PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface

Simulated gastrointestinal digestion of walnut protein yields anti-inflammatory peptides

The impact of the simulated gastrointestinal digestion process on walnut protein and the potential anti-inflammatory properties of its metabolites was studied. Structural changes induced by digestion, notably in α-Helix, β-Turn, and Random Coil configurations, were unveiled. Proteins over 10,000 Da significantly decreased by 35.6 %. Antioxidant activity in these metabolites paralleled increased amino acid content. Molecular docking identified three walnut polypeptides-IPAGTPVYLINR, FQGQLPR, and VVYVLR-with potent anti-inflammatory properties. RMSD and RMSF analysis demonstrated the stable and flexible interaction of these polypeptides with their target proteins. In lipopolysaccharide (LPS)-induced inflammation in normal human colon mucosal epithelial NCM460 cells, these peptides decreased 5-hydroxytryptamine (5-HT), tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor (VEGF) expression, while mitigating cell apoptosis and inflammation. Our study offers valuable insights into walnut protein physiology, shedding light on its potential health benefits.

Enzyme and Pathway Engineering for Improved Betanin Production in Saccharomyces cerevisiae

Betanin is a water-soluble red-violet pigment belonging to the betacyanins family. It has become more and more attractive for its natural food colorant properties and health benefits. However, the commercial production of betanin, typically extracted from red beetroot, faces economic and sustainability challenges. Microbial heterologous production therefore offers a promising alternative. Here, we performed combinatorial engineering of plant P450 enzymes and precursor metabolisms to improve the de novo production of betanin in Saccharomyces cerevisiae. Semirational design by computer simulation and molecular docking was used to improve the catalytic activity of CYP76AD. Alanine substitution and site-directed saturation mutants were screened, with a combination mutant showing an approximately 7-fold increase in betanin titer compared to the wild type. Subsequently, betanin production was improved by enhancing the l-tyrosine pathway flux and UDP-glucose supply. Finally, after optimization of the fermentation process, the engineered strain BEW10 produced 134.1 mg/L of betanin from sucrose, achieving the highest reported titer of betanin in a shake flask by microbes. This work shows the P450 enzyme and metabolic engineering strategies for the efficient microbial production of natural complex products.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

SARS-CoV-2 Spike mutations modify the interaction between virus Spike and human ACE2 receptors

The high mutability of the SARS-CoV-2 virus is a growing concern among scientific communities and health professionals since it brings the effectiveness of repurposed drugs and vaccines for COVID-19 into question. Although the mutational investigation of the Spike protein of the SARS-CoV-2 virus has been confirmed by many different researchers, there is no thorough investigation carried out at the interacting region to reveal the mutational status and its associated severity. All the energetically favorable mutations and their detailed analytical features that could impact the infection severity of the SARS-CoV-2 virus need to be identified. Therefore, we have thoroughly investigated the most important site of the SARS-CoV-2 virus, which is the interface region (Residue 417–505) of the virus Spike that interacts with the human ACE2 receptor. Further, we have utilized molecular dynamic simulation to observe the relative stability of the Spike protein with partner ACE2, as a consequence of these mutations. In our study, we have identified 52 energetically favorable Spike mutations at the interface while binding to ACE2, of which only 36 significantly enhance the stabilization of the Spike-ACE2 complex. The stability order and molecular interactions of these mutations were also identified. The highest stabilizing mutation V503D confirmed in our study is also known for neutralization resistance.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Engineering diacetylchitobiose deacetylase from Pyrococcus horikoshii towards an efficient glucosamine production

In this study, semi-rational design based on site-directed saturation mutagenesis and surface charge modification was used to improve the catalytic efficiency of the diacetylchitobiose deacetylase derived from Pyrococcus horikoshii (PhDac). PhDac mutant M14, which was screened by site-directed saturation mutagenesis, showed a ~ 2.21 -fold enhanced catalytic efficiency (kcat/Km) and the specific activity was improved by 70.02%. To keep the stability of glucosamine (GlcN), we reduced the optimal pH of M14 by modifying the surface charge from -35 to -59 to obtain mutant M20, whose specific activity reached 2 -fold of the wild-type. The conversion rate of N-acetylglucosamine (GlcNAc) to GlcN catalyzed by M20 reached 94.3%. Moreover, the decline of GlcN production was slowed down by the reduction of pH when temperature was higher than 50 ℃. Our results would accelerate the process of industrial production of GlcN by biocatalysis.

Anchor-Locker Binding Mechanism of the Coronavirus Spike Protein to Human ACE2: Insights from Computational Analysis

COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus' S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an "anchor-locker" recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.

PDBeCIF: an open-source mmCIF/CIF parsing and processing package

Background

Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible.

Results

To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository (http://pypi.org/project/pdbecif) and from GitHub (https://github.com/pdbeurope/pdbecif) along with rich documentation and many ready-to-use examples.

Conclusions

PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04271-9.