Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins

Unraveling the evolutionary origin of the P5CS gene: a story of gene fusion and horizontal transfer

The accumulation of proline in response to the most diverse types of stress is a widespread defense mechanism. In prokaryotes, fungi, and certain unicellular eukaryotes (green algae), the first two reactions of proline biosynthesis occur through two distinct enzymes, γ-glutamyl kinase (GK E.C. 2.7.2.11) and γ-glutamyl phosphate reductase (GPR E.C. 1.2.1.41), encoded by two different genes, ProB and ProA, respectively. Plants, animals, and a few unicellular eukaryotes carry out these reactions through a single bifunctional enzyme, the Δ1-pyrroline-5-carboxylate synthase (P5CS), which has the GK and GPR domains fused. To better understand the origin and diversification of the P5CS gene, we use a robust phylogenetic approach with a broad sampling of the P5CS, ProB and ProA genes, including species from all three domains of life. Our results suggest that the collected P5CS genes have arisen from a single fusion event between the ProA and ProB gene paralogs. A peculiar fusion event occurred in an ancestral eukaryotic lineage and was spread to other lineages through horizontal gene transfer. As for the diversification of this gene family, the phylogeny of the P5CS gene in plants shows that there have been multiple independent processes of duplication and loss of this gene, with the duplications being related to old polyploidy events.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

A new paradigm for applying deep learning to protein-ligand interaction prediction

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

EEG emotion recognition using improved graph neural network with channel selection

BACKGROUND AND OBJECTIVE

Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with promising applications in healthcare areas such as autism research and emotion detection in pregnant women. However, the complex data acquisition environment provides a variable number of EEG channels, which interferes with the model to simulate the process of information transfer in the human brain. Therefore, this paper proposes an improved graph convolution model with dynamic channel selection.

METHODS

The proposed model combines the advantages of 1D convolution and graph convolution to capture the intra- and inter-channel EEG features, respectively. We add functional connectivity in the graph structure that helps to simulate the relationship between brain regions further. In addition, an adjustable scale of channel selection can be performed based on the attention distribution in the graph structure.

RESULTS

We conducted various experiments on the DEAP-Twente, DEAP-Geneva, and SEED datasets and achieved average accuracies of 90.74%, 91%, and 90.22%, respectively, which exceeded most existing models. Meanwhile, with only 20% of the EEG channels retained, the models achieved average accuracies of 82.78%, 84%, and 83.93% on the above three datasets, respectively.

CONCLUSIONS

The experimental results show that the proposed model can achieve effective emotion classification in complex dataset environments. Also, the proposed channel selection method is informative for reducing the cost of affective computing.

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

Localpdb- a Python package to manage protein structures and their annotations

Motivation

The wealth of protein structures collected in the Protein Data Bank enabled large-scale studies of their function and evolution. Such studies, however, require the generation of customized datasets combining the structural data with miscellaneous accessory resources providing functional, taxonomic and other annotations. Unfortunately, the functionality of currently available tools for the creation of such datasets is limited and their usage frequently requires laborious surveying of various data sources and resolving inconsistencies between their versions.

Results

To address this problem, we developed localpdb, a versatile Python library for the management of protein structures and their annotations. The library features a flexible plugin system enabling seamless unification of the structural data with diverse auxiliary resources, full version control and powerful functionality of creating highly customized datasets. The localpdb can be used in a wide range of bioinformatic tasks, in particular those involving large-scale protein structural analyses and machine learning.

Availability and implementation

localpdb is freely available at https://github.com/labstructbioinf/localpdb. Documentation along with the usage examples can be accessed at https://labstructbioinf.github.io/localpdb/.