Defining the hydrophobic interactions that drive competence stimulating peptide (CSP)-ComD binding in Streptococcus pneumoniae

Quorum sensing (QS) is a cell–cell communication mechanism that enables bacteria to assess their population density and alter their behavior upon reaching high cell number. Many bacterial pathogens utilize QS to initiate an attack on their host, thus QS has attracted significant attention as a potential antivirulence alternative to traditional antibiotics. Streptococcus pneumoniae, a notorious human pathogen responsible for a variety of acute and chronic infections, utilizes the competence regulon and its associated signaling peptide, the competence stimulating peptide (CSP), to acquire antibiotic resistance and establish an infection. In this work, we sought to define the binding pockets within the ComD1 receptor used for binding the hydrophobic side-chain residues in CSP1 through the introduction of highly-conservative point mutations within the peptide. Optimization of these binding interactions could lead to the development of highly potent CSP-based QS modulators while the inclusion of non-natural amino acids within the CSP sequence would confer resistance to protease degradation, a requirement for drug candidates.

Kinetics of the multitasking high-affinity Win binding site of WDR5 in restricted and unrestricted conditions

Recent advances in quantitative proteomics show that WD40 proteins play a pivotal role in numerous cellular networks. Yet, they have been fairly unexplored and their physical associations with other proteins are ambiguous. A quantitative understanding of these interactions has wide-ranging significance. WD40 repeat protein 5 (WDR5) interacts with all members of human SET1/MLL methyltransferases, which regulate methylation of the histone 3 lysine 4 (H3K4). Here, using real-time binding measurements in a high-throughput setting, we identified the kinetic fingerprint of transient associations between WDR5 and 14-residue WDR5 interaction (Win) motif peptides of each SET1 protein (SET1Win). Our results reveal that the high-affinity WDR5-SET1Win interactions feature slow association kinetics. This finding is likely due to the requirement of SET1Win to insert into the narrow WDR5 cavity, also named the Win binding site. Furthermore, our explorations indicate fairly slow dissociation kinetics. This conclusion is in accordance with the primary role of WDR5 in maintaining the functional integrity of a large multisubunit complex, which regulates the histone methylation. Because the Win binding site is considered a key therapeutic target, the immediate outcomes of this study could form the basis for accelerated developments in medical biotechnology.

Moonlighting Biochemistry of Cysteine Synthase: A Species-specific Global Regulator

Cysteine Synthase (CS), the enzyme that synthesizes cysteine, performs non-canonical regulatory roles by binding and modulating functions of disparate proteins. Beyond its role in catalysis and regulation in the cysteine biosynthesis pathway, it exerts its moonlighting effect by binding to few other proteins which possess a C-terminal "CS-binding motif", ending with a terminal ILE. Therefore, we hypothesized that CS might regulate many other disparate proteins with the "CS-binding motif". In this study, we developed an iterative sequence matching method for mapping moonlighting biochemistry of CS and validated our prediction by analytical and structural approaches. Using a minimal protein-peptide interaction system, we show that five previously unknown CS-binder proteins that participate in diverse metabolic processes interact with CS in a species-specific manner. Furthermore, results show that signatures of protein-protein interactions, including thermodynamic, competitive-inhibition, and structural features, highly match the known CS-Binder, serine acetyltransferase (SAT). Together, the results presented in this study allow us to map the extreme multifunctional space (EMS) of CS and reveal the biochemistry of moonlighting space, a subset of EMS. We believe that the integrated computational and experimental workflow developed here could be further modified and extended to study protein-specific moonlighting properties of multifunctional proteins.

Identification of Aethina tumida Kir Channels as Putative Targets of the Bee Venom Peptide Tertiapin Using Structure-Based Virtual Screening Methods

Venoms are comprised of diverse mixtures of proteins, peptides, and small molecules. Identifying individual venom components and their target(s) with mechanism of action is now attainable to understand comprehensively the effectiveness of venom cocktails and how they collectively function in the defense and predation of an organism. Here, structure-based computational methods were used with bioinformatics tools to screen and identify potential biological targets of tertiapin (TPN), a venom peptide from Apis mellifera (European honey bee). The small hive beetle (Aethina tumida (A. tumida)) is a natural predator of the honey bee colony and was found to possess multiple inwardly rectifying K⁺ (Kir) channel subunit genes from a genomic BLAST search analysis. Structure-based virtual screening of homology modelled A. tumida Kir (atKir) channels found TPN to interact with a docking profile and interface “footprint” equivalent to known TPN-sensitive mammalian Kir channels. The results support the hypothesis that atKir channels, and perhaps other insect Kir channels, are natural biological targets of TPN that help defend the bee colony from infestations by blocking K⁺ transport via atKir channels. From these in silico findings, this hypothesis can now be subsequently tested in vitro by validating atKir channel block as well as in vivo TPN toxicity towards A. tumida. This study highlights the utility and potential benefits of screening in virtual space for venom peptide interactions and their biological targets, which otherwise would not be feasible.

Novel Polymyxin-Inspired Peptidomimetics Targeting the SARS-CoV-2 Spike:hACE2 Interface

Though the bulk of the COVID-19 pandemic is behind, the search for effective and safe anti-SARS-CoV-2 drugs continues to be relevant. A highly pursued approach for antiviral drug development involves targeting the viral spike (S) protein of SARS-CoV-2 to prevent its attachment to the cellular receptor ACE2. Here, we exploited the core structure of polymyxin B, a naturally occurring antibiotic, to design and synthesize unprecedented peptidomimetics (PMs), intended to target contemporarily two defined, non-overlapping regions of the S receptor-binding domain (RBD). Monomers 1, 2, and 8, and heterodimers 7 and 10 bound to the S-RBD with micromolar affinity in cell-free surface plasmon resonance assays (KD ranging from 2.31 μM to 2.78 μM for dimers and 8.56 μM to 10.12 μM for monomers). Although the PMs were not able to fully protect cell cultures from infection with authentic live SARS-CoV-2, dimer 10 exerted a minimal but detectable inhibition of SARS-CoV-2 entry in U87.ACE2+ and A549.ACE2.TMPRSS2+ cells. These results validated a previous modeling study and provided the first proof-of-feasibility of using medium-sized heterodimeric PMs for targeting the S-RBD. Thus, heterodimers 7 and 10 may serve as a lead for the development of optimized compounds, which are structurally related to polymyxin, with improved S-RBD affinity and anti-SARS-CoV-2 potential.

Domain Analysis and Motif Matcher (DAMM): A Program to Predict Selectivity Determinants in Monosiga brevicollis PDZ Domains Using Human PDZ Data

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 μM affinity, a value commonly considered the threshold for cellular PDZ-peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution.

Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions

INTRODUCTION

Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.

AREAS COVERED

We overview 20 years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides, and lipids. These methods range from simple discriminant analysis to sophisticated deep transformer networks that use protein language models. They generate relatively accurate predictions as evidenced by the results of a recently published community-driven assessment.

EXPERT OPINION

MoRFs prediction is a mature field of research that is poised to continue at a steady pace in the foreseeable future. We anticipate further expansion of the scope of MoRF predictions to additional partner molecules, such as nucleic acids, and continued use of recent machine learning advances. Other future efforts should concentrate on improving availability of MoRF predictions by releasing, maintaining, and popularizing web servers and by depositing MoRF predictions to large databases of protein structure and function predictions. Furthermore, accurate MoRF predictions should be coupled with the equally accurate prediction and modeling of the resulting structures of complexes.

Performance of Hybrid Strategies Combining MDockPP and AlphaFold2 in CAPRI Rounds 47-55

CAPRI challenges offer a range of blind tests for biomolecule interaction prediction. This study evaluates the performance of our prediction protocols for the human group Zou and the server group MDockPP in CAPRI rounds 47-55, highlighting the impact of AlphaFold2 (AF2) and the effectiveness of massive sampling approaches. Prior to AlphaFold2's release, our methods relied on homology modeling and docking-based protocols, achieving limited accuracy due to constraints in structural templates and inherent docking limitations. After AlphaFold2's public release, which demonstrated breakthrough accuracy in protein structure prediction, we integrated its multimer models and massive sampling techniques into our protocols. This integration significantly improved prediction accuracy, with human predictions increasing from 1 correct interface of 19 pre-AlphaFold2 to 4 of 8 post-AlphaFold2. The massive sampling approach further enhanced performance, particularly for targets T231 and T233, yielding medium-quality models that default parameters could not achieve.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

In silico screening of protein-binding peptides with an application to developing peptide inhibitors against antibiotic resistance

The field of therapeutic peptides is experiencing a surge, fueled by their advantageous features. These include predictable metabolism, enhanced safety profile, high selectivity, and reduced off-target effects compared with small-molecule drugs. Despite progress in addressing limitations associated with peptide drugs, a significant bottleneck remains: the absence of a large-scale in silico screening method for a given protein target structure. Such methods have proven invaluable in accelerating small-molecule drug discovery. The high flexibility of peptide structures and the large diversity of peptide sequences greatly hinder the development of urgently needed computational methods. Here, we report a method called MDockPeP2_VS to address these challenges. It integrates molecular docking with structural conservation between protein folding and protein-peptide binding. Briefly, we discovered that when the interfacial residues are conserved, a sequence fragment derived from a monomeric protein exhibits a high propensity to bind a target protein with a similar conformation. This valuable insight significantly reduces the search space for peptide conformations, resulting in a substantial reduction in computational time and making in silico peptide screening practical. We applied MDockPeP2_VS to develop peptide inhibitors targeting the TEM-1 β-lactamase of Escherichia coli, a key mechanism behind antibiotic resistance in gram-negative bacteria. Among the top 10 peptides selected from in silico screening, TF7 (KTYLAQAAATG) showed significant inhibition of β-lactamase activity with a K i value of 1.37 ± 0.37 µM. This fully automated, large-scale structure-based in silico peptide screening software is available for free download at https://zougrouptoolkit.missouri.edu/mdockpep2_vs/download.html.