News from the strong interactions program of NA61/SHINE

NA61/SHINE (SPS Heavy Ion and Neutrino Experiment) is a fixedtarget experiment operating at the CERN SPS accelerator. The main goal of the strong interactions program of NA61/SHINE is to study the properties of the phase transition between confined matter and quark-gluon plasma by performing a two-dimensional scan in beam momentum and size of collided nuclei. Within this program, collisions of different systems (p+p, p+Pb, Be+Be, Ar+Sc, Xe+La, Pb+Pb) over a wide range of beam momenta (13A-150(8)A GeV/c) have been recorded. This contribution discusses the latest results of hadron production in p+p, Be+Be, Ar+Sc and Pb+Pb reactions measured by the NA61/SHINE. In particular, the results include charged kaons and pions spectra and higher-order moments of multiplicity and net charge distributions. The presented data are compared with the predictions of different theoretical models as well as the results from other experiments. Finally, the motivation and plans for future NA61/SHINE measurements are discussed.

Exploration of the Structure–Function Relationships of a Novel Frog Skin Secretion-Derived Bioactive Peptide, t-DPH1, through Use of Rational Design, Cationicity Enhancement and In Vitro Studies

Amphibian skin-derived antimicrobial peptides (AMPs) have attracted increasing attention from scientists because of their excellent bioactivity and low drug resistance. In addition to being the alternative choice of antibiotics or anticancer agents, natural AMPs can also be modified as templates to optimise their bioactivities further. Here, a novel dermaseptin peptide, t-DPH1, with extensive antimicrobial activity and antiproliferative activity, was isolated from the skin secretion of Phyllomedusa hypochondrialis through ‘shotgun’ cloning. A series of cationicity-enhanced analogues of t-DPH1 were designed to further improve its bioactivities and explore the charge threshold of enhancing the bioactivity of t-DPH1. The present data suggest that improving the net charge can enhance the bioactivities to some extent. However, when the charge exceeds a specific limit, the bioactivities decrease or remain the same. When the net charge achieves the limit, improving the hydrophobicity makes no sense to enhance bioactivity. For t-DPH1, the upper limit of the net charge was +7. All the designed cationicity-enhanced analogues produced no drug resistance in the Gram-negative bacterium, Escherichia coli. These findings provide creative insights into the role of natural drug discovery in providing templates for structural modification for activity enhancement.

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.

The Analyses of High Infectivity Mechanism of SARS-CoV-2 and Its Variants

SARS-CoV-2 has high infectivity and some of its variants have higher transmissibility. To explore the high infectivity mechanism, the charge distributions of SARS-CoV, SARS-CoV-2, and variants of concern were calculated through a series of net charge calculation formulas. The results showed that the SARS-CoV-2 spike protein had more positive charges than that of SARS-CoV. Further results showed that the variants had similar but higher positive charges than preexisting SARS-CoV-2. In particular, the Delta variant had the greatest increase in positive charges in S1 resulting in the highest infectivity. In particular, the S1 positive charge increased greatly in the Delta variant. The S1 positive charge increased, and due to the large negative charge of angiotensin-converting enzyme-2 (ACE2), this resulted in a large increase in Coulomb’s force between S1 and ACE2. This finding agrees with the expectation that the positive charges in the spike protein result in more negative charges on SARS-CoV-2 antibodies than that of SARS-CoV. Thus, the infectivity of a novel SARS-CoV-2 variant may be evaluated preliminarily by calculating the charge distribution.

An intrinsically disordered nascent protein interacts with specific regions of the ribosomal surface near the exit tunnel

The influence of the ribosome on nascent chains is poorly understood, especially in the case of proteins devoid of signal or arrest sequences. Here, we provide explicit evidence for the interaction of specific ribosomal proteins with ribosome-bound nascent chains (RNCs). We target RNCs pertaining to the intrinsically disordered protein PIR and a number of mutants bearing a variable net charge. All the constructs analyzed in this work lack N-terminal signal sequences. By a combination chemical crosslinking and Western-blotting, we find that all RNCs interact with ribosomal protein L23 and that longer nascent chains also weakly interact with L29. The interacting proteins are spatially clustered on a specific region of the large ribosomal subunit, close to the exit tunnel. Based on chain-length-dependence and mutational studies, we find that the interactions with L23 persist despite drastic variations in RNC sequence. Importantly, we also find that the interactions are highly Mg+2-concentration-dependent. This work is significant because it unravels a novel role of the ribosome, which is shown to engage with the nascent protein chain even in the absence of signal or arrest sequences.

Silver Nanoparticle-Mediated Synthesis of Fluorescent Thiolated Gold Nanoclusters

A new strategy using silver nanoparticles (Ag NPs) to synthesize thiolated Au NCs is demonstrated. The quasi-spherical Ag NPs serve as a platform, functioning as a reducing agent for Au (III) and attracting capping ligands to the surface of the Ag NPs. Glutathione disulfide (GSSG) and dithiothreitol (DTT) were used as capping ligands to synthesize thiolated Au NCs (glutathione-Au NCs and DTT-Au NCs). The glutathione-Au NCs and DTT-Au NCs showed red color luminance with similar emission wavelengths (630 nm) at an excitation wavelength of 354 nm. The quantum yields of the glutathione-Au NCs and DTT-Au NCs were measured to be 7.3% and 7.0%, respectively. An electrophoretic mobility assay showed that the glutathione-Au NCs moved toward the anode, while the DTT-Au NCs were not mobile under the electric field, suggesting that the total net charge of the thiolated Au NCs is determined by the charges on the capping ligands. The detection of the KSV values, 26 M−1 and 0 M−1, respectively, revealed that glutathione-Au NCs are much more accessible to an aqueous environment than DTT-Au NCs.

Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production

Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins.