Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

MOTIVATION: The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. RESULTS: We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. AVAILABILITY AND IMPLEMENTATION: Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Investigation of the halophilic PET hydrolase PET6 from Vibrio gazogenes.

The handling of plastic waste and the associated ubiquitous occurrence of microplastic poses one of the biggest challenges of our time. Recent investigations of plastic degrading enzymes have opened new prospects for biological microplastic decomposition as well as recycling applications. For polyethylene terephthalate, in particular, several natural and engineered enzymes are known to have such promising properties. From a previous study that identified new PETase candidates by homology search, we chose the candidate PET6 from the globally distributed, halophilic organism Vibrio gazogenes for further investigation. By mapping the occurrence of Vibrios containing PET6 homologs we demonstrated their ubiquitous prevalence in the pangenome of several Vibrio strains. The biochemical characterization of PET6 showed that PET6 has a comparatively lower activity than other enzymes but also revealed a superior turnover at very high salt concentrations. The crystal structure of PET6 provides structural insights into this adaptation to saline environments. By grafting only a few beneficial mutations from other PET degrading enzymes onto PET6, we increased the activity up to three-fold, demonstrating the evolutionary potential of the enzyme. MD simulations of the variant helped rationalize the mutational effects of those mutants and elucidate the interaction of the enzyme with a PET substrate. With tremendous amounts of plastic waste in the Ocean and the prevalence of Vibrio gazogenes in marine biofilms and estuarine marshes, our findings suggest that Vibrio and the PET6 enzyme are worthy subjects to study the PET degradation in marine environments.

Serine and Cysteine Peptidases: So Similar, Yet Different. How the Active-Site Electrostatics Facilitates Different Reaction Mechanisms.

The catalytic mechanisms of serine and cysteine peptidases are similar: the proton of the nucleophile (serine or cysteine) is transferred to the catalytic histidine, and the nucleophile attacks the substrate for cleavage. However, they differ in an important aspect: cysteine peptidases form a stable ion-pair intermediate in a stepwise mechanism, while serine peptidases follow a concerted mechanism. While it is known that a positive electrostatic potential at the active site of cysteine peptidases stabilizes the cysteine anion in the ion-pair state, the physical basis of the concerted mechanism of serine peptidases is poorly understood. In this work, we use continuum electrostatic analysis and quantum mechanical/molecular mechanical (QM/MM) simulations to demonstrate that a destabilization of an anionic serine by a negative electrostatic potential in combination with a compact active site geometry facilitates a concerted mechanism in serine peptidases. Moreover, we show that an anionic serine would destabilize the protein significantly compared to an anionic cysteine in cysteine peptidases, which underlines the necessity of a concerted mechanism for serine peptidases. On the basis of our calculations on an inactive serine mutant of a natural cysteine peptidase, we show that the energy barrier for the catalytic mechanism can be substantially decreased by introducing a negative electrostatic potential and by reducing the relevant distances indicating that these parameters are essential for the activity of serine peptidases. Our work demonstrates that the concerted mechanism of serine peptidases represents an evolutionary innovative way to perform catalysis without the energetically expensive need to stabilize the anionic serine. In contrast in cysteine peptidases, the anionic cysteine is energetically easily accessible and it is a very efficient nucleophile, making these peptidases mechanistically simple. However, a cysteine is highly oxygen sensitive, which is problematic in an aerobic environment. On the basis of the analysis in this work, we suggest that serine peptidases represent an oxygen-insensitive alternative to cysteine peptidases.

Structural and Biophysical Analysis of the Phytochelatin-Synthase-Like Enzyme from Nostoc sp. Shows That Its Protease Activity is Sensitive to the Redox State of the Substrate.

Phytochelatins (PCs) are nonribosomal thiol-rich oligopeptides synthetized from glutathione (GSH) in a γ-glutamylcysteinyl transpeptidation reaction catalyzed by PC synthases (PCSs). Ubiquitous in plant and present in some invertebrates, PCSs are involved in metal detoxification and homeostasis. The PCS-like enzyme from the cyanobacterium Nostoc sp. (NsPCS) is considered to be an evolutionary precursor enzyme of genuine PCSs because it shows sufficient sequence similarity for homology to the catalytic domain of the eukaryotic PCSs and shares the peptidase activity consisting in the deglycination of GSH. In this work, we investigate the catalytic mechanism of NsPCS by combining structural, spectroscopic, thermodynamic, and theoretical techniques. We report several crystal structures of NsPCS capturing different states of the catalyzed chemical reaction: (i) the structure of the wild-type enzyme (wt-NsPCS); (ii) the high-resolution structure of the γ-glutamyl-cysteine acyl-enzyme intermediate (acyl-NsPCS); and (iii) the structure of an inactive variant of NsPCS, with the catalytic cysteine mutated into serine (C70S-NsPCS). We characterize NsPCS as a relatively slow enzyme whose activity is sensitive to the redox state of the substrate. Namely, NsPCS is active with reduced glutathione (GSH), but is inhibited by oxidized glutathione (GSSG) because the cleavage product is not released from the enzyme. Our biophysical analysis led us to suggest that the biological function of NsPCS is being a part of a redox sensing system. In addition, we propose a mechanism how PCS-like enzymes may have evolved toward genuine PCS enzymes.

Modular peptide binders - development of a predictive technology as alternative for reagent antibodies.

Current biomedical research and diagnostics critically depend on detection agents for specific recognition and quantification of protein molecules. Monoclonal antibodies have been used for this purpose over decades and facilitated numerous biological and biomedical investigations. Recently, however, it has become apparent that many commercial reagent antibodies lack specificity or do not recognize their target at all. Thus, synthetic alternatives are needed whose complex designs are facilitated by multidisciplinary approaches incorporating experimental protein engineering with computational modeling. Here, we review the status of such an engineering endeavor based on the modular armadillo repeat protein scaffold and discuss challenges in its implementation.

Computational Biochemistry-Enzyme Mechanisms Explored.

Understanding enzyme mechanisms is a major task to achieve in order to comprehend how living cells work. Recent advances in biomolecular research provide huge amount of data on enzyme kinetics and structure. The analysis of diverse experimental results and their combination into an overall picture is, however, often challenging. Microscopic details of the enzymatic processes are often anticipated based on several hints from macroscopic experimental data. Computational biochemistry aims at creation of a computational model of an enzyme in order to explain microscopic details of the catalytic process and reproduce or predict macroscopic experimental findings. Results of such computations are in part complementary to experimental data and provide an explanation of a biochemical process at the microscopic level. In order to evaluate the mechanism of an enzyme, a structural model is constructed which can be analyzed by several theoretical approaches. Several simulation methods can and should be combined to get a reliable picture of the process of interest. Furthermore, abstract models of biological systems can be constructed combining computational and experimental data. In this review, we discuss structural computational models of enzymatic systems. We first discuss various models to simulate enzyme catalysis. Furthermore, we review various approaches how to characterize the enzyme mechanism both qualitatively and quantitatively using different modeling approaches.

PyCPR - a python-based implementation of the Conjugate Peak Refinement (CPR) algorithm for finding transition state structures.

Conjugate peak refinement (CPR) is a powerful and robust method to search transition states on a molecular potential energy surface. Nevertheless, the method was to the best of our knowledge so far only implemented in CHARMM. In this paper, we present PyCPR, a new Python-based implementation of the CPR algorithm within the pDynamo framework. We provide a detailed description of the theory underlying our implementation and discuss the different parts of the implementation. The method is applied to two different problems. First, we illustrate the method by analyzing the gauche to anti-periplanar transition of butane using a semiempirical QM method. Second, we reanalyze the mechanism of a glycyl-radical enzyme, namely of 4-hydroxyphenylacetate decarboxylase (HPD) using QM/MM calculations. In the end, we suggest a strategy how to use our implementation of the CPR algorithm. The integration of PyCPR into the framework pDynamo allows the combination of CPR with the large variety of methods implemented in pDynamo. PyCPR can be used in combination with quantum mechanical and molecular mechanical methods (and hybrid methods) implemented directly in pDynamo, but also in combination with external programs such as ORCA using pDynamo as interface. PyCPR is distributed as free, open source software and can be downloaded from http://www.bisb.uni-bayreuth.de/index.php?page=downloads . Graphical Abstract PyCPR is a search tool for finding saddle points on the potential energy landscape of a molecular system.