Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.     

alfaNET: A Database of Alfalfa-Bacterial Stem Blight Protein–Protein Interactions Revealing the Molecular Features of the Disease-causing Bacteria

Alfalfa has emerged as one of the most important forage crops, owing to its wide adaptation and high biomass production worldwide. In the last decade, the emergence of bacterial stem blight (caused by Pseudomonas syringae pv. syringae ALF3) in alfalfa has caused around 50% yield losses in the United States. Studies are being conducted to decipher the roles of the key genes and pathways regulating the disease, but due to the sparse knowledge about the infection mechanisms of Pseudomonas, the development of resistant cultivars is hampered. The database alfaNET is an attempt to assist researchers by providing comprehensive Pseudomonas proteome annotations, as well as a host–pathogen interactome tool, which predicts the interactions between host and pathogen based on orthology. alfaNET is a user-friendly and efficient tool and includes other features such as subcellular localization annotations of pathogen proteins, gene ontology (GO) annotations, network visualization, and effector protein prediction. Users can also browse and search the database using particular keywords or proteins with a specific length. Additionally, the BLAST search tool enables the user to perform a homology sequence search against the alfalfa and Pseudomonas proteomes. With the successful implementation of these attributes, alfaNET will be a beneficial resource to the research community engaged in implementing molecular strategies to mitigate the disease. alfaNET is freely available for public use at http://bioinfo.usu.edu/alfanet/.     

Challenges in Discovering Drugs That Target the Protein–Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

A Comparative Molecular Dynamics Study of Selected Point Mutations in the Shwachman–Bodian–Diamond Syndrome Protein SBDS

The Shwachman–Diamond Syndrome (SDS) is an autosomal recessive disease whose majority of patients display mutations in a ribosome assembly protein named Shwachman–Bodian–Diamond Syndrome protein (SBDS). A specific therapy for treating this rare disease is missing, due to the lack of knowledge of the molecular mechanisms responsible for its pathogenesis. Starting from the observation that SBDS single-point mutations, localized in different domains of the proteins, are responsible for an SDS phenotype, we carried out the first comparative Molecular Dynamics simulations on three SBDS mutants, namely R19Q, R126T and I212T. The obtained 450-ns long trajectories were compared with those returned by both the open and closed forms of wild type SBDS and strongly indicated that two distinct conformations (open and closed) are both necessary for the proper SBDS function, in full agreement with recent experimental observations. Our study supports the hypothesis that the SBDS function is governed by an allosteric mechanism involving domains I and III and provides new insights into SDS pathogenesis, thus offering a possible starting point for a specific therapeutic option.     

Residue–Residue Interaction Prediction via Stacked Meta-Learning

Protein–protein interactions (PPIs) are the basis of most biological functions determined by residue–residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta’s performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.     

Huntingtin: A Protein with a Peculiar Solvent Accessible Surface

Taking advantage of the last cryogenic electron microscopy structure of human huntingtin, we explored with computational methods its physicochemical properties, focusing on the solvent accessible surface of the protein and highlighting a quite interesting mix of hydrophobic and hydrophilic patterns, with the prevalence of the latter ones. We then evaluated the probability of exposed residues to be in contact with other proteins, discovering that they tend to cluster in specific regions of the protein. We then found that the remaining portions of the protein surface can contain calcium-binding sites that we propose here as putative mediators for the protein to interact with membranes. Our findings are justified in relation to the present knowledge of huntingtin functional annotation.     

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA–protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, ‘in cell’ hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different ‘in cell’ hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.     

cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein–Protein Interactions

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.     

Applying Protein–Protein Interactions and Complex Networks to Identify Novel Genes in Retinitis Pigmentosa Pathogenesis

Retinitis Pigmentosa (RP) is a hereditary retinal disorder that causes the atrophy of photoreceptor rod cells. Since individual defective genes converge on the same disease, we hypothesized that all causal genes of RP belong in a complex network. To explore this hypothesis, we conducted a gene connection analysis using 161 genes attributed to RP, compiled from the Retinal Information Network, RetNet. We then examined the protein interaction network (PIN) of these genes. In line with our hypothesis, using STRING, we directly connected 149 genes out of the recognized 159 genes. To uncover the association between the PIN and the ten unrecalled genes, we developed an algorithm to pinpoint the best candidate genes to connect the uncalled genes to the PIN and identified ten such genes. We propose that mutations within these ten genes may also cause RP; this notion is supported by analyzing and categorizing the known causal genes based on cellular locations and related functions. The successful establishment of the PIN among all documented genes and the discovery of novel genes for RP strongly suggest an interconnectedness that causes the disease on the molecular level. In addition, our computational gene search protocol can help identify the genes and loci responsible for genetic diseases, not limited to RP.     

Telomerase Interaction Partners–Insight from Plants

Telomerase, an essential enzyme that maintains chromosome ends, is important for genome integrity and organism development. Various hypotheses have been proposed in human, ciliate and yeast systems to explain the coordination of telomerase holoenzyme assembly and the timing of telomerase performance at telomeres during DNA replication or repair. However, a general model is still unclear, especially pathways connecting telomerase with proposed non-telomeric functions. To strengthen our understanding of telomerase function during its intracellular life, we report on interactions of several groups of proteins with the Arabidopsis telomerase protein subunit (AtTERT) and/or a component of telomerase holoenzyme, POT1a protein. Among these are the nucleosome assembly proteins (NAP) and the minichromosome maintenance (MCM) system, which reveal new insights into the telomerase interaction network with links to telomere chromatin assembly and replication. A targeted investigation of 176 candidate proteins demonstrated numerous interactions with nucleolar, transport and ribosomal proteins, as well as molecular chaperones, shedding light on interactions during telomerase biogenesis. We further identified protein domains responsible for binding and analyzed the subcellular localization of these interactions. Moreover, additional interaction networks of NAP proteins and the DOMINO1 protein were identified. Our data support an image of functional telomerase contacts with multiprotein complexes including chromatin remodeling and cell differentiation pathways.     

Extracellular Prion Protein Aggregates in Nine Gerstmann–Sträussler–Scheinker Syndrome Subjects with Mutation P102L: A Micromorphological Study and Comparison with Literature Data

Gerstmann–Sträussler–Scheinker syndrome (GSS) is a hereditary neurodegenerative disease characterized by extracellular aggregations of pathological prion protein (PrP) forming characteristic plaques. Our study aimed to evaluate the micromorphology and protein composition of these plaques in relation to age, disease duration, and co-expression of other pathogenic proteins related to other neurodegenerations. Hippocampal regions of nine clinically, neuropathologically, and genetically confirmed GSS subjects were investigated using immunohistochemistry and multichannel confocal fluorescent microscopy. Most pathognomic prion protein plaques were small (2–10 µm), condensed, globous, and did not contain any of the other investigated proteinaceous components, particularly dystrophic neurites. Equally rare (in two cases out of nine) were plaques over 50 µm having predominantly fibrillar structure and exhibit the presence of dystrophic neuritic structures; in one case, the plaques also included bulbous dystrophic neurites. Co-expression with hyperphosphorylated protein tau protein or amyloid beta-peptide (Aβ) in GSS PrP plaques is generally a rare observation, even in cases with comorbid neuropathology. The dominant picture of the GSS brain is small, condensed plaques, often multicentric, while presence of dystrophic neuritic changes accumulating hyperphosphorylated protein tau or Aβ in the PrP plaques are rare and, thus, their presence probably constitutes a trivial observation without any relationship to GSS development and progression.     

Swine Enteric Coronavirus: Diverse Pathogen–Host Interactions

Swine enteric coronavirus (SeCoV) causes acute gastroenteritis and high mortality in newborn piglets. Since the last century, porcine transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhea virus (PEDV) have swept farms all over the world and caused substantial economic losses. In recent years, porcine delta coronavirus (PDCoV) and swine acute diarrhea syndrome coronavirus (SADS-CoV) have been emerging SeCoVs. Some of them even spread across species, which made the epidemic situation of SeCoV more complex and changeable. Recent studies have begun to reveal the complex SeCoV–host interaction mechanism in detail. This review summarizes the current advances in autophagy, apoptosis, and innate immunity induced by SeCoV infection. These complex interactions may be directly involved in viral replication or the alteration of some signal pathways.     

Nano–Liposomes Double Loaded with Curcumin and Tetrandrine: Preparation,Characterization, Hepatotoxicity and Anti–Tumor Effects

(1) Background: Curcumin (CUR) and tetrandrine (TET) are natural compounds with various bioactivities, but have problems with low solubility, stability, and absorption rate, resulting in low bioavailability, and limited applications in food, medicine, and other fields. It is very important to improve the solubility while maintaining the high activity of drugs. Liposomes are micro–vesicles synthesized from cholesterol and lecithin. With high biocompatibility and biodegradability, liposomes can significantly improve drug solubility, efficacy, and bioavailability. (2) Methods: In this work, CUR and TET were encapsulated with nano–liposomes and g DSPE–MPEG 2000 (DP)was added as a stabilizer to achieve better physicochemical properties, biosafety, and anti–tumor effects. (3) Results: The nano–liposome (CT–DP–Lip) showed stable particle size (under 100 nm) under different conditions, high solubility, drug encapsulation efficiency (EE), loading capacity (LC), release rate in vitro, and stability. In addition, in vivo studies demonstrated CT–DP–Lip had no significant toxicity on zebrafish. Tumor cytotoxicity test showed that CT–DP–Lip had a strong inhibitory effect on a variety of cancer cells. (4) Conclusions: This work showed that nano–liposomes can significantly improve the physical and chemical properties of CUR and TET and make them safer and more efficient.     

Role of the Renin–Angiotensin–Aldosterone and Kinin–Kallikrein Systems in the Cardiovascular Complications of COVID-19 and Long COVID

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the virus responsible for the COVID-19 pandemic. Patients may present as asymptomatic or demonstrate mild to severe and life-threatening symptoms. Although COVID-19 has a respiratory focus, there are major cardiovascular complications (CVCs) associated with infection. The reported CVCs include myocarditis, heart failure, arrhythmias, thromboembolism and blood pressure abnormalities. These occur, in part, because of dysregulation of the Renin–Angiotensin–Aldosterone System (RAAS) and Kinin–Kallikrein System (KKS). A major route by which SARS-CoV-2 gains cellular entry is via the docking of the viral spike (S) protein to the membrane-bound angiotensin converting enzyme 2 (ACE2). The roles of ACE2 within the cardiovascular and immune systems are vital to ensure homeostasis. The key routes for the development of CVCs and the recently described long COVID have been hypothesised as the direct consequences of the viral S protein/ACE2 axis, downregulation of ACE2 and the resulting damage inflicted by the immune response. Here, we review the impact of COVID-19 on the cardiovascular system, the mechanisms by which dysregulation of the RAAS and KKS can occur following virus infection and the future implications for pharmacological therapies.     

Functional Roles of PARP2 in Assembling Protein–Protein Complexes Involved in Base Excision DNA Repair

Poly(ADP-ribose) polymerase 2 (PARP2) participates in base excision repair (BER) alongside PARP1, but its functions are still under study. Here, we characterize binding affinities of PARP2 for other BER proteins (PARP1, APE1, Polβ, and XRCC1) and oligomerization states of the homo- and hetero-associated complexes using fluorescence-based and light scattering techniques. To compare PARP2 and PARP1 in the efficiency of PAR synthesis, in the absence and presence of protein partners, the size of PARP2 PARylated in various reaction conditions was measured. Unlike PARP1, PARP2 forms more dynamic complexes with common protein partners, and their stability is effectively modulated by DNA intermediates. Apparent binding affinity constants determined for homo- and hetero-oligomerized PARP1 and PARP2 provide evidence that the major form of PARP2 at excessive PARP1 level is their heterocomplex. Autoregulation of PAR elongation at high PARP and NAD⁺ concentrations is stronger for PARP2 than for PARP1, and the activity of PARP2 is more effectively inhibited by XRCC1. Moreover, the activity of both PARP1 and PARP2 is suppressed upon their heteroPARylation. Taken together, our findings suggest that PARP2 can function differently in BER, promoting XRCC1-dependent repair (similarly to PARP1) or an alternative XRCC1-independent mechanism via hetero-oligomerization with PARP1.