Structure-based prediction of protein-nucleic acid binding using graph neural networks

A comprehensive review of computational methods for Protein-DNA binding site prediction

Accurately identifying protein-DNA binding sites is essential for understanding the molecular mechanisms underlying biological processes, which in turn facilitates advancements in drug discovery and design. While biochemical experiments provide the most accurate way to locate DNA-binding sites, they are generally time-consuming, resource-intensive, and expensive. There is a pressing need to develop computational methods that are both efficient and accurate for DNA-binding site prediction. This study thoroughly reviews and categorizes major computational approaches for predicting DNA-binding sites, including template detection, statistical machine learning, and deep learning-based methods. The 14 state-of-the-art DNA-binding site prediction models have been benchmarked on 136 non-redundant proteins, where the deep learning-based, especially pre-trained large language model-based, methods achieve superior performance over the other two categories. Applications of these DNA-binding site prediction methods are also involved.

Molecular surfaces modeling: Advancements in deep learning for molecular interactions and predictions

Molecular surface analysis can provide a high-dimensional, rich representation of molecular properties and interactions, which is crucial for enabling powerful predictive modeling and rational molecular design across diverse scientific and technological domains. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in accelerating molecular discovery and innovation. The integration of AI techniques with molecular surface analysis has opened up new frontiers, allowing researchers to uncover hidden patterns, relationships, and design principles that were previously elusive. By leveraging the complementary strengths of molecular surface representations and advanced AI algorithms, scientists can now explore chemical space more efficiently, optimize molecular properties with greater precision, and drive transformative advancements in areas like drug development, materials engineering, and catalysis. In this review, we aim to provide an overview of recent advancements in the field of molecular surface analysis and its integration with AI techniques. These AI-driven approaches have led to significant advancements in various downstream tasks, including interface site prediction, protein-protein interaction prediction, surface-centric molecular generation and design.

A new strategy for Cas protein recognition based on graph neural networks and SMILES encoding

The CRISPR-Cas system, an adaptive immune mechanism found in bacteria and archaea, has evolved into a promising genomic editing tool, with various types of Cas proteins playing a crucial role. In this study, we developed a set of strategies for mining and identifying Cas1 proteins. Firstly, we analyzed the characteristic differences of 14 types of Cas proteins in the protein large language model embedding space in detail; then converted proteins into the Simplified Molecular Input Line Entry System (SMILES) format, thereby constructing graph data representing atom and bond features. Next, based on the characteristic differences of different Cas proteins, we designed and trained an ensemble model composed of two Directed Message Passing Neural Network (DMPNN) models for high-precision identification of Cas1 proteins. This ensemble model performed excellently on both training data and newly designed datasets. The comparison of this method with other methods, such as CRISPRCasFinder, has demonstrated its effectiveness. Finally, the ensemble model was successfully employed to identify potential Cas1 proteins in the Ensemble database, further highlighting its robustness and practicality. The strategies and models from this research may potentially be extended to other types of Cas proteins, though this would require further investigation and validation. Moreover, our work highlights SMILES encoding as a versatile tool for studying biological macromolecules, enabling efficient structural representation and advanced computational applications in protein research and beyond.

Highly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment

Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima. Such mapping allows speeding up simulations by precalculating the intermolecular energy values. Our earlier study combined FFT docking with the Monte Carlo protocol, enabling simulation of cell-size, crowded protein systems with seconds, and longer trajectories at atomic resolution, several orders of magnitude longer than those achievable by alternative approaches. In this study, we present a further drastic extension of the modeling capabilities by parallelized implementation of the simulation protocol. The procedure was applied to a panel of Death Fold Domains that form nucleated polymers in human innate immune signaling, recapitulating their homooligomerization tendencies and providing insights into the molecular mechanisms of polymer nucleation. The parallelized protocol allows extension of the simulation trajectories by orders of magnitude beyond the previously reported implementation, reaching into the uncharted territory of atomic resolution simulation of cell-sized systems.

Contribution of DNA breathing to physical interactions with transcription factors

Interaction between transcription factors (TFs) and DNA plays a key role in regulating gene expression. It is generally believed that these interactions are controlled through recognition of DNA core motifs by TFs. Nevertheless, several studies pointed out the limitation of this view, in particular, DNA sequence variants influencing TF binding are often located outside of core motifs. One possible explanation is that the physical properties of DNA may play a role in TF-DNA interactions. Recent studies have supported the importance of DNA shape features, especially in flanking regions of core motifs. Another important physical property of DNA is DNA breathing, the spontaneous opening of double-stranded DNA through thermal motions. But there have been few genomic studies of the role of DNA breathing in TF-DNA interactions. In this work, we analyzed in vitro TF-DNA binding data of three TFs and found that DNA breathing features inside or near core motifs are correlated with binding affinity. This suggests that these TFs may prefer locally and temporally melted DNA formed through breathing. We extended the analysis to 44 TFs with in vivo ChIP-seq binding data. We found that for a large proportion of TFs, their breathing features in or near core motifs are associated with binding, but the sign and magnitude of these associations vary substantially across TF families. Altogether, our study supports the hypothesis that DNA breathing features near binding motifs contribute to TF-DNA interactions.

Accurate prediction of nucleic acid binding proteins using protein language model

Motivation

Nucleic acid binding proteins (NABPs) play critical roles in various and essential biological processes. Many machine learning-based methods have been developed to predict different types of NABPs. However, most of these studies have limited applications in predicting the types of NABPs for any given protein with unknown functions, due to several factors such as dataset construction, prediction scope and features used for training and testing. In addition, single-stranded DNA binding proteins (DBP) (SSBs) have not been extensively investigated for identifying novel SSBs from proteins with unknown functions.

Results

To improve prediction accuracy of different types of NABPs for any given protein, we developed hierarchical and multi-class models with machine learning-based methods and a feature extracted from protein language model ESM2. Our results show that by combining the feature from ESM2 and machine learning methods, we can achieve high prediction accuracy up to 95% for each stage in the hierarchical approach, and 85% for overall prediction accuracy from the multi-class approach. More importantly, besides the much improved prediction of other types of NABPs, the models can be used to accurately predict single-stranded DBPs, which is underexplored.

Availability and implementation

The datasets and code can be found at https://figshare.com/projects/Prediction_of_nucleic_acid_binding_proteins_using_protein_language_model/211555.

DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes

DNAproDB (https://dnaprodb.usc.edu/) is a database, visualization tool, and processing pipeline for analyzing structural features of protein-DNA interactions. Here, we present a substantially updated version of the database through additional structural annotations, search, and user interface functionalities. The update expands the number of pre-analyzed protein-DNA structures, which are automatically updated weekly. The analysis pipeline identifies water-mediated hydrogen bonds that are incorporated into the visualizations of protein-DNA complexes. Tertiary structure-aware nucleotide layouts are now available. New file formats and external database annotations are supported. The website has been redesigned, and interacting with graphs and data is more intuitive. We also present a statistical analysis on the updated collection of structures revealing salient patterns in protein-DNA interactions.

Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences

Computational prediction of nucleic acid-binding residues in protein sequences is an active field of research, with over 80 methods that were released in the past 2 decades. We identify and discuss 87 sequence-based predictors that include dozens of recently published methods that are surveyed for the first time. We overview historical progress and examine multiple practical issues that include availability and impact of predictors, key features of their predictive models, and important aspects related to their training and assessment. We observe that the past decade has brought increased use of deep neural networks and protein language models, which contributed to substantial gains in the predictive performance. We also highlight advancements in vital and challenging issues that include cross-predictions between deoxyribonucleic acid (DNA)-binding and ribonucleic acid (RNA)-binding residues and targeting the two distinct sources of binding annotations, structure-based versus intrinsic disorder-based. The methods trained on the structure-annotated interactions tend to perform poorly on the disorder-annotated binding and vice versa, with only a few methods that target and perform well across both annotation types. The cross-predictions are a significant problem, with some predictors of DNA-binding or RNA-binding residues indiscriminately predicting interactions with both nucleic acid types. Moreover, we show that methods with web servers are cited substantially more than tools without implementation or with no longer working implementations, motivating the development and long-term maintenance of the web servers. We close by discussing future research directions that aim to drive further progress in this area.

Special issue: Multiscale simulations of DNA from electrons to nucleosomes

This editorial for Volume 16, Issue 3 of Biophysical Reviews highlights the three-dimensional structural and dynamic information encoded in DNA sequences and introduces the topics covered in this special issue of the journal on Multiscale Simulations of DNA from Electrons to Nucleosomes. Biophysical Reviews is the official journal of the International Union for Pure and Applied Biophysics (IUPAB 2024). The international scope of the articles in the issue exemplifies the goals of IUPAB to organize worldwide advancements, co-operation, communication, and education in biophysics.