PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity.

Virtual screening of protein-protein and protein-peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein-protein complexes have been proposed, methods specifically developed to predict protein-peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein-protein and protein-peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein-protein and protein-peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Selective Disruption of Survivin's Protein-Protein Interactions: A Supramolecular Approach Based on Guanidiniocarbonylpyrrole.

Targeting specific protein binding sites to interfere with protein-protein interactions (PPIs) is crucial for the rational modulation of biologically relevant processes. Survivin, which is highly overexpressed in most cancer cells and considered to be a key player of carcinogenesis, features two functionally relevant binding sites. Here, we demonstrate selective disruption of the Survivin/Histone H3 or the Survivin/Crm1 interaction using a supramolecular approach. By rational design we identified two structurally related ligands (LNES and LHIS ), capable of selectively inhibiting these PPIs, leading to a reduction in cancer cell proliferation.

Superoxide Dismutase 1 Folding Stability as a Target for Molecular Tweezers in SOD1-Related Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation are hallmarks of many severe neurodegenerative diseases including Alzheimer's, Parkinson's and Huntington's disease. As a supramolecular ligand that binds to lysine and arginine residues, the molecular tweezer CLR01 was found to modify the aggregation pathway of disease-relevant proteins in vitro and in vivo with beneficial effects on toxicity. However, the molecular mechanisms of how tweezers exert these effects remain mainly unknown, hampering further drug development. Here, we investigate the modulation mechanism of unfolding and aggregation pathways of SOD1, which are involved in amyotrophic lateral sclerosis (ALS), by CLR01. Using a truncated version of the wildtype SOD1 protein, SOD1bar , we show that CLR01 acts on the first step of the aggregation pathway, the unfolding of the SOD1 monomer. CLR01 increases, by ∼10 °C, the melting temperatures of the A4V and G41D SOD1 mutants, which are commonly observed mutations in familial ALS. Molecular dynamics simulations and binding free energy calculations as well as native mass spectrometry and mutational studies allowed us to identify K61 and K92 as binding sites for the tweezers to mediate the stability increase. The data suggest that the modulation of SOD1 conformational stability is a promising target for future developments of supramolecular ligands against neurodegenerative diseases.

Effect of Organic Solvents on the Structure and Activity of a Minimal Lipase.

Lipases are ubiquitously used in chemo-enzymatic synthesis and industrial applications. Nevertheless, the modulation of the activity of lipases by organic solvents still is not fully understood at the molecular level. We systematically investigated the activity and structure of lipase A from Bacillus subtilis in binary water-organic solvent mixtures of dimethyl sulfoxide (DMSO), acetonitrile (ACN), and isopropyl alcohol (IPA) using activity assays, fluorescence spectroscopy, molecular dynamics (MD) simulations, and FRET/MD analysis. The enzymatic activity strongly depended on the type and amount of organic solvent in the reaction media. Whereas IPA and ACN reduced the activity of the enzyme, small concentrations of DMSO led to lipase activation via an uncompetitive mechanism. DMSO molecules did not directly interfere with the binding of the substrate in the active site, contrary to what is known for other solvents and enzymes. We propose that the His156-Asp133 interaction, the binding of organic molecules to the active site, and the water accessibility of the substrate are key factors modulating the catalytic activity. Furthermore, we rationalized the role of solvent descriptors on the regulation of enzymatic activity in mixtures with low concentrations of the organic molecule, with prospective implications for the optimization of biocatalytic processes via solvent tuning.

Supramolecular Mechanism of Viral Envelope Disruption by Molecular Tweezers.

Broad-spectrum antivirals are powerful weapons against dangerous viruses where no specific therapy exists, as in the case of the ongoing SARS-CoV-2 pandemic. We discovered that a lysine- and arginine-specific supramolecular ligand (CLR01) destroys enveloped viruses, including HIV, Ebola, and Zika virus, and remodels amyloid fibrils in semen that promote viral infection. Yet, it is unknown how CLR01 exerts these two distinct therapeutic activities. Here, we delineate a novel mechanism of antiviral activity by studying the activity of tweezer variants: the "phosphate tweezer" CLR01, a "carboxylate tweezer" CLR05, and a "phosphate clip" PC. Lysine complexation inside the tweezer cavity is needed to antagonize amyloidogenesis and is only achieved by CLR01. Importantly, CLR01 and CLR05 but not PC form closed inclusion complexes with lipid head groups of viral membranes, thereby altering lipid orientation and increasing surface tension. This process disrupts viral envelopes and diminishes infectivity but leaves cellular membranes intact. Consequently, CLR01 and CLR05 display broad antiviral activity against all enveloped viruses tested, including herpesviruses, Measles virus, influenza, and SARS-CoV-2. Based on our mechanistic insights, we potentiated the antiviral, membrane-disrupting activity of CLR01 by introducing aliphatic ester arms into each phosphate group to act as lipid anchors that promote membrane targeting. The most potent ester modifications harbored unbranched C4 units, which engendered tweezers that were approximately one order of magnitude more effective than CLR01 and nontoxic. Thus, we establish the mechanistic basis of viral envelope disruption by specific tweezers and establish a new class of potential broad-spectrum antivirals with enhanced activity.

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches. Here, we introduce a procedure for the general-purpose numerical codification of polypeptides. This procedure transforms pairs of amino acid sequences into a machine learning-friendly vector, whose elements represent numerical descriptors of residues in proteins. We used this numerical encoding procedure for the development of a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect (https://ppi-detect.zmb.uni-due.de/) outperforms state of the art sequence-based predictors of PPI. We employed PPI-Detect for the analysis of derivatives of EPI-X4, an endogenous peptide inhibitor of CXCR4, a G-protein-coupled receptor. There, we identified with high accuracy those peptides which bind better than EPI-X4 to the receptor. Also using PPI-Detect, we designed a novel peptide and then experimentally established its anti-CXCR4 activity. © 2019 Wiley Periodicals, Inc.

Multivalent Ligands with Tailor-Made Anion Binding Motif as Stabilizers of Protein-Protein Interactions.

Modulation of protein-protein interactions (PPIs) is essential for understanding and tuning biologically relevant processes. Although inhibitors for PPIs are widely used, the field still lacks the targeted design of stabilizers. Here, we report unnatural stabilizers based on the combination of multivalency effects and the artificial building block guanidiniocarbonylpyrrol (GCP), an arginine mimetic. Unlike other GCP-based ligands that modulate PPIs in different protein targets, only a tetrameric design shows potent activity as stabilizer of the 14-3-3ζ/C-Raf and 14-3-3ζ/Tau complexes in the low-micromolar range. This evidences the role of multivalency for achieving higher specificity in the modulation of PPIs.

Mono- and Bivalent 14-3-3 Inhibitors for Characterizing Supramolecular "Lysine Wrapping" of Oligoethylene Glycol (OEG) Moieties in Proteins.

Previous studies have indicated the presence of defined interactions between oligo or poly(ethylene glycol) (OEG or PEG) and lysine residues. In these interactions, the OEG or PEG residues "wrap around" the lysine amino group, thereby enabling complexation of the amino group by the ether oxygen residues. The resulting biochemical binding affinity and thus biological relevance of this supramolecular interaction however remains unclear so far. Here, we report that OEG-containing phosphophenol ether inhibitors of 14-3-3 proteins also display such a "lysine-wrapping" binding mode. For better investigating the biochemical relevance of this binding mode, we made use of the dimeric nature of 14-3-3 proteins and designed as well as synthesized a set of bivalent 14-3-3 inhibitors for biochemical and X-ray crystallography-based structural studies. We found that all synthesized derivatives adapted the "lysine-wrapping" binding mode in the crystal structures; in solution, a different binding mode is however observed, most probably as the "lysine-wrapping" binding mode turned out to be a rather weak interaction. Accordingly, our studies demonstrate that structural studies of OEG-lysine interactions are difficult to interpret and their presence in structural studies may not automatically be correlated with a relevant interaction also in solution but requires further biochemical studies.

Novel "extended sequons" of human N-glycosylation sites improve the precision of qualitative predictions: an alignment-free study of pattern recognition using ProtDCal protein features.

N-Glycosylation is a common post-translational modification that plays an important role in the proper folding and function of many proteins. This modification is largely dependent on the presence of a sequence motif called a "sequon" defined as Asn-Xxx-Ser/Thr. However, evidence has shown that the presence of such a "sequon" is insufficient to determine the occurrence of N-glycosylation with high precision. This study aims to elucidate patterns that can more accurately predict N-glycosylation sites in human proteins. The novel motifs are evaluated using benchmarking data from 188 organisms. Performance is largely sustained compared to the human data, which validates the robustness of the novel extracted "extended sequons". We, therefore, introduce new knowledge about sequence-related factors that control N-glycosylation.

ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins.

BACKGROUND: The exponential growth of protein structural and sequence databases is enabling multifaceted approaches to understanding the long sought sequence-structure-function relationship. Advances in computation now make it possible to apply well-established data mining and pattern recognition techniques to these data to learn models that effectively relate structure and function. However, extracting meaningful numerical descriptors of protein sequence and structure is a key issue that requires an efficient and widely available solution. RESULTS: We here introduce ProtDCal, a new computational software suite capable of generating tens of thousands of features considering both sequence-based and 3D-structural descriptors. We demonstrate, by means of principle component analysis and Shannon entropy tests, how ProtDCal's sequence-based descriptors provide new and more relevant information not encoded by currently available servers for sequence-based protein feature generation. The wide diversity of the 3D-structure-based features generated by ProtDCal is shown to provide additional complementary information and effectively completes its general protein encoding capability. As demonstration of the utility of ProtDCal's features, prediction models of N-linked glycosylation sites are trained and evaluated. Classification performance compares favourably with that of contemporary predictors of N-linked glycosylation sites, in spite of not using domain-specific features as input information. CONCLUSIONS: ProtDCal provides a friendly and cross-platform graphical user interface, developed in the Java programming language and is freely available at: http://bioinf.sce.carleton.ca/ProtDCal/ . ProtDCal introduces local and group-based encoding which enhances the diversity of the information captured by the computed features. Furthermore, we have shown that adding structure-based descriptors contributes non-redundant additional information to the features-based characterization of polypeptide systems. This software is intended to provide a useful tool for general-purpose encoding of protein sequences and structures for applications is protein classification, similarity analyses and function prediction.

A Hooke׳s law-based approach to protein folding rate.

Kinetics is a key aspect of the renowned protein folding problem. Here, we propose a comprehensive approach to folding kinetics where a polypeptide chain is assumed to behave as an elastic material described by the Hooke׳s law. A novel parameter called elastic-folding constant results from our model and is suggested to distinguish between protein with two-state and multi-state folding pathways. A contact-free descriptor, named folding degree, is introduced as a suitable structural feature to study protein-folding kinetics. This approach generalizes the observed correlations between varieties of structural descriptors with the folding rate constant. Additionally several comparisons among structural classes and folding mechanisms were carried out showing the good performance of our model with proteins of different types. The present model constitutes a simple rationale for the structural and energetic factors involved in protein folding kinetics.

Global stability of protein folding from an empirical free energy function.

The principles governing protein folding stand as one of the biggest challenges of Biophysics. Modeling the global stability of proteins and predicting their tertiary structure are hard tasks, due in part to the variety and large number of forces involved and the difficulties to describe them with sufficient accuracy. We have developed a fast, physics-based empirical potential, intended to be used in global structure prediction methods. This model considers four main contributions: Two entropic factors, the hydrophobic effect and configurational entropy, and two terms resulting from a decomposition of close-packing interactions, namely the balance of the dispersive interactions of folded and unfolded states and electrostatic interactions between residues. The parameters of the model were fixed from a protein data set whose unfolding free energy has been measured at the "standard" experimental conditions proposed by Maxwell et al. (2005) and a large data set of 1151 monomeric proteins obtained from the PDB. A blind test with proteins taken from ProTherm database, at similar experimental conditions, was carried out. We found a good correlation with the test data set, proving the effectiveness of our model for predicting protein folding free energies in considered standard conditions. Such a prediction compares favorably against estimations made with FoldX's function and the force field GROMOS96. This model constitutes a valuable tool for the fast evaluation of protein structure stability in 3D structure prediction methods.

Spermine and spermidine bind CXCR4 and inhibit CXCR4- but not CCR5-tropic HIV-1 infection.

Semen is an important vector for sexual HIV-1 transmission. Although CXCR4-tropic (X4) HIV-1 may be present in semen, almost exclusively CCR5-tropic (R5) HIV-1 causes systemic infection after sexual intercourse. To identify factors that may limit sexual X4-HIV-1 transmission, we generated a seminal fluid-derived compound library and screened it for antiviral agents. We identified four adjacent fractions that blocked X4-HIV-1 but not R5-HIV-1 and found that they all contained spermine and spermidine, abundant polyamines in semen. We showed that spermine, which is present in semen at concentrations up to 14 mM, binds CXCR4 and selectively inhibits cell-free and cell-associated X4-HIV-1 infection of cell lines and primary target cells at micromolar concentrations. Our findings suggest that seminal spermine restricts sexual X4-HIV-1 transmission.

ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria.

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs' discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.

Computational Design of Inhibitors Targeting the Catalytic ß Subunit of Escherichia coli FOF1-ATP Synthase.

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic ß subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the ß subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

The role of DNA nanostructures in the catalytic properties of an allosterically regulated protease.

DNA-scaffolded enzymes typically show altered kinetic properties; however, the mechanism behind this phenomenon is still poorly understood. We address this question using thrombin, a model of allosterically regulated serine proteases, encaged into DNA origami cavities with distinct structural and electrostatic features. We compare the hydrolysis of substrates that differ only in their net charge due to a terminal residue far from the cleavage site and presumably involved in the allosteric activation of thrombin. Our data show that the reaction rate is affected by DNA/substrate electrostatic interactions, proportionally to the degree of DNA/enzyme tethering. For substrates of opposite net charge, this leads to an inversion of the catalytic response of the DNA-scaffolded thrombin when compared to its freely diffusing counterpart. Hence, by altering the electrostatic environment nearby the encaged enzyme, DNA nanostructures interfere with charge-dependent mechanisms of enzyme-substrate recognition and may offer an alternative tool to regulate allosteric processes through spatial confinement.

Engineering protein fragments via evolutionary and protein-protein interaction algorithms: de novo design of peptide inhibitors for FO F1 -ATP synthase.

Enzyme subunit interfaces have remarkable potential in drug design as both target and scaffold for their own inhibitors. We show an evolution-driven strategy for the de novo design of peptide inhibitors targeting interfaces of the Escherichia coli FoF1-ATP synthase as a case study. The evolutionary algorithm ROSE was applied to generate diversity-oriented peptide libraries by engineering peptide fragments from ATP synthase interfaces. The resulting peptides were scored with PPI-Detect, a sequence-based predictor of protein-protein interactions. Two selected peptides were confirmed by in vitro inhibition and binding tests. The proposed methodology can be widely applied to design peptides targeting relevant interfaces of enzymatic complexes.

Specific inhibition of the Survivin-CRM1 interaction by peptide-modified molecular tweezers.

Survivin's dual function as apoptosis inhibitor and regulator of cell proliferation is mediated via its interaction with the export receptor CRM1. This protein-protein interaction represents an attractive target in cancer research and therapy. Here, we report a sophisticated strategy addressing Survivin's nuclear export signal (NES), the binding site of CRM1, with advanced supramolecular tweezers for lysine and arginine. These were covalently connected to small peptides resembling the natural, self-complementary dimer interface which largely overlaps with the NES. Several biochemical methods demonstrated sequence-selective NES recognition and interference with the critical receptor interaction. These data were strongly supported by molecular dynamics simulations and multiscale computational studies. Rational design of lysine tweezers equipped with a peptidic recognition element thus allowed to address a previously unapproachable protein surface area. As an experimental proof-of-principle for specific transport signal interference, this concept should be transferable to any protein epitope with a flanking well-accessible lysine.

Inhibition of Staphylococcus aureus biofilm-forming functional amyloid by molecular tweezers.

Biofilms are rigid and largely impenetrable three-dimensional matrices constituting virulence determinants of various pathogenic bacteria. Here, we demonstrate that molecular tweezers, unique supramolecular artificial receptors, modulate biofilm formation of Staphylococcus aureus. In particular, the tweezers affect the structural and assembly properties of phenol-soluble modulin α1 (PSMα1), a biofilm-scaffolding functional amyloid peptide secreted by S. aureus. The data reveal that CLR01, a diphosphate tweezer, exhibits significant S. aureus biofilm inhibition and disrupts PSMα1 self-assembly and fibrillation, likely through inclusion of lysine side chains of the peptide. In comparison, different peptide binding occurs in the case of CLR05, a tweezer containing methylenecarboxylate units, which exhibits lower affinity for the lysine residues yet disrupts S. aureus biofilm more strongly than CLR01. Our study points to a possible role for molecular tweezers as potent biofilm inhibitors and antibacterial agents, particularly against untreatable biofilm-forming and PSM-producing bacteria, such as methicillin-resistant S. aureus.