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Viruses are a diverse biological group capable of infecting several hosts such as bacteria, plants, and animals, including humans. Viral infections constitute a threat to the human population as they may cause high mortality rates, decrease food production, and generate large economical losses. Viruses co-evolve with their hosts and this constant evolution must be clarified to better predict possible viral outbreaks, and to develop improved diagnostic methods and therapeutical approaches. In this review, we summarize several viral databases that store key information retrieved from a variety of omics approaches. Furthermore, we explore the use of such databases to predict Virus-Host interactions through artificial intelligence algorithms, focusing on the latest methodologies to characterize biological networks., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein-protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver at http://moreiralab.com/resources/spotone, only requiring the user to submit a FASTA file with one or more protein sequences.

Membrane proteins are essential vessels for cell communication both with other cells and noncellular structures. They modulate environment responses and mediate a myriad of biological processes. Dimerization and multimerization processes have been shown to further increase the already high specificity of these processes. Due to their central role in various cell and organism functions, these multimers are often associated with health conditions, such as Alzheimer's disease (AD), Parkinson's disease (PD), and diabetes, among others.Understanding the membrane protein dimers' interface takes advantage of the specificity of the structure, for which we must pinpoint the most relevant interfacial residues, since they are extremely likely to be crucial for complex formation. Here, we describe step by step our own in silico protocol to characterize these residues, making use of known experimental structures. We detail the computational pipeline from data acquisition and pre-processing to feature extraction. A molecular dynamics simulation protocol to further study membrane dimer proteins and their interfaces is also illustrated.

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools.

A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set., (Copyright © 2013 Wiley Periodicals, Inc.)

Protein-protein binding is one of the critical events in biology, and knowledge of proteic complexes three-dimensional structures is of fundamental importance for the biochemical study of pharmacologic compounds. In the past two decades there was an emergence of a large variety of algorithms designed to predict the structures of protein-protein complexes--a procedure named docking. Computational methods, if accurate and reliable, could play an important role, both to infer functional properties and to guide new experiments. Despite the outstanding progress of the methodologies developed in this area, a few problems still prevent protein-protein docking to be a widespread practice in the structural study of proteins. In this review we focus our attention on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem. We also focus our attention on the use of a flexible description of the proteins under study and the use of biological information as the localization of the hot spots, the important residues for protein-protein binding. The most common docking softwares are described too., (Copyright 2009 Wiley Periodicals, Inc.)

Highlights: [•] Imaging of NOS in vivo with 99mTc(I) probes would allow earlier diagnosis of diseases. [•] Re(I) non-radioactive surrogates display different degrees of iNOS inhibition. [•] A computational study combining molecular docking, MD simulations and FEP calculations was performed to establish a structure activity relationship. [•] Interactions responsible for iNOS-recognizing ability of the complexes were identified. [•] Electrostatic interactions between the Re(CO)3 core and R260/R382 are a key issue. [Copyright &y& Elsevier]

Nicotinamide Adenine Dinucleotide (NAD) levels are essential for cellular homeostasis and survival. Main sources of intracellular NAD are the salvage pathways from nicotinamide, where Nicotinamide phosphoribosyltransferases (NAMPTs) and Nicotinamidases (PNCs) have a key role. NAMPTs and PNCs are important in aging, infection and disease conditions such as diabetes and cancer. These enzymes have been considered redundant since either one or the other exists in each individual genome. The co-occurrence of NAMPT and PNC was only recently detected in invertebrates though no structural or functional characterization exists for them. Here, using expression and evolutionary analysis combined with homology modeling and protein-ligand docking, we show that both genes are expressed simultaneously in key species of major invertebrate branches and emphasize sequence and structural conservation patterns in metazoan NAMPT and PNC homologues. The results anticipate that NAMPTs and PNCs are simultaneously active, raising the possibility that NAD salvage pathways are not redundant as both are maintained to fulfill the requirement for NAD production in some species. [ABSTRACT FROM AUTHOR]