GENPPI: standalone software for creating protein interaction networks from genomes

BackGround Bacterial genomes are being deposited into online databases at an increasing rate. Genome annotation represents one of the first efforts to understand organisms and their diseases. Some evolutionary relationships capable of being annotated only from genomes are conserved gene neighbourhoods (CNs), phylogenetic profiles (PPs), and gene fusions. At present, there is no standalone software that enables networks of interactions among proteins to be created using these three evolutionary characteristics with efficient and effective results. Results We developed GENPPI software for the ab initio prediction of interaction networks using predicted proteins from a genome. In our case study, we employed 50 genomes of the genus Corynebacterium. Based on the PP relationship, GENPPI differentiated genomes between the ovis and equi biovars of the species Corynebacterium pseudotuberculosis and created groups among the other species analysed. If we inspected only the CN relationship, we could not entirely separate biovars, only species. Our software GENPPI was determined to be efficient because, for example, it creates interaction networks from the central genomes of 50 species/lineages with an average size of 2200 genes in less than 40 min on a conventional computer. Moreover, the interaction networks that our software creates reflect correct evolutionary relationships between species, which we confirmed with average nucleotide identity analyses. Additionally, this software enables the user to define how he or she intends to explore the PP and CN characteristics through various parameters, enabling the creation of customized interaction networks. For instance, users can set parameters regarding the genus, metagenome, or pangenome. In addition to the parameterization of GENPPI, it is also the user’s choice regarding which set of genomes they are going to study. Conclusions GENPPI can help fill the gap concerning the considerable number of novel genomes assembled monthly and our ability to process interaction networks considering the noncore genes for all completed genome versions. With GENPPI, a user dictates how many and how evolutionarily correlated the genomes answer a scientific query.

Spliceator: multi-species splice site prediction using convolutional neural networks

Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.

Interface Modeling for the Ab Initio Evaluation of the Water Contact Angle on a Metallic Cu(111) Surface

Controlling the water contact angle on a surface is important for regulating its wettability in industrial applications. Therefore, it is crucial to develop ab initio evaluation methods that can accurately predict this angle. The ab initio predictions require an adsorption structure model for the adsorption of liquid molecules on a surface, but the construction of this model depends on whether the test surface comprises an insulating or metallic material because the surface reconstruction takes quite a different form in each case. Previous studies have focused on the estimation of the water contact angle on insulators; however, this study elucidates the water contact angle on a metallic surface, Cu(111). Because the feasibility of ab initio evaluations depends on the approximation of liquid–gas interface energy, which can be roughly estimated through the interface energy of crystal ice, it is natural to use the periodic-honeycomb array of water molecules as the adsorption model for the water on the surface. However, despite the successful application of the periodic model for ab initio prediction of the water contact angle on insulating surfaces, applying this model to metallic surfaces has not provided satisfactory predictions that reproduce experimental values. Therefore, in this study, we propose the use of models with isolated water oligomers for the ab initio prediction of the water contact angle on a metallic surface, which achieved an accurate prediction. The ambiguity of the models based on the size and coverage of the oligomers was small (∼ ±10 °), which was averaged out to give a plausible value based on the Boltzmann weight with the adsorbing energies. The proposed procedure can be used to estimate the wettability of the surfaces of other metallic materials.