for an unbalance binary classification problem. We use the Random Forest classifier for data training. Each amino acid is enriched using a selected subset of physicochemical and biochemical amino acid characteristics from the AAIndex1 database. We test the framework on two classes of proteins, Antibody-Antigen and Antigen-Bound Antibody, extracted from the Protein-Protein Docking Benchmark 5.0. The obtained results evaluated in terms of the area under the ROC curve (AU-ROC) on these classes outperform the sequence-based algorithms in the literature and are comparable with the ones based on three-dimensional structure

Stress Detection from Wearable Sensor Data Using Gramian Angular Fields and CNN

Stress is a body reaction that is one of the principal causes of many physical and mental disorders, including cardiovascular disease and depression. Developing robust methods for rapid and accurate stress detection plays an important role in improving people’s life quality and wellness. Prior research shows that analyzing physiological signals collected from wearable sensors is a reliable predictor of stress. For stress detection, methods based on machine learning techniques have been defined in the literature. However, they require hand-crafted features to be effective. Deep learning-based approaches overcome these limitations. In this work, we introduce STREDWES, a method for stress detection that analyzes biosignals obtained from wearable sensor data. STREDWES extracts signal fragments using a sliding windows approach and converts them into Gramian Angular Fields images. These images are then classified using a Convolutional Neural Network, a deep learning algorithm. We apply our method to a publicly available dataset. The analysis of the performance values shows that our method outperforms other state-of-the-art competitors