An iterative knowledge-based scoring function for protein-protein recognition

An immunoinformatics and extended molecular dynamics approach for designing a polyvalent vaccine against multiple strains of Human T-lymphotropic virus (HTLV)

Human T-lymphotropic virus (HTLV), a group of retroviruses belonging to the oncovirus family, has long been associated with various inflammatory and immunosuppressive disorders. At present, there is no approved vaccine capable of effectively combating all the highly pathogenic strains of HTLV that makes this group of viruses a potential threat to human health. To combat the devastating impact of any potential future outbreak caused by this virus group, our study employed a reverse vaccinology approach to design a novel polyvalent vaccine targeting the highly virulent subtypes of HTLV. Moreover, we comprehensively analyzed the molecular interactions between the designed vaccine and corresponding Toll-like receptors (TLRs), providing valuable insights for future research on preventing and managing HTLV-related diseases and any possible outbreaks. The vaccine was designed by focusing on the envelope glycoprotein gp62, a crucial protein involved in the infectious process and immune mechanisms of HTLV inside the human body. Epitope mapping identified T cell and B cell epitopes with low binding energies, ensuring their immunogenicity and safety. Linkers and adjuvants were incorporated to enhance the vaccine's stability, antigenicity, and immunogenicity. Initially, two vaccine constructs were formulated, and among them, vaccine construct-2 exhibited superior solubility and structural stability. Molecular docking analyses also revealed strong binding affinity between the vaccine construct-2 and both targeted TLR2 and TLR4. Molecular dynamics simulations demonstrated enhanced stability, compactness, and consistent hydrogen bonding within TLR-vaccine complexes, suggesting a strong binding affinity. The stability of the complexes was further corroborated by contact, free energy, structure, and MM-PBSA analyses. Consequently, our research proposes a vaccine targeting multiple HTLV subtypes, offering valuable insights into the molecular interactions between the vaccine and TLRs. These findings should contribute to developing effective preventive and treatment approaches against HTLV-related diseases and preventing possible outbreaks. However, future research should focus on in-depth validation through experimental studies to confirm the interactions identified in silico and to evaluate the vaccine's efficacy in relevant animal models and, eventually, in clinical trials.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Biocomputational characterisation of MBO_200107 protein of Mycobacterium tuberculosis variant caprae: a molecular docking and simulation study

The principal objective of this study was to delineate the potentiality of the MBO_200107 protein from the Mycobacterium tuberculosis variant caprae in cancer research. It is a cytoplasmic protein, comprised of a 354-long amino acid chain, alkaline, had a molecular weight of 39089.37 Da, an isoelectric point of 9.62 and a grand average of hydropathicity of -0.345. One of the functional domains was predicted as Gammaglutamylcyclotransferase (GGCT). Among tertiary structures, the Modeller and Phyre2 model satisfied all the quality parameters, though they are truncated; contrarily, the I-TASSER model is full length and contains the sequence for the GGCT domain, though it did not meet all the quality parameters. It also has significant sequence similarities (47.5% by EMBOSS Water and 72.4% by EMBOSS Matcher) with a human GGCT, and the conserved sequences are confined to the GGCT domain of the MBO_200107. According to molecular docking analyses, the protein has a binding affinity of -4.8 kcal/mol by Autodock Vina and -56.465 kcal/mol by HPEPDOCK to the human glutathione (GSH), an essential metabolite for GGCT metabolism. The Molecular dynamic simulation of the docked complex showed the binding efficiency of the GSH to MBO_200107 with a minimal structural alteration. The in silico findings mentioned above revealed that the protein could be used as a supplementary tool in cancer research, such as designing vaccines or drugs where the role of GGCT has been implicated. Further, we recommend fully characterising the protein and conducting essential in vitro and in vivo experiments to determine its detailed usefulness.Communicated by Ramaswamy H. Sarma.

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network

Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the protein. However, there is still a challenge to selecting the near-native decoys generated by protein-protein docking. Here, we propose a docking evaluation method using 3D point cloud neural network named PointDE. PointDE transforms protein structure to the point cloud. Using the state-of-the-art point cloud network architecture and a novel grouping mechanism, PointDE can capture the geometries of the point cloud and learn the interaction information from the protein interface. On public datasets, PointDE surpasses the state-of-the-art method using deep learning. To further explore the ability of our method in different types of protein structures, we developed a new dataset generated by high-quality antibody-antigen complexes. The result in this antibody-antigen dataset shows the strong performance of PointDE, which will be helpful for the understanding of PPI mechanisms.

Tryptophan-PNA gc Conjugates Self-Assemble to Form Fibers

Peptide nucleic acids and their conjugates to peptides can self-assemble and generate complex architectures. In this work, we explored the self-assembly of PNA dimers conjugated to the dipeptide WW. Our studies suggest that the indole ring of tryptophan promotes aggregation of the conjugates. The onset of fluorescence is observed upon self-assembly. The structure of self-assembled WWgc is concentration-dependent, being spherical at low concentrations and fibrous at high concentrations. As suggested by molecular modeling studies, fibers are stabilized by stacking interactions between tryptophans and Watson-Crick hydrogen bonds between nucleobases.

Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.

The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions

Computational docking is an instrumental method of the structural biology toolbox. Specifically, integrative modeling software, such as LightDock, arise as complementary and synergetic methods to experimental structural biology techniques. Ubiquitousness and accessibility are fundamental features to promote ease of use and to improve user experience. With this goal in mind, we have developed the LightDock Server, a web server for the integrative modeling of macromolecular interactions, along with several dedicated usage modes. The server builds upon the LightDock macromolecular docking framework, which has proved useful for modeling medium-to-high flexible complexes, antibody-antigen interactions, or membrane-associated protein assemblies. We believe that this free-to-use resource will be a valuable addition to the structural biology community and can be accessed online at: https://server.lightdock.org/.

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

MOTIVATION

Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery.

RESULTS

In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures.

AVAILABILITY AND IMPLEMENTATION

The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

A computational and laboratory approach for the investigation of interactions of peptide conjugated natural terpenes with EpHA2 receptor

CONTEXT

Ephrin type A receptor 2 (EphA2) is a well-known drug target for cancer treatment due to its overexpression in numerous types of cancers. Thus, it is crucial to determine the binding interactions of this receptor with both the ligand-binding domain (LBD) and the kinase-binding domain (KBD) through a targeted approach in order to modulate its activity. In this work, natural terpenes with inherent anticancer properties were conjugated with short peptides YSAYP and SWLAY that are known to bind to the LBD of EphA2 receptor. We examined the binding interactions of six terpenes (maslinic acid, levopimaric acid, quinopimaric acid, oleanolic, polyalthic, and hydroxybetulinic acid) conjugated to the above peptides with the ligand-binding domain (LBD) of EphA2 receptor computationally. Additionally, following the "target-hopping approach," we also examined the interactions of the conjugates with the KBD. Our results indicated that most of the conjugates showed higher binding interactions with the EphA2 kinase domain compared to LBD. Furthermore, the binding affinities of the terpenes increased upon conjugating the peptides with the terpenes. In order to further investigate the specificity toward EphA2 kinase domain, we also examined the binding interactions of the terpenes conjugated to VPWXE (x = norleucine), as VPWXE has been shown to bind to other RTKs. Our results indicated that the terpenes conjugated to SWLAY in particular showed high efficacy toward binding to the KBD. We also designed conjugates where in the peptide portion and the terpenes were separated by a butyl (C4) group linker to examine if the binding interactions could be enhanced. Docking studies showed that the conjugates with linkers had enhanced binding with the LBD compared to those without linkers, though binding remained slightly higher without linkers toward the KBD. As a proof of concept, maslinate and oleanolate conjugates of each of the peptides were then tested with F98 tumor cells which are known to overexpress EphA2 receptor. Results indicated that the oleanolate-amido-SWLAY conjugates were efficacious in reducing the cell proliferation of the tumor cells and may be potentially developed and further studied for targeting tumor cells overexpressing the EphA2 receptor. To test if these conjugates could bind to the receptor and potentially function as kinase inhibitors, we conducted SPR analysis and ADP-Glo assay. Our results indicated that OA conjugate with SWLAY showed the highest inhibition.

METHODS

Docking studies were carried out using AutoDock Vina, v.1.2.0; Molecular Dynamics and MMGBSA calculations were carried out through Schrodinger Software DESMOND.

Isolation and Characterization of scFv Antibody against Internal Ribosomal Entry Site of Enterovirus A71

Enterovirus A71 (EV-A71) is one of the causative agents of hand-foot-mouth disease, which can be associated with neurocomplications of the central nervous system. A limited understanding of the virus's biology and pathogenesis has led to the unavailability of effective anti-viral treatments. The EV-A71 RNA genome carries type I internal ribosomal entry site (IRES) at 5' UTR that plays an essential role in the viral genomic translation. However, the detailed mechanism of IRES-mediated translation has not been elucidated. In this study, sequence analysis revealed that the domains IV, V, and VI of EV-A71 IRES contained the structurally conserved regions. The selected region was transcribed in vitro and labeled with biotin to use as an antigen for selecting the single-chain variable fragment (scFv) antibody from the naïve phage display library. The so-obtained scFv, namely, scFv #16-3, binds specifically to EV-A71 IRES. The molecular docking showed that the interaction between scFv #16-3 and EV-A71 IRES was mediated by the preferences of amino acid residues, including serine, tyrosine, glycine, lysine, and arginine on the antigen-binding sites contacted the nucleotides on the IRES domains IV and V. The so-produced scFv has the potential to develop as a structural biology tool to study the biology of the EV-A71 RNA genome.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

Motivation

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations.

Results

Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE.

Availability and implementation

An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Human IL-32θA94V mutant attenuates monocyte-endothelial adhesion by suppressing the expression of ICAM-1 and VCAM-1 via binding to cell surface receptor integrin αVβ3 and αVβ6 in TNF-α-stimulated HUVECs

Interleukin-32 (IL-32), first reported in 2005, and its isoforms have been the subject of numerous studies investigating their functions in virus infection, cancer, and inflammation. IL-32θ, one of the IL-32 isoforms, has been shown to modulate cancer development and inflammatory responses. A recent study identified an IL-32θ mutant with a cytosine to thymine replacement at position 281 in breast cancer tissues. It means that alanine was also replaced to valine at position 94 in amino acid sequence (A94V). In this study, we investigated the cell surface receptors of IL-32θA94V and evaluated their effect on human umbilical vein endothelial cells (HUVECs). Recombinant human IL-32θA94V was expressed, isolated, and purified using Ni-NTA and IL-32 mAb (KU32-52)-coupled agarose columns. We observed that IL-32θA94V could bind to the integrins αVβ3 and αVβ6, suggesting that integrins act as cell surface receptors for IL-32θA94V. IL-32θA94V significantly attenuated monocyte-endothelial adhesion by inhibiting the expression of Intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) in tumor necrosis factor (TNF)-α-stimulated HUVECs. IL-32θA94V also reduced the TNF-α-induced phosphorylation of protein kinase B (AKT) and c-jun N-terminal kinases (JNK) by inhibiting phosphorylation of focal adhesion kinase (FAK). Additionally, IL-32θA94V regulated the nuclear translocation of nuclear factor kappa B (NF-κB) and activator protein 1 (AP-1), which are involved in ICAM-1 and VCAM-1 expression. Monocyte-endothelial adhesion mediated by ICAM-1 and VCAM-1 is an important early step in atherosclerosis, which is a major cause of cardiovascular disease. Our findings suggest that IL-32θA94V binds to the cell surface receptors, integrins αVβ3 and αVβ6, and attenuates monocyte-endothelial adhesion by suppressing the expression of ICAM-1 and VCAM-1 in TNF-α-stimulated HUVECs. These results demonstrate that IL-32θA94V can act as an anti-inflammatory cytokine in a chronic inflammatory disease such as atherosclerosis.

AcrNET: predicting anti-CRISPR with deep learning

MOTIVATION

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.

RESULTS

On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.

AVAILABILITY AND IMPLEMENTATION

Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.

A novel variant in the FBP1 gene causes fructose-1,6-bisphosphatase deficiency through increased ubiquitination

Fructose-1,6-bisphosphatase (FBPase) deficiency is an autosomal recessive disorder characterized by impaired gluconeogenesis caused by mutations in the fructose-1,6-bisphosphatase 1 (FBP1) gene. The molecular mechanisms underlying FBPase deficiency caused by FBP1 mutations require investigation. Herein, we report the case of a Chinese boy with FBPase deficiency who presented with hypoglycemia, ketonuria, metabolic acidosis, and repeated episodes of generalized seizures that progressed to epileptic encephalopathy. Whole-exome sequencing revealed compound heterozygous variants, c.761A  >  G (H254R) and c.962C  >  T (S321F), in FBP1. The variants, especially the novel H254R, reduced protein stability and enzymatic activity in patient-derived leukocytes and transfected HepG2 and U251 cells. Mutant FBP1 undergoes enhanced ubiquitination and proteasomal degradation. NEDD4-2 was identified as an E3 ligase for FBP1 ubiquitination in transfected cells and the liver and brain of Nedd4-2 knockout mice. The H254R mutant FBP1 interacted with NEDD4-2 at significantly higher levels than the wild-type control. Our study identified a novel H254R variant of FBP1 underlying FBPase deficiency and elucidated the molecular mechanism underlying the enhanced NEDD4-2-mediated ubiquitination and proteasomal degradation of mutant FBP1.

Dual-Mechanism Glycolipidpeptide with High Antimicrobial Activity, Immunomodulatory Activity, and Potential Application for Combined Antibacterial Therapy

Bacterial drug resistance is becoming increasingly serious, and it is urgent to develop effective antibacterial drugs. Antimicrobial peptides (AMPs), as potential candidates against bacteria, have a broad prospect for development. Herein, a series of AMPs with biological characteristics (net positive charge, amphiphilicity, and α-helix), an AXA motif recognized by membrane bound serine protease type I signal peptidases (SPase I), an FLPII motif to reduce hemolysis, and a monosaccharide motif to improve the stability and activity were designed and synthesized, and among which, the glycolipidpeptide GLP6 (glycosylated LP6 lipopeptide) had excellent antibacterial and immunomodulatory activity, good stability and biocompatibility, and excellent biofilm eradication and membrane penetrating activity. The positively charged spherical aggregates formed by self-assembly of GLP6 could encapsulate tetracycline (TC) to form GLP6@TC with a sustained-release effect, which could enhance the sensitivity of bacteria to the antibiotic and realize combined sterilization. The results of acute peritonitis and bacterial keratitis showed that GLP6@TC had a good combined antibacterial effect and the ability to inhibit interleukin-2 (IL-2), which could significantly reduce the inflammatory response while treating bacterial infection, and it had great potential for application. The results of computer molecular docking showed the AXA motif could effectively bind to SPase I, which was consistent with the results of biological experiments. In general, the study could provide a perspective for the design of AMPs and combined antibacterial therapy.

Vaccinomics Approach for Multi-Epitope Vaccine Design against Group A Rotavirus Using VP4 and VP7 Proteins

Rotavirus A is the most common cause of Acute Gastroenteritis globally among children <5 years of age. Due to a segmented genome, there is a high frequency of genetic reassortment and interspecies transmission which has resulted in the emergence of novel genotypes. There are concerns that monovalent (Rotarix: GlaxoSmithKline Biologicals, Rixensart, Belgium) and pentavalent (RotaTeq: MERCK & Co., Inc., Kenilworth, NJ, USA) vaccines may be less effective against non-vaccine strains, which clearly shows the demand for the design of a vaccine that is equally effective against all circulating genotypes. In the present study, a multivalent vaccine was designed from VP4 and VP7 proteins of RVA. Epitopes were screened for antigenicity, allergenicity, homology with humans and anti-inflammatory properties. The vaccine contains four B-cell, three CTL and three HTL epitopes joined via linkers and an N-terminal RGD motif adjuvant. The 3D structure was predicted and refined preceding its docking with integrin. Immune simulation displayed promising results both in Asia and worldwide. In the MD simulation, the RMSD value varied from 0.2 to 1.6 nm while the minimum integrin amino acid fluctuation (0.05-0.1 nm) was observed with its respective ligand. Codon optimization was performed with an adenovirus vector in a mammalian expression system. The population coverage analysis showed 99.0% and 98.47% in South Asia and worldwide, respectively. These computational findings show potential against all RVA genotypes; however, in-vitro/in-vivo screening is essential to devise a meticulous conclusion.

Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.

Triterpene Derivatives as Potential Inhibitors of the RBD Spike Protein from SARS-CoV-2: An In Silico Approach

The appearance of a new coronavirus, SARS-CoV-2, in 2019 kicked off an international public health emergency. Although rapid progress in vaccination has reduced the number of deaths, the development of alternative treatments to overcome the disease is still necessary. It is known that the infection begins with the interaction of the spike glycoprotein (at the virus surface) and the angiotensin-converting enzyme 2 cell receptor (ACE2). Therefore, a straightforward solution for promoting virus inhibition seems to be the search for molecules capable of abolishing such attachment. In this work, we tested 18 triterpene derivatives as potential inhibitors of SARS-CoV-2 against the receptor-binding domain (RBD) of the spike protein by means of molecular docking and molecular dynamics simulations, modeling the RBD S1 subunit from the X-ray structure of the RBD-ACE2 complex (PDB ID: 6M0J). Molecular docking revealed that at least three triterpene derivatives of each type (i.e., oleanolic, moronic and ursolic) present similar interaction energies as the reference molecule, i.e., glycyrrhizic acid. Molecular dynamics suggest that two compounds from oleanolic and ursolic acid, OA5 and UA2, can induce conformational changes capable of disrupting the RBD-ACE2 interaction. Finally, physicochemical and pharmacokinetic properties simulations revealed favorable biological activity as antivirals.

Development of KLA-RGD integrated lipopeptide with the effect of penetrating membrane which target the αvβ3 receptor and the application of combined antitumor

Herein, an amphiphilic cationic anticancer lipopeptide P17 with α-helical structure was synthesized based on the integration of KLA and RGD peptide which could bind with the receptor of integrin α_vβ₃. P17 could self assemble into stable spherical aggregates in aqueous solution, and which could encapsulate the anticancer drugs (Such as Dox) to form P17 @ Anticancer drug nanomedicine (P17 @ Dox nanomedicine) which could play the combined therapy of P17 and anticancer drugs (Dox). The encapsulation efficiency of P17 aggregates to Dox was 80.4 ± 3.2 %, and the release behavior of P17 @ Dox nanomedicine in vitro had the characteristics of slow-release and pH responsiveness. The experiments in vitro showed that P17 lipopeptide had low cytotoxicity, high serum stability, low hemolysis and strong penetrating membrane ability. The release of Dox from P17 @ Dox in cells was time-dependment, and the P17 @ Dox nanomedicine had a good anticancer effect. The experiments in vivo showed that P17 and P17 @ Dox nanomedicine both had low hemolysis, and P17 @ Dox nanomedicine could effectively inhibit tumor growth and significantly reduce the toxic and side effects of Dox. Molecular docking experiments showed that P17 could effectively interact with the receptor of integrin α_vβ₃. In conclusion, P17 lipopeptide could be used as an excellent drug carrier and play the combined anticancer effect of P17 and anticancer drugs.

Structural evolution of an amphibian-specific globin: A computational evolutionary biochemistry approach

Studies on the globin family are continuously revealing insights into the mechanisms of gene and protein evolution. The rise of a new globin gene type in Pelobatoidea and Neobatrachia (Amphibia:Anura) from an α-globin precursor provides the opportunity to investigate the genetic and physical mechanisms underlying the origin of new protein structural and functional properties. This amphibian-specific globin (globin A/GbA) discovered in the heart of Rana catesbeiana is a monomer. As the ancestral oligomeric state of α-globins is a homodimer, we inferred that the ancestral state was lost somewhere in the GbA lineage. Here, we combined computational molecular evolution with structural bioinformatics to determine the extent to which the loss of the homodimeric state is pervasive in the GbA clade. We also characterized the loci of GbA genes in Bufo bufo. We found two GbA clades in Neobatrachia. One was deleted in Ranidae, but retained and expanded to yield a new globin cluster in Bufonidae species. Loss of the ancestral oligomeric state seems to be pervasive in the GbA clade. However, a taxonomic sampling that includes more Pelobatoidea, as well as early Neobatrachia, lineages would be necessary to determine the oligomeric state of the last common ancestor of all GbA. The evidence presented here points out a possible loss of oligomerization in Pelobatoidea GbA as a result of amino acid substitutions that weaken the homodimeric state. In contrast, the loss of oligomerization in both Neobatrachia GbA clades was linked to independent deletions that disrupted many packing contacts at the homodimer interface.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE .

G-RANK: an equivariant graph neural network for the scoring of protein-protein docking models

Motivation

Protein complex structure prediction is important for many applications in bioengineering. A widely used method for predicting the structure of protein complexes is computational docking. Although many tools for scoring protein-protein docking models have been developed, it is still a challenge to accurately identify near-native models for unknown protein complexes. A recently proposed model called the geometric vector perceptron-graph neural network (GVP-GNN), a subtype of equivariant graph neural networks, has demonstrated success in various 3D molecular structure modeling tasks.

Results

Herein, we present G-RANK, a GVP-GNN-based method for the scoring of protein-protein docking models. When evaluated on two different test datasets, G-RANK achieved a performance competitive with or better than the state-of-the-art scoring functions. We expect G-RANK to be a useful tool for various applications in biological engineering.

Availability and implementation

Source code is available at https://github.com/ha01994/grank.

Contact

kds@kaist.ac.kr.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Bioinformatics evaluation of anticancer properties of GP63 protein-derived peptides on MMP2 protein of melanoma cancer

Background

GP63, also known as Leishmanolysin, is a multifunctional virulence factor abundant on the surface of Leishmania spp. small peptides with anticancer capabilities that are selective and toxic to cancer cells are known as anticancer peptides. We aimed to demonstrate the activity of GP63 and its anticancer properties on melanoma using a range of in silico tools and screening methods to identify predicted and designed anticancer peptides.

Methods

Various in silico modeling methodologies are used to establish the three-dimensional (3D) structure of GP63. Refinement and re-evaluation of the modeled structures and the built models' quality evaluated using the different docking used to find the interacting amino acids between MMP2 and GP63 and its anticancer peptides. AntiCP2.0 is used for screening anticancer peptides. 2D interaction plots of protein-ligand complexes evaluated by Protein-Ligand Interaction Profiler server. It is for the first time that used anticancer peptides of GP63 and the predicted and designed peptides.

Results

We used 3 peptides of GP63 based on the AntiCP 2.0 server with scores of 0.63, 0.53, and 0.49, and common peptides of GP63/MMP2 (continues peptide: mean the completely selected peptide after docking with non-anticancer effect, predicted with 0.58 score and designed peptides with 0.47 and 0.45 scores by AntiCP 2.0 server).

Conclusions

The antileishmanial and anticancer peptide research topics exemplify the multidisciplinary nature of peptide research. The advancement of therapeutics targeting cancer and/or Leishmania requires an interconnected research strategy shown in this work.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

New N4-Donor Ligands as Supramolecular Guests for DNA and RNA: Synthesis, Structural Characterization, In Silico, Spectrophotometric and Antimicrobial Studies

The present work reports the synthesis of new N4-donor compounds carrying p-xylyl spacers in their structure. Different Schiff base aliphatic N-donors were obtained synthetically and subsequently evaluated for their ability to interact with two models of nucleic acids: calf-thymus DNA (CT-DNA) and the RNA from yeast Saccharomyces cerevisiae (herein simply indicated as RNA). In more detail, by condensing p-xylylenediamine and a series of aldehydes, we obtained the following Schiff base ligands: 2-thiazolecarboxaldehyde (L1), pyridine-2-carboxaldehyde (L2), 5-methylisoxazole-3-carboxaldehyde (L3), 1-methyl-2-imidazolecarboxaldehyde (L4), and quinoline-2-carboxaldehyde (L5). The structural characterisation of the ligands L1-L5 (X-ray, ¹H NMR, ¹³C NMR, elemental analysis) and of the coordination polymers {[CuL1]PF₆}_n (herein referred to as Polymer1) and {[AgL1]BF₄}_n, (herein referred to as Polymer2, X-ray, ¹H NMR, ESI-MS) is herein described in detail. The single crystal X-ray structures of complexes Polymer1 and Polymer2 were also investigated, leading to the description of one-dimensional coordination polymers. The spectroscopic and in silico evaluation of the most promising compounds as DNA and RNA binders, as well as the study of the influence of the 1D supramolecular polymers Polymer1 and Polymer2 on the proliferation of Escherichia coli bacteria, were performed in view of their nucleic acid-modulating and antimicrobial applications. Spectroscopic measurements (UV-Vis) combined with molecular docking calculations suggest that the thiazolecarboxaldehyde derivative L1 is able to bind CT-DNA with a mechanism different from intercalation involving the thiazole ring in the molecular recognition and shows a binding affinity with DNA higher than RNA. Finally, Polymer2 was shown to slow down the proliferation of bacteria much more effectively than the free Ag(I) salt.

Computational Modeling of IN-CTD/TAR Complex to Elucidate Additional Strategies to Inhibit HIV-1 Replication

HIV-1 integrase (IN) is a key enzyme that is essential for mediating the insertion of retroviral DNA into the host chromosome. IN also exhibits additional functions which are not fully elucidated, including its ability to bind to viral genomic RNA. Lack of binding of IN to RNA within the virions has been shown to be associated with production of morphologically defective virus particles. However, the exact structure of HIV-1 IN bound to RNA is not known. Based on the studies that C-terminal domain (CTD) of IN binds to TAR RNA region and based on the observation that TAR and the host factor INI1 binding to IN-CTD are identical, we computationally modelled the IN-CTD/TAR complex structure. Computational modeling of nucleic acid binding to proteins is a valuable method to understand the macromolecular interaction when experimental methods of solving the complex structures are not feasible. The current model of the IN-CTD/TAR complex may facilitate further understanding of this interaction and may lead to therapeutic targeting of IN-CTD/RNA interactions to inhibit HIV-1 replication.

Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.

HDOCK update for modeling protein-RNA/DNA complex structures

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

Detailed Molecular Interactions between Respiratory Syncytial Virus Fusion Protein and the TLR4/MD-2 Complex In Silico

Molecular interactions between respiratory syncytial virus (RSV) fusion protein (F protein) and the cellular receptor Toll-like receptor 4 (TLR4) and myeloid differentiation factor-2 (MD-2) protein complex are unknown. Thus, to reveal the detailed molecular interactions between them, in silico analyses were performed using various bioinformatics techniques. The present simulation data showed that the neutralizing antibody (NT-Ab) binding sites in both prefusion and postfusion proteins at sites II and IV were involved in the interactions between them and the TLR4 molecule. Moreover, the binding affinity between postfusion proteins and the TLR4/MD-2 complex was higher than that between prefusion proteins and the TLR4/MD-2 complex. This increased binding affinity due to conformational changes in the F protein may be able to form syncytium in RSV-infected cells. These results may contribute to better understand the infectivity and pathogenicity (syncytium formation) of RSV.

In vitro inhibition of acetylcholinesterase activity by yellow field pea (Pisum sativum) protein-derived peptides as revealed by kinetics and molecular docking

Compounds with structural similarities to the neurotransmitter (acetylcholine) are mostly used to inhibit the activity of acetylcholinesterase (AChE) in Alzheimer’s disease (AD) therapy. However, the existing drugs only alleviate symptoms of moderate to mild conditions and come with side effects; hence, the search is still on for potent and safer options. In this study, High performance liquid chromatography (HPLC) fractionations of AChE-inhibitory pea protein hydrolysates obtained from alcalase, flavourzyme and pepsin digestions were carried out followed by sequence identification of the most active fractions using mass spectrometry. Subsequently, 20 novel peptide sequences identified from the active fractions were synthesized and five peptides, QSQS, LQHNA, SQSRS, ETRSQ, PQDER (IC₅₀ = 1.53 – 1.61 μg/mL) were selected and analyzed for ability to change AChE protein conformation (fluorescence emission and circular dichroism), kinetics of enzyme inhibition, and enzyme-ligand binding configurations using molecular docking. The kinetics studies revealed different inhibition modes by the peptides with relatively low (<0.02 mM and <0.1 mM) inhibition constant and Michaelis constant, respectively, while maximum velocity was reduced. Conformational changes were confirmed by losses in fluorescence intensity and reduced α-helix content of AChE after interactions with different peptides. Molecular docking revealed binding of the peptides to both the catalytic anionic site and the peripheral anionic site. The five analyzed peptides all contained glutamine (Q) but sequences with Q in the penultimate N-terminal position (LQHNA, SQSRS, and PQDER) had stronger binding affinity. Results from the different analysis in this study confirm that the peptides obtained from enzymatic digestion of pea protein possess the potential to be used as novel AChE-inhibitory agents in AD management.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ligand-based design of peptide entry inhibitors targeting the endosomal receptor binding site of filoviruses

Filoviruses enter cells through micropinocytosis and trafficking into the endosomes in which they bind to the receptor Niemann-Pick C1 protein (NPC1) for membrane fusion and entry into the cytoplasm. The endosomal receptor-binding is critical step for filovirus entry. Designing inhibitors to block receptor binding will prevent viral entry. Using available binding structural information from the co-crystal structures of the viral GP with the receptor NPC1 or with monoclonal antibodies, we have conducted structure-based design of peptide inhibitors to target the receptor binding site (RBS). The designed peptides were tested for their inhibition activity against pseudo-typed or replication-competent viruses in a cell-based assay. The results indicate that these peptides exhibited strong activities against both Ebola and Marburg virus infection. It is expected that these peptides can be further developed for therapeutic use to treat filovirus infection and combat the outbreaks.

Studying protein-protein interaction through side-chain modeling method OPUS-Mut

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

Identification of a HTT-specific binding motif in DNAJB1 essential for suppression and disaggregation of HTT

Huntington’s disease is a neurodegenerative disease caused by an expanded polyQ stretch within Huntingtin (HTT) that renders the protein aggregation-prone, ultimately resulting in the formation of amyloid fibrils. A trimeric chaperone complex composed of Hsc70, DNAJB1 and Apg2 can suppress and reverse the aggregation of HTTExon1Q₄₈. DNAJB1 is the rate-limiting chaperone and we have here identified and characterized the binding interface between DNAJB1 and HTTExon1Q₄₈. DNAJB1 exhibits a HTT binding motif (HBM) in the hinge region between C-terminal domains (CTD) I and II and binds to the polyQ-adjacent proline rich domain (PRD) of soluble as well as aggregated HTT. The PRD of HTT represents an additional binding site for chaperones. Mutation of the highly conserved H244 of the HBM of DNAJB1 completely abrogates the suppression and disaggregation of HTT fibrils by the trimeric chaperone complex. Notably, this mutation does not affect the binding and remodeling of any other protein substrate, suggesting that the HBM of DNAJB1 is a specific interaction site for HTT. Overexpression of wt DNAJB1, but not of DNAJB1^H244A can prevent the accumulation of HTTExon1Q₉₇ aggregates in HEK293 cells, thus validating the biological significance of the HBM within DNAJB1.

Ayala Mariscal et al have identified and characterized the interface of pathogenic Huntingtin and the molecular chaperone DNAJB1. Histidine-244 of the C-terminal domain of DNAJB1 is a key residues for binding to the poly-proline region of HTT. This binding site is specific for the interaction with Huntingtin.

CD, UV, and In Silico Insights on the Effect of 1,3-Bis(1'-uracilyl)-2-propanone on Serum Albumin Structure

1,3-diaryl-2-propanone derivatives are synthetic compounds used as building blocks for the realization not only of antimicrobial drugs but also of new nanomaterials thanks to their ability to self-assemble in solution and interact with nucleopeptides. However, their ability to interact with proteins is a scarcely investigated theme considering the therapeutic importance that 1,3-diaryl-2-propanones could have in the modulation of protein-driven processes. Within this scope, we investigated the protein binding ability of 1,3-bis(1'-uracilyl)-2-propanone, which was previously synthesized in our laboratory utilizing a Dakin-West reaction and herein indicated as U2O, using bovine serum albumin (BSA) as the model protein. Through circular dichroism (CD) and UV spectroscopy, we demonstrated that the compound, but not the similar thymine derivative T2O, was able to alter the secondary structure of the serum albumin leading to significant consequences in terms of BSA structure with respect to the unbound protein (Δβ-turn + Δβ-sheet = +23.6%, Δα = -16.7%) as revealed in our CD binding studies. Moreover, molecular docking studies suggested that U2O is preferentially housed in the domain IIIB of the protein, and its affinity for the albumin is higher than that of the reference ligand HA 14-1 (HDOCK score (top 1-3 poses): -157.11 ± 1.38 (U2O); -129.80 ± 6.92 (HA 14-1); binding energy: -7.6 kcal/mol (U2O); -5.9 kcal/mol (HA 14-1)) and T2O (HDOCK score (top 1-3 poses): -149.93 ± 2.35; binding energy: -7.0 kcal/mol). Overall, the above findings suggest the ability of 1,3-bis(1'-uracilyl)-2-propanone to bind serum albumins and the observed reduction of the α-helix structure with the concomitant increase in the β-structure are consistent with a partial protein destabilization due to the interaction with U2O.

Methylphenidate Analogues as a New Class of Potential Disease-Modifying Agents for Parkinson's Disease: Evidence from Cell Models and Alpha-Synuclein Transgenic Mice

Parkinson's disease (PD) is characterized by dopaminergic nigrostriatal neurons degeneration and Lewy body pathology, mainly composed of α-synuclein (αSyn) fibrillary aggregates. We recently described that the neuronal phosphoprotein Synapsin III (Syn III) participates in αSyn pathology in PD brains and is a permissive factor for αSyn aggregation. Moreover, we reported that the gene silencing of Syn III in a human αSyn transgenic (tg) mouse model of PD at a pathological stage, manifesting marked insoluble αSyn deposits and dopaminergic striatal synaptic dysfunction, could reduce αSyn aggregates, restore synaptic functions and motor activities and exert neuroprotective effects. Interestingly, we also described that the monoamine reuptake inhibitor methylphenidate (MPH) can recover the motor activity of human αSyn tg mice through a dopamine (DA) transporter-independent mechanism, which relies on the re-establishment of the functional interaction between Syn III and α-helical αSyn. These findings support that the pathological αSyn/Syn III interaction may constitute a therapeutic target for PD. Here, we studied MPH and some of its analogues as modulators of the pathological αSyn/Syn III interaction. We identified 4-methyl derivative I-threo as a lead candidate modulating αSyn/Syn III interaction and having the ability to reduce αSyn aggregation in vitro and to restore the motility of αSyn tg mice in vivo more efficiently than MPH. Our results support that MPH derivatives may represent a novel class of αSyn clearing agents for PD therapy.

Sigma-1 receptor attenuates osteoclastogenesis by promoting ER-associated degradation of SERCA2

Sigma-1 receptor (Sigmar1) is a specific chaperone located in the mitochondria-associated endoplasmic reticulum membrane (MAM) and plays a role in several physiological processes. However, the role of Sigmar1 in bone homeostasis remains unknown. Here, we show that mice lacking Sigmar1 exhibited severe osteoporosis in an ovariectomized model. In contrast, overexpression of Sigmar1 locally alleviated the osteoporosis phenotype. Treatment with Sigmar1 agonists impaired both human and mice osteoclast formation in vitro. Mechanistically, SERCA2 was identified to interact with Sigmar1 based on the immunoprecipitation-mass spectrum (IP-MS) and co-immunoprecipitation (co-IP) assays, and Q615 of SERCA2 was confirmed to be the critical residue for their binding. Furthermore, Sigmar1 promoted SERCA2 degradation through Hrd1/Sel1L-dependent ER-associated degradation (ERAD). Ubiquitination of SERCA2 at K460 and K541 was responsible for its proteasomal degradation. Consequently, inhibition of SERCA2 impeded Sigmar1 deficiency enhanced osteoclastogenesis. Moreover, we found that dimemorfan, an FDA-approved Sigmar1 agonist, effectively rescued bone mass in various established bone-loss models. In conclusion, Sigmar1 is a negative regulator of osteoclastogenesis, and activation of Sigmar1 by dimemorfan may be a potential treatment for osteoporosis in clinical practice.

Anurans against SARS-CoV-2: A review of the potential antiviral action of anurans cutaneous peptides

At the end of 2019, in China, clinical signs and symptoms of unknown etiology have been reported in several patients whose sample sequencing revealed pneumonia caused by the SARS-CoV-2 virus. COVID-19 is a disease triggered by this virus, and in 2020, the World Health Organization declared it a pandemic. Since then, efforts have been made to find effective therapeutic agents against this disease. Identifying novel natural antiviral drugs can be an alternative to treatment. For this reason, antimicrobial peptides secreted by anurans' skin have gained attention for showing a promissory antiviral effect. Hence, this review aimed to elucidate how and which peptides secreted by anurans' skin can be considered therapeutic agents to treat or prevent human viral infectious diseases. Through a literature review, we attempted to identify potential antiviral frogs' peptides to combat COVID-19. As a result, the Magainin-1 and -2 peptides, from the Magainin family, the Dermaseptin-S9, from the Dermaseptin family, and Caerin 1.6 and 1.10, from the Caerin family, are molecules that already showed antiviral effects against SARS-CoV-2 in silico. In addition to these peptides, this review suggests that future studies should use other families that already have antiviral action against other viruses, such as Brevinins, Maculatins, Esculentins, Temporins, and Urumins. To apply these peptides as therapeutic agents, experimental studies with peptides already tested in silico and new studies with other families not tested yet should be considered.

Fibronectin containing alternatively spliced extra domain A interacts at the central and c-terminal domain of Toll-like receptor-4

Extra domain A of cellular fibronectin (FN-EDA) is known to cause insulin resistance, atherosclerosis, tissue fibrosis, ischemic stroke and exaggerated myocardial reperfusion injury through Toll-like receptor 4 (TLR4). However, the FN-EDA-TLR4 interacting site is not well established. Therefore, in-silico approaches have been used to study FN-EDA and TLR4 interactions at the interface. In the present study, molecular docking studies of FN-EDA with TLR4-myeloid differentiation factor 2 (MD2) heterodimer have been performed to unravel the FN-EDA-TLR4 interacting sequence. Furthermore, the modulatory role of FN-EDA adjacent domains FNIII(11) and FNIII(12) on its interaction with TLR4-MD2 was investigated. The results show that FN-EDA interacting sequence “SPEDGIRELF” selectively interacts with TLR4 directly near its central and C-terminal domain region. The regulatory domains, FN type III 11 facilitate and 12 impede the FN-EDA-TLR4 interaction. Furthermore, the molecular dynamic simulation studies confirmed that FN-EDA forms a stable complex with TLR4-MD2 heterodimer. In conclusion, FN-EDA interacts and forms a stable complex through its “SPEDGIRELF” sequence at the central and C-terminal domain region of TLR4. The revelation of FN-EDA and TLR4 interacting sites may help design novel therapeutics for drug discovery research.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

HIF-1-Dependent Induction of β3 Adrenoceptor: Evidence from the Mouse Retina

A major player in the homeostatic response to hypoxia is the hypoxia-inducible factor (HIF)-1 that transactivates a number of genes involved in neovessel proliferation in response to low oxygen tension. In the retina, hypoxia overstimulates β-adrenoceptors (β-ARs) which play a key role in the formation of pathogenic blood vessels. Among β-ARs, β3-AR expression is increased in proliferating vessels in concomitance with increased levels of HIF-1α and vascular endothelial growth factor (VEGF). Whether, similarly to VEGF, hypoxia-induced β3-AR upregulation is driven by HIF-1 is still unknown. We used the mouse model of oxygen-induced retinopathy (OIR), an acknowledged model of retinal angiogenesis, to verify the hypothesis of β3-AR transcriptional regulation by HIF-1. Investigation of β3-AR regulation over OIR progression revealed that the expression profile of β3-AR depends on oxygen tension, similar to VEGF. The additional evidence that HIF-1α stabilization decouples β3-AR expression from oxygen levels further indicates that HIF-1 regulates the expression of the β3-AR gene in the retina. Bioinformatics predicted the presence of six HIF-1 binding sites (HBS #1-6) upstream and inside the mouse β3-AR gene. Among these, HBS #1 has been identified as the most suitable HBS for HIF-1 binding. Chromatin immunoprecipitation-qPCR demonstrated an effective binding of HIF-1 to HBS #1 indicating the existence of a physical interaction between HIF-1 and the β3-AR gene. The additional finding that β3-AR gene expression is concomitantly activated indicates the possibility that HIF-1 transactivates the β3-AR gene. Our results are indicative of β3-AR involvement in HIF-1-mediated response to hypoxia.

SARS-CoV-2 Proteins Interact with Alpha Synuclein and Induce Lewy Body-like Pathology In Vitro

Growing cases of patients reported have shown a potential relationship between (severe acute respiratory syndrome coronavirus 2) SARS-CoV-2 infection and Parkinson’s disease (PD). However, it is unclear whether there is a molecular link between these two diseases. Alpha-synuclein (α-Syn), an aggregation-prone protein, is considered a crucial factor in PD pathology. In this study, bioinformatics analysis confirmed favorable binding affinity between α-Syn and SARS-CoV-2 spike (S) protein and nucleocapsid (N) protein, and direct interactions were further verified in HEK293 cells. The expression of α-Syn was upregulated and its aggregation was accelerated by S protein and N protein. It was noticed that SARS-CoV-2 proteins caused Lewy-like pathology in the presence of α-Syn overexpression. By confirming that SARS-CoV-2 proteins directly interact with α-Syn, our study offered new insights into the mechanism underlying the development of PD on the background of COVID-19.

TRScore: a three-dimensional RepVGG-based scoring method for ranking protein docking models

MOTIVATION

Protein-protein interactions (PPI) play important roles in cellular activities. Due to the technical difficulty and high cost of experimental methods, there are considerable interests towards the development of computational approaches, such as protein docking, to decipher PPI patterns. One of the important and difficult aspects in protein docking is recognizing near-native conformations from a set of decoys, but unfortunately traditional scoring functions still suffer from limited accuracy. Therefore, new scoring methods are pressingly needed in methodological and/or practical implications.

RESULTS

We present a new deep learning-based scoring method for ranking protein-protein docking models based on a three-dimensional (3D) RepVGG network, named TRScore. To recognize near-native conformations from a set of decoys, TRScore voxelizes the protein-protein interface into a 3D grid labeled by the number of atoms in different physicochemical classes. Benefiting from the deep convolutional RepVGG architecture, TRScore can effectively capture the subtle differences between energetically favorable near-native models and unfavorable non-native decoys without needing extra information. TRScore was extensively evaluated on diverse test sets including protein-protein docking benchmark 5.0 update set, DockGround decoy set, as well as realistic CAPRI decoy set, and overall obtained a significant improvement over existing methods in cross validation and independent evaluations.

AVAILABILITY

Codes available at: https://github.com/BioinformaticsCSU/TRScore.

The Healing Power of Clean Rivers: In Silico Evaluation of the Antipsoriatic Potential of Apiin and Hyperoside Plant Metabolites Contained in River Waters

Humanity may benefit greatly from intact riverine ecosystems not only because they supply water to be used in the most common human activities, but also for the effects that clean rivers can have on human health. Herein, we used a computational approach to show that some phytochemicals produced by riparian plants as secondary metabolites, which are naturally released into river waters, can have therapeutic properties. These include antipsoriatic activities which we demonstrated in silico by modelling the interaction of apiin, guanosine and hyperoside, a few main river plant metabolites, with NF-kB, IL-17 and IL-36, which are recognized targets involved in psoriasis disease. In particular, we found that apiin and hyperoside are endowed with docking energies and binding affinities which are more favorable than the known reference inhibitors of the three protein targets whilst, in silico, guanosine shows comparable activity with respect to the inhibitors of IL-36 and NF-kB. The low skin permeation (logKp < −8) we predicted for apiin and hyperoside led us to hypothesize their possible utilization as topic antipsoriatic therapeutics, and in particular after PAINS (pan-assay interference compounds) score evaluation, we reached the conclusion that apiin, with no predicted tendency to react nonspecifically with the numerous targets involved in the biological cellular pathways, is particularly interesting for the desired therapeutic application.

Efficient Enzymatic Incorporation of Dehydroalanine Based on SAMDI-Assisted Identification of Optimized Tags for OspF/SpvC

Site-specific modification of proteins has important applications in biological research and drug development. Reactive tags such as azide, alkyne, and tetrazine have been used extensively to achieve the abovementioned goal. However, bulky side-chain "ligation scars" are often left after the labeling and may hinder the biological application of such engineered protein products. Conjugation chemistry via dehydroalanine (Dha) may provide an opportunity for "traceless" ligation because the activated alkene moiety on Dha can then serve as an electrophile to react with radicalophile, thiol/amine nucleophile, and reactive phosphine probe to introduce a minimal linker in the protein post-translational modifications. In this report, we present a mild and highly efficient enzymatic approach to incorporate Dha with phosphothreonine/serine lyases, OspF and SpvC. These lyases originally catalyze an irreversible elimination reaction that converts a doubly phosphorylated substrate with phosphothreonine (pT) or phosphoserine (pS) to dehydrobutyrine (Dhb) or Dha. To generate a simple monophosphorylated tag for these lyases, we conducted a systematic approach to profile the substrate specificity of OspF and SpvC using peptide arrays and self-assembled monolayers for matrix-assisted laser desorption/ionization mass spectrometry. The optimized tag, [F/Y/W]-pT/pS-[F/Y/W] (where [F/Y/W] indicates an aromatic residue), results in a ∼10-fold enhancement of the overall peptide labeling efficiency via Dha chemistry and enables the first demonstration of protein labeling as well as live cell labeling with a minimal ligation linker via enzyme-mediated incorporation of Dha.

Disaggregation of Islet Amyloid Polypeptide Fibrils as a Potential Anti-Fibrillation Mechanism of Tetrapeptide TNGQ

Islet amyloid polypeptide (IAPP) fibrillation has been commonly associated with the exacerbation of type 2 diabetes prognosis. Consequently, inhibition of IAPP fibrillation to minimize β-cell cytotoxicity is an important approach towards β-cell preservation and type 2 diabetes management. In this study, we identified three tetrapeptides, TNGQ, MANT, and YMSV, that inhibited IAPP fibrillation. Using thioflavin T (ThT) fluorescence assay, circular dichroism (CD) spectroscopy, dynamic light scattering (DLS), and molecular docking, we evaluated the potential anti-fibrillation mechanism of the tetrapeptides. ThT fluorescence kinetics and microscopy as well as transmission electron microscopy showed that TNGQ was the most effective inhibitor based on the absence of normal IAPP fibrillar morphology. CD spectroscopy showed that TNGQ maintained the α-helical conformation of monomeric IAPP, while DLS confirmed the presence of varying fibrillation species. Molecular docking showed that TNGQ and MANT interact with monomeric IAPP mainly by hydrogen bonding and electrostatic interaction, with TNGQ binding at IAPP surface compared to YMSV, which had the highest docking score, but interact mainly through hydrophobic interaction in IAPP core. The highly polar TNGQ was the most active and appeared to inhibit IAPP fibrillation by disaggregation of preformed IAPP fibrils. These findings indicate the potential of TNGQ in the development of peptide-based anti-fibrillation and antidiabetic nutraceuticals.