LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Identification of potential molecular mimicry in pathogen-host interactions

Pathogens have evolved sophisticated strategies to manipulate host signaling pathways, including the phenomenon of molecular mimicry, where pathogen-derived biomolecules imitate host biomolecules. In this study, we resurrected, updated, and optimized a sequence-based bioinformatics pipeline to identify potential molecular mimicry candidates between humans and 32 pathogenic species whose proteomes' 3D structure predictions were available at the start of this study. We observed considerable variation in the number of mimicry candidates across pathogenic species, with pathogenic bacteria exhibiting fewer candidates compared to fungi and protozoans. Further analysis revealed that the candidate mimicry regions were enriched in solvent-accessible regions, highlighting their potential functional relevance. We identified a total of 1,878 mimicked regions in 1,439 human proteins, and clustering analysis indicated diverse target proteins across pathogen species. The human proteins containing mimicked regions revealed significant associations between these proteins and various biological processes, with an emphasis on host extracellular matrix organization and cytoskeletal processes. However, immune-related proteins were underrepresented as targets of mimicry. Our findings provide insights into the broad range of host-pathogen interactions mediated by molecular mimicry and highlight potential targets for further investigation. This comprehensive analysis contributes to our understanding of the complex mechanisms employed by pathogens to subvert host defenses and we provide a resource to assist researchers in the development of novel therapeutic strategies.

Identification of all-against-all protein-protein interactions based on deep hash learning

BACKGROUND

Protein-protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

RESULTS

In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database with M proteins can be transformed into a much more simpler problem: to find a number inside a sorted array of length M. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.

CONCLUSIONS

The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from [Formula: see text] to [Formula: see text] for performing an all-against-all PPI prediction for a database with M proteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Early Metazoan Origin and Multiple Losses of a Novel Clade of RIM Presynaptic Calcium Channel Scaffolding Protein Homologs

The precise localization of Ca_V2 voltage-gated calcium channels at the synapse active zone requires various interacting proteins, of which, Rab3-interacting molecule or RIM is considered particularly important. In vertebrates, RIM interacts with Ca_V2 channels in vitro via a PDZ domain that binds to the extreme C-termini of the channels at acidic ligand motifs of D/E-D/E/H-WC-_COOH, and knockout of RIM in vertebrates and invertebrates disrupts Ca_V2 channel synaptic localization and synapse function. Here, we describe a previously uncharacterized clade of RIM proteins bearing domain architectures homologous to those of known RIM homologs, but with some notable differences including key amino acids associated with PDZ domain ligand specificity. This novel RIM emerged near the stem lineage of metazoans and underwent extensive losses, but is retained in select animals including the early-diverging placozoan Trichoplax adhaerens, and molluscs. RNA expression and localization studies in Trichoplax and the mollusc snail Lymnaea stagnalis indicate differential regional/tissue type expression, but overlapping expression in single isolated neurons from Lymnaea. Ctenophores, the most early-diverging animals with synapses, are unique among animals with nervous systems in that they lack the canonical RIM, bearing only the newly identified homolog. Through phylogenetic analysis, we find that Ca_V2 channel D/E-D/E/H-WC-_COOH like PDZ ligand motifs were present in the common ancestor of cnidarians and bilaterians, and delineate some deeply conserved C-terminal structures that distinguish Ca_V1 from Ca_V2 channels, and Ca_V1/Ca_V2 from Ca_V3 channels.

Molecular-based detection, genetic characterization and phylogenetic analysis of porcine circovirus 4 from Korean domestic swine farms

Porcine circovirus 4 (PCV4), a novel and unclassified member of the genus Circovirus, was first reported in China in 2019. Aiming to provide more evidence about the active circulation of PCV4, this study screened 335 pooled internal organs and detected the virus (i) at a rate of 3.28%, (ii) from both clinically healthy and clinically sick pigs of various age groups, and (iii) in six out of nine provinces of Korea. The complete genomic sequence of the Korean PCV4 strain (E115) was 1,770 nucleotides in length and had 98.5%-98.9% identity to three PCV4 strains currently available at GenBank. Utilizing a set of bioinformatic programs, it was revealed that the Korean PCV4 strain contained several genomic features of (i) a palindrome stem-loop structure with a conserved nonanucleotide, (ii) packed overlapping ORFs oriented in different directions and (iii) two intergenic regions in between genes encoding the putative replication-associated protein (Rep) and capsid (Cap) proteins. This study also predicted the presence of essential elements for the replication of circoviruses in all PCV4 strains, for example the origin of DNA replication, endonuclease and helicase domains of Rep, and the nuclear localization signal on the putative Cap protein. Finally, based on the phylogeny inferred from sequences of the putative Rep protein, this study further clarified the genetic relationships between PCV4 and other CRESS DNA viruses in general and circoviruses in particular.

Structural Cues for Understanding eEF1A2 Moonlighting

Spontaneous mutations in the EEF1A2 gene cause epilepsy and severe neurological disabilities in children. The crystal structure of eEF1A2 protein purified from rabbit skeletal muscle reveals a post-translationally modified dimer that provides information about the sites of interaction with numerous binding partners, including itself, and maps these mutations onto the dimer and tetramer interfaces. The spatial locations of the side chain carboxylates of Glu301 and Glu374, to which phosphatidylethanolamine is uniquely attached via an amide bond, define the anchoring points of eEF1A2 to cellular membranes and interorganellar membrane contact sites. Additional bioinformatic and molecular modeling results provide novel structural insight into the demonstrated binding of eEF1A2 to SH3 domains, the common MAPK docking groove, filamentous actin, and phosphatidylinositol-4 kinase IIIβ. In this new light, the role of eEF1A2 as an ancient, multifaceted, and articulated G protein at the crossroads of autophagy, oncogenesis and viral replication appears very distant from the "canonical" one of delivering aminoacyl-tRNAs to the ribosome that has dominated the scene and much of the thinking for many decades.

Rational design of affinity ligands for bioseparation

Affinity adsorbents have been the cornerstone in protein purification. The selective nature of the molecular recognition interactions established between an affinity ligands and its target provide the basis for efficient capture and isolation of proteins. The plethora of affinity adsorbents available in the market reflects the importance of affinity chromatography in the bioseparation industry. Ligand discovery relies on the implementation of rational design techniques, which provides the foundation for the engineering of novel affinity ligands. The main goal for the design of affinity ligands is to discover or improve functionality, such as increased stability or selectivity. However, the methodologies must adapt to the current needs, namely to the number and diversity of biologicals being developed, and the availability of new tools for big data analysis and artificial intelligence. In this review, we offer an overview on the development of affinity ligands for bioseparation, including the evolution of rational design techniques, dating back to the years of early discovery up to the current and future trends in the field.

MotifAnalyzer-PDZ: A computational program to investigate the evolution of PDZ-binding target specificity

Recognition of short linear motifs (SLiMs) or peptides by proteins is an important component of many cellular processes. However, due to limited and degenerate binding motifs, prediction of cellular targets is challenging. In addition, many of these interactions are transient and of relatively low affinity. Here, we focus on one of the largest families of SLiM-binding domains in the human proteome, the PDZ domain. These domains bind the extreme C-terminus of target proteins, and are involved in many signaling and trafficking pathways. To predict endogenous targets of PDZ domains, we developed MotifAnalyzer-PDZ, a program that filters and compares all motif-satisfying sequences in any publicly available proteome. This approach enables us to determine possible PDZ binding targets in humans and other organisms. Using this program, we predicted and biochemically tested novel human PDZ targets by looking for strong sequence conservation in evolution. We also identified three C-terminal sequences in choanoflagellates that bind a choanoflagellate PDZ domain, the Monsiga brevicollis SHANK1 PDZ domain (mbSHANK1), with endogenously-relevant affinities, despite a lack of conservation with the targets of a homologous human PDZ domain, SHANK1. All three are predicted to be signaling proteins, with strong sequence homology to cytosolic and receptor tyrosine kinases. Finally, we analyzed and compared the positional amino acid enrichments in PDZ motif-satisfying sequences from over a dozen organisms. Overall, MotifAnalyzer-PDZ is a versatile program to investigate potential PDZ interactions. This proof-of-concept work is poised to enable similar types of analyses for other SLiM-binding domains (e.g., MotifAnalyzer-Kinase). MotifAnalyzer-PDZ is available at http://motifAnalyzerPDZ.cs.wwu.edu.

Machine-learning techniques for the prediction of protein-protein interactions

Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.