Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Protein-protein interaction and non-interaction predictions using gene sequence natural vector

Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein–protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

Protein-protein non-interactions and interactions are distinguished and predicted by gene sequence using single nucleotide and contiguous nucleotides combined with machine learning models.

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.

iBRET Screen of the ABCD1 Peroxisomal Network and Mutation-Induced Network Perturbations

Mapping the network of proteins provides a powerful means to investigate the function of disease genes and to unravel the molecular basis of phenotypes. We present an automated informatics-aided and bioluminescence resonance energy transfer-based approach (iBRET) enabling high-confidence detection of protein-protein interactions in living mammalian cells. A screen of the ABCD1 protein, which is affected in X-linked adrenoleukodystrophy (X-ALD), against an organelle library of peroxisomal proteins demonstrated applicability of iBRET for large-scale experiments. We identified novel protein-protein interactions for ABCD1 (with ALDH3A2, DAO, ECI2, FAR1, PEX10, PEX13, PEX5, PXMP2, and PIPOX), mapped its position within the peroxisomal protein-protein interaction network, and determined that pathogenic missense variants in ABCD1 alter the interaction with selected binding partners. These findings provide mechanistic insights into pathophysiology of X-ALD and may foster the identification of new disease modifiers.

Photo-affinity pulling down of low-affinity binding proteins mediated by post-translational modifications

Weak and transient protein-protein interactions (PPIs) mediated by the post-translational modifications (PTMs) play key roles in biological systems. However, technical challenges to investigate the PTM-mediated PPIs have impeded many research advances. In this work, we develop a photo-affinity pull-down assay method to pull-down low-affinity binding proteins, thus for the screen of PTM-mediated PPIs. In this method, the PTM-mediated non-covalent interactions can be converted to the covalent interactions by the photo-activated linkage, so as to freeze frame the low-affinity binding interactions. The fabricated photo-affinity magnetic beads (PAMBs) ensure high specificity and resolution to capture the interacted proteins. Besides, the introduction of PEG passivation layer on PAMB has significantly reduced the non-specific interaction as compared to the traditional pull-down assay. For proof-of-concept, by using this newly developed assay method, we have identified a set of proteins that can interact with a specific methylation site on Flap Endonuclease 1 (FEN1) protein. Less interfering proteins (decreased over 80%) and more proteins sub-classes are profiled as compared to the traditional biotin-avidin pull-down system. Therefore, this new pull-down method may provide a useful tool for the study of low-affinity PPIs, and contribute to the discovery of potential targets for renewed PTM-mediated interactions that is fundamentally needed in biomedical research.