Conformational entropy of a single peptide controlled under force governs protease recognition and catalysis

Bacterial growth - from physical principles to autolysins

For bacteria to increase in size, they need to enzymatically expand their cell envelopes, and more concretely their peptidoglycan cell wall. A major task of growth is to increase intracellular space for the accumulation of macromolecules, notably proteins, RNA, and DNA. Here, we review recent progress in our understanding of how cells coordinate envelope growth with biomass growth, focusing on elongation of rod-like bacteria. We first describe the recent discovery that surface area, but not cell volume, increases in proportion to mass growth. We then discuss how this relation could possibly be implemented mechanistically, reviewing the role of envelope insertion for envelope growth. Since cell-wall expansion requires the well-controlled activity of autolysins, we finally review recent progress in our understanding of autolysin regulation.

Molecular interpretation of single-molecule force spectroscopy experiments with computational approaches

Single molecule force-spectroscopy techniques have granted access to unprecedented molecular-scale details about biochemical and biological mechanisms. However, the interpretation of the experimental data is often challenging. Computational and simulation approaches (all-atom steered MD simulations in particular) are key to provide molecular details about the associated mechanisms, to help test different hypotheses and to predict experimental results. In this review, particular recent efforts directed towards the molecular interpretation of single-molecule force spectroscopy experiments on proteins and protein-related systems (often in close collaboration with experimental groups) will be presented. These results will be discussed in the broader context of the field, highlighting the recent achievements and the ongoing challenges for computational biophysicists and biochemists. In particular, I will focus on the input gained from molecular simulations approaches to rationalize the origin of the unfolded protein elasticity and the protein conformational behavior under force, to understand how force denaturation differs from chemical, thermal or shear unfolding, and to unravel the molecular details of unfolding events for a variety of systems. I will also discuss the use of models based on Langevin dynamics on a 1-D free-energy surface to understand the effect of protein segmentation on the work exerted by a force, or, at the other end of the spectrum of computational techniques, how quantum calculations can help to understand the reactivity of disulfide bridges exposed to force.

Recent Advances and Emerging Challenges in the Molecular Modeling of Mechanobiological Processes

Many biological processes result from the effect of mechanical forces on macromolecular structures and on their interactions. In particular, the cell shape, motion, and differentiation directly depend on mechanical stimuli from the extracellular matrix or from neighboring cells. The development of experimental techniques that can measure and characterize the tiny forces acting at the cellular scale and down to the single-molecule, biomolecular level has enabled access to unprecedented details about the involved mechanisms. However, because the experimental observables often do not provide a direct atomistic picture of the corresponding phenomena, particle-based simulations performed at various scales are instrumental in complementing these experiments and in providing a molecular interpretation. Here, we will review the recent key achievements in the field, and we will highlight and discuss the many technical challenges these simulations are facing, as well as suggest future directions for improvement.

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.

An automated protocol for modelling peptide substrates to proteases

BACKGROUND

Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition.

RESULTS

As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches.

CONCLUSION

Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.

Structural basis of mammalian mucin processing by the human gut O-glycopeptidase OgpA from Akkermansia muciniphila

Akkermansia muciniphila is a mucin-degrading bacterium commonly found in the human gut that promotes a beneficial effect on health, likely based on the regulation of mucus thickness and gut barrier integrity, but also on the modulation of the immune system. In this work, we focus in OgpA from A. muciniphila, an O-glycopeptidase that exclusively hydrolyzes the peptide bond N-terminal to serine or threonine residues substituted with an O-glycan. We determine the high-resolution X-ray crystal structures of the unliganded form of OgpA, the complex with the glycodrosocin O-glycopeptide substrate and its product, providing a comprehensive set of snapshots of the enzyme along the catalytic cycle. In combination with O-glycopeptide chemistry, enzyme kinetics, and computational methods we unveil the molecular mechanism of O-glycan recognition and specificity for OgpA. The data also contribute to understanding how A. muciniphila processes mucins in the gut, as well as analysis of post-translational O-glycosylation events in proteins.

The bacterial metalloprotease NleD selectively cleaves mitogen-activated protein kinases that have high flexibility in their activation loop

Microbial pathogens often target the host mitogen-activated protein kinase (MAPK) network to suppress host immune responses. We previously identified a bacterial type III secretion system effector, termed NleD, a metalloprotease that inactivates MAPKs by specifically cleaving their activation loop. Here, we show that NleDs form a growing family of virulence factors harbored by human and plant pathogens as well as insect symbionts. These NleDs disable specifically Jun N-terminal kinases (JNKs) and p38s that are required for host immune response, whereas extracellular signal-regulated kinase (ERK), which is essential for host cell viability, remains intact. We investigated the mechanism that makes ERK resistant to NleD cleavage. Biochemical and structural analyses revealed that NleD exclusively targets activation loops with high conformational flexibility. Accordingly, NleD cleaved the flexible loops of JNK and p38 but not the rigid loop of ERK. Our findings elucidate a compelling mechanism of native substrate proteolysis that is promoted by entropy-driven specificity. We propose that such entropy-based selectivity is a general attribute of proteolytic enzymes.

Disulfide engineering of human Kunitz-type serine protease inhibitors enhances proteolytic stability and target affinity toward mesotrypsin

Serine protease inhibitors of the Kunitz-bovine pancreatic trypsin inhibitor (BPTI) family are ubiquitous biological regulators of proteolysis. These small proteins are resistant to proteolysis, but can be slowly cleaved within the protease-binding loop by target proteases, thereby compromising their activity. For the human protease mesotrypsin, this cleavage is especially rapid. Here, we aimed to stabilize the Kunitz domain structure against proteolysis through disulfide engineering. Substitution within the Kunitz inhibitor domain of the amyloid precursor protein (APPI) that incorporated a new disulfide bond between residues 17 and 34 reduced proteolysis by mesotrypsin 74-fold. Similar disulfide engineering of tissue factor pathway inhibitor-1 Kunitz domain 1 (^KD1TFPI1) and bikunin Kunitz domain 2 (^KD2bikunin) likewise stabilized these inhibitors against mesotrypsin proteolysis 17- and 6.6-fold, respectively. Crystal structures of disulfide-engineered APPI and ^KD1TFPI1 variants in a complex with mesotrypsin at 1.5 and 2.0 Å resolution, respectively, confirmed the formation of well-ordered disulfide bonds positioned to stabilize the binding loop. Long all-atom molecular dynamics simulations of disulfide-engineered Kunitz domains and their complexes with mesotrypsin revealed conformational stabilization of the primed side of the inhibitor-binding loop by the engineered disulfide, along with global suppression of conformational dynamics in the Kunitz domain. Our findings suggest that the Cys-17-Cys-34 disulfide slows proteolysis by dampening conformational fluctuations in the binding loop and minimizing motion at the enzyme-inhibitor interface. The generalizable approach developed here for the stabilization against proteolysis of Kunitz domains, which can serve as important scaffolds for therapeutics, may thus find applications in drug development.