Computational studies of the mitochondrial carrier family SLC25. Present status and future perspectives

The members of the mitochondrial carrier family, also known as solute carrier family 25 (SLC25), are transmembrane proteins involved in the translocation of a plethora of small molecules between the mitochondrial intermembrane space and the matrix. These transporters are characterized by three homologous domains structure and a transport mechanism that involves the transition between different conformations. Mutations in regions critical for these transporters' function often cause several diseases, given the crucial role of these proteins in the mitochondrial homeostasis. Experimental studies can be problematic in the case of membrane proteins, in particular concerning the characterization of the structure-function relationships. For this reason, computational methods are often applied in order to develop new hypotheses or to support/explain experimental evidence. Here the computational analyses carried out on the SLC25 members are reviewed, describing the main techniques used and the outcome in terms of improved knowledge of the transport mechanism. Potential future applications on this protein family of more recent and advanced in silico methods are also suggested

Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state

In mitochondria, metabolic processes require the trafficking of solutes and organic molecules, such as amino acids. This task is accomplished by the Mitochondrial Carrier Family members (also known as SLC25), among which the SLC25A29 is responsible for the translocation of basic amino acids. In this regard, nitric oxide levels originated by the arginine mitochondrial catabolism have been shown to strongly affect cancer cells’ metabolic status. Furthermore, the metabolic disease saccharopinuria has been linked to a mitochondrial dysregulation caused by a toxic intermediate of the lysine catabolism. In both cases, a reduction of the activity of SLC25A29 has been shown to ameliorate these pathological conditions. However, no detailed structural data are available on SLC25A29. In the present work, molecular modelling, docking and dynamics simulations have been employed to analyse the structural determinants of ligands recognition by SLC25A29 in the c-state. Results confirm and reinforce earlier predictions that Asn73, Arg160 and Glu161, and Arg257 represent the ligand contact points I, II, and III, respectively, and that Arg160, Trp204 and Arg257 form a stable interaction, likely critical for ligand binding and translocation. These results are discussed in view of the experimental data available for SLC25A29 and other homologous carriers of the same family

DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining that aims to predict several factors of a business process (e.g., next activity prediction) based on the knowledge learned from historical event logs. Despite recent PPM algorithms have gained predictive accuracy using deep learning, they commonly perform an offline analysis of event data assuming that logged processes remain in a steady state over time. However, this is often not the real-world case due to concept drifts. The main goal of this work is to solve the next-activity prediction problem under dynamic conditions of business data streams. To this aim, we propose DARWIN as a novel PPM method that detects concept drifts and adapts a deep neural model to concept drifts. A deep empirical analysis of different factors that may influence the performance of DARWIN in streaming scenarios is provided. Experiments with various benchmark event streams show the effectiveness of the proposed approach

Enhanced security-constrained OPF with FACTS devices

The features of the new electricity market with the presence of many different operators lead to a new interest in the congestion management. The changes to the market-clearing point schedules required by the presence of bottlenecks in the electric grid can be strongly reduced if flexible ac transmission system (FACTS) devices are suitably installed in the transmission system with the aim of redistributing real and reactive power flows. Their optimal setting and operation mode can be determined by the use of customized security-constrained optimal power flow (SCOPF) programs. This paper deals with the use of FACTS devices as control variables in a compact and reduced SCOPF formulation, focusing on the definition of their control regions and on a new procedure implemented to find a global solution without sticking on local minima. The application of the new SCOPF procedure to a real system is also presented

A multi-view deep learning approach for predictive business processes monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces basedon the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety ofinformation possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods

Dockingapp rf: A state-of-the-art novel scoring function for molecular docking in a user-friendly interface to autodock vina

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning-and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features

FOX: a neuro-Fuzzy model for process Outcome prediction and eXplanation

Predictive process monitoring (PPM) techniques have become a key element in both public and private organizations by enabling crucial operational support of their business processes. Thanks to the availability of large amounts of data, different solutions based on machine and deep learning have been proposed in the literature for the monitoring of process instances. These state-of-the-art approaches leverage accuracy as main objective of the predictive modeling, while they often neglect the interpretability of the model. Recent studies have addressed the problem of interpretability of predictive models leading to the emerging area of Explainable AI (XAI). In an attempt to bring XAI in PPM, in this paper we propose a fully interpretable model for outcome prediction. The proposed method is based on a set of fuzzy rules acquired from event data via the training of a neuro-fuzzy network. This solution provides a good trade-off between accuracy and interpretability of the predictive model. Experimental results on different benchmark event logs are encouraging and motivate the importance to develop explainable models for predictive process analytics