Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques.

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8-140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.

Potent and Protease Resistant Azapeptide Agonists of the GLP-1 and GIP Receptors.

The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play important physiological roles. Stabilized agonists of the GLP-1 receptor (GLP-1R) and the GIP receptor (GIPR) for the management of diabetes and obesity have generated widespread enthusiasm and have become blockbuster drugs. These therapeutics are refractory to the action of dipeptidyl peptidase-4 (DPP4), that catalyzes rapid removal of the two N-terminal residues of the native peptides, in turn severely diminishing their activity profiles.  Here we report that a single atom change from carbon to nitrogen in the backbone of the entire peptide make them refractory to DPP4 action while still retaining full potency and efficacy at their respective receptors.  This was accomplished by use of aza-amino acids, that are bioisosteric replacements for a-amino acids that perturb the structural backbone and local side chain conformations.  Molecular dynamics simulations reveal that aza-amino acid can populate the same conformational space that GLP-1 adopts when bound to the GLP-1R. The insertion of an aza-amino acid at the second position from the N-terminus in semaglutide and in a dual agonist of GLP-1R and GIPR further demonstrates its capability as a viable alternative to current DPP4 resistance strategies while offering additional structural variety.

A Peptide Strategy for Inhibiting Different Protein Aggregation Pathways.

Protein aggregation correlates with many human diseases. Protein aggregates differ in structure and shape. Strategies to develop effective aggregation inhibitors that reach the clinic failed so far. Here, we developed a family of peptides targeting early aggregation stages for both amorphous and fibrillar aggregates of proteins unrelated in sequence and structure. They act on dynamic precursors before mechanistic differentiation takes place. Using peptide arrays, we first identified peptides inhibiting the amorphous aggregation of a molten globular, aggregation-prone mutant of the Axin tumor suppressor. Optimization revealed that the peptides activity did not depend on their sequences but rather on their molecular determinants: a composition of 20-30 % flexible, 30-40 % aliphatic and 20-30 % aromatic residues, a hydrophobicity/hydrophilicity ratio close to 1, and an even distribution of residues of different nature throughout the sequence. The peptides also suppressed fibrillation of Tau, a disordered protein that forms amyloids in Alzheimer's disease, and slowed down that of Huntingtin Exon1, an amyloidogenic protein in Huntington's disease, both entirely unrelated to Axin. Our compounds thus target early stages of different aggregation mechanisms, inhibiting both amorphous and amyloid aggregation. Such cross-mechanistic, multi-targeting aggregation inhibitors may be lead compounds for developing drug candidates against various protein aggregation diseases.

Assembly-Induced Membrane Selectivity of Artificial Model Peptides through Entropy-Enthalpy Competition.

Peptide design and drug development offer a promising solution for combating serious diseases or infections. In this study, using an AI-human negotiation approach, we have designed a class of minimal model peptides against tuberculosis (TB), among which K7W6 exhibits potent efficacy attributed to its assembly-induced function. Comprising lysine and tryptophan with an amphiphilic α-helical structure, the K7W6 sequence exhibits robust activity against various infectious bacteria causing TB (including clinically isolated and drug-resistant strains) both in vitro and in vivo. Moreover, it synergistically enhances the effectiveness of the first-line antibiotic rifampicin while displaying low potential for inducing drug resistance and minimal toxicity toward mammalian cells. Biophysical experiments and simulations elucidate that K7W6's exceptional performance can be ascribed to its highly selective and efficient membrane permeabilization activity induced by its distinctive self-assembly behavior. Additionally, these assemblies regulate the interplay between enthalpy and entropy during K7W6-membrane interaction, leading to the peptide's two-step mechanism of membrane interaction. These findings provide valuable insights into rational design principles for developing advanced peptide-based drugs while uncovering the functional role played by assembly.

De novo synthetic antimicrobial peptide design with a recurrent neural network.

Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors.

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Influence of antimicrobial peptides on the bacterial membrane curvature and vice versa.

Many factors contribute to bacterial membrane stabilization, including steric effects between lipids, membrane spontaneous curvature, and the difference in the number of neighboring molecules. This forum provides an overview of the physicochemical properties associated with membrane curvature and how this parameter can be tuned to design more effective antimicrobial peptides.

Structure-aware machine learning strategies for antimicrobial peptide discovery.

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design.

Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter hurdles due to the complexities and costs of laboratory experiments. Recent advancements in deep learning and Bayesian Optimization have paved the way for innovative research in this domain. In this context, our study presents a novel approach that effectively combines protein structure prediction with Bayesian Optimization for peptide design. By applying carefully designed objective functions, we guide and enhance the optimization trajectory for new peptide sequences. Benchmarked against multiple native structures, our methodology is tailored to generate new peptides to their optimal potential biological properties.

Function and Mechanism of Antiviral Wasp Venom Peptide Protopolybia-MP III and Its Derivatives against HSV-1.

Viruses are one of the leading causes of human disease, and many highly pathogenic viruses still have no specific treatment drugs. Therefore, producing new antiviral drugs is an urgent matter. In our study, we first found that the natural wasp venom peptide Protopolybia-MP III had a significant inhibitory effect on herpes simplex virus type 1 (HSV-1) replication in vitro by using quantitative real-time PCR (qPCR), Western blotting, and plaque-forming assays. Immunofluorescence analysis showed Protopolybia-MP III could enter cells, and it inhibited multiple stages of the HSV-1 life cycle, including the attachment, entry/fusion, and post-entry stages. Furthermore, ultracentrifugation and electron microscopy detected that Protopolybia-MP III significantly suppressed HSV-1 virion infectivity at different temperatures by destroying the integrity of the HSV-1 virion. Finally, by comparing the antiviral activity of Protopolybia-MP III and its mutants, a series of peptides with better anti-HSV-1 activity were identified. Overall, this work found the function and mechanism of the antiviral wasp venom peptide Protopolybia-MP III and its derivatives against HSV-1 and laid the foundation for the research and development of wasp venom-derived antiviral candidate peptide drugs.

Cationicity Enhancement on the Hydrophilic Face of Ctriporin Significantly Reduces Its Hemolytic Activity and Improves the Antimicrobial Activity against Antibiotic-Resistant ESKAPE Pathogens.

The ESKAPE pathogen-associated antimicrobial resistance is a global public health issue, and novel therapeutic strategies are urgently needed. The short cationic antimicrobial peptide (AMP) family represents an important subfamily of scorpion-derived AMPs, but high hemolysis and poor antimicrobial activity hinder their therapeutic application. Here, we recomposed the hydrophilic face of Ctriporin through lysine substitution. We observed non-linear correlations between the physiochemical properties of the peptides and their activities, and significant deviations regarding the changes of antimicrobial activities against different bacterial species, as well as hemolytic activity. Most importantly, we obtained two Ctriporin analogs, CM5 and CM6, these two have significantly reduced hemolytic activity and more potent antimicrobial activities against all tested antibiotic-resistant ESKAPE pathogens. Fluorescence experiments indicated they may perform the bactericidal function through a membrane-lytic action model. Our work sheds light on the potential of CM5 and CM6 in developing novel antimicrobials and gives clues for optimizing peptides from the short cationic AMP family.

In silico design of potential Mcl-1 peptide-based inhibitors.

CONTEXT: BIM (Bcl-2 interacting mediator of apoptosis)-derived peptides that specifically target over-expressed Mcl-1 (myeloid cell leukemia-1) protein and induce apoptosis are potentially anti-cancer agents. Since the helicity of BIM-derived peptides has a crucial role in their functionality, a range of strategies have been used to increase the helicity including the introduction of unnatural residues and stapling methods that have some drawbacks such as the accumulation in the liver. To avoid these drawbacks, this study aimed to design a more helical peptide by utilizing bioinformatics algorithms and molecular dynamics simulations without exploiting unnatural residues and stapling methods. MM-PBSA results showed that the mutations of A4fE and A2eE in analogue 5 demonstrate a preference towards binding with Mcl-1. As evidenced by Circular dichroism results, the helicity increases from 18 to 34%, these findings could enhance the potential of analogue 5 as an anti-cancer agent targeting Mcl-1. The applied strategies in this research could shed light on the in silico peptide design. Moreover, analogue 5 as a drug candidate can be evaluated in vitro and in vivo studies. METHODS: The sequence of the lead peptide was determined using the ApInAPDB database and PRALINE program. Contact finder and PDBsum web server softwares were used to determine the contact involved amino acids in complex with Mcl-1. All identified salt bridge contributing residues were unaltered to preserve the binding affinity. After proposing novel analogues, their secondary structures were predicted by Cham finder web server software and GOR, Neural Network, and Chou-Fasman algorithms. Finally, molecular dynamics simulations run for 100 ns were done using the GROMACS, version 5.0.7, with the CHARMM36 force field. MM-PBSA was used to assess binding affinity specificity in targeting Mcl-1 and Bcl-xL (B-cell lymphoma extra-large).

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Short Fragmented Peptides from Pardachirus Marmoratus Exhibit Stronger Anticancer Activities in In Silico Residue Replacement and Analyses.

BACKGROUND: Cancer is a worldwide issue. It has been observed that conventional therapies face many problems, such as side effects and drug resistance. Recent research reportedly used marine-derived products to treat various diseases and explored their potential in treating cancers. OBJECTIVE: This study aims to discover short-length anticancer peptides derived from pardaxin 6 through an in silico approach. METHODS: Fragmented peptides ranging from 5 to 15 amino acids were derived from the pardaxin 6 parental peptide. These peptides were further replaced with one residue and, along with the original fragmented peptides, were predicted for their SVM scores and physicochemical properties. The top 5 derivative peptides were further examined for their toxicity, hemolytic probability, peptide structures, docking models, and energy scores using various web servers. The trend of in silico analysis outputs across 5 to 15 amino acid fragments was further analyzed. RESULTS: Results showed that when the amino acids were increased, SVM scores of the original fragmented peptides were also increased. Designed peptides had increased SVM scores, which was aligned with previous studies where the single residue replacement transformed the non-anticancer peptide into an anticancer agent. Moreover, in vitro studies validated that the designed peptides retained or enhanced anticancer effects against different cancer cell lines. Interestingly, a decreasing trend was observed in those fragmented derivative peptides. CONCLUSION: Single residue replacement in fragmented pardaxin 6 was found to produce stronger anticancer agents through in silico predictions. Through bioinformatics tools, fragmented peptides improved the efficiency of marine-derived drugs with higher efficacy and lower hemolytic effects in treating cancers.

The amphipathic design in helical antimicrobial peptides.

Amphipathicity is a critical characteristic of helical antimicrobial peptides (AMPs). The hydrophilic region, primarily composed of cationic residues, plays a pivotal role in the initial binding to negatively charged components on bacterial membranes through electrostatic interactions. Subsequently, the hydrophobic region interacts with hydrophobic components, inducing membrane perturbation, ultimately leading to cell death, or inhibiting intracellular function. Due to the extensive diversity of natural and synthetic AMPs with regard to the design of amphipathicity, it is complicated to study the structure-activity relationships. Therefore, this work aims to categorize the common amphipathic design and investigate their impact on the biological properties of AMPs. Besides, the connection between current structural modification approaches and amphipathic styles was also discussed.

The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides.

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting with bacterial membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) with AMP-like properties could exhibit antimicrobial activity. We tested 14 peptides from different IDRs predicted to have antimicrobial activity and found that nearly all of them did not display the anticipated effects. These peptides failed to adopt a defined secondary structure and had compromised membrane interactions, resulting in a lack of antimicrobial activity. We hypothesize that evolutionary constraints may prevent IDRs from folding, even in membrane-like environments, limiting their antimicrobial potential. Moreover, our research reveals that current antimicrobial predictors fail to accurately capture the structural features of peptides when dealing with intrinsically unstructured sequences. Hence, the results presented here may have far-reaching implications for designing and improving antimicrobial strategies and therapies against infectious diseases.

Structural Analyses of Designed α-Helix and ß-Sheet Peptide Nanofibers Using Solid-State Nuclear Magnetic Resonance and Cryo-Electron Microscopy and Introduction of Structure-Based Metal-Responsive Properties.

The 21-residue peptide α3, which is artificially designed and consists of three repeats of 7 residues, is known to rapidly assemble into the α-helix nanofiber. However, its molecular structure within the fiber has not yet been fully elucidated. Thus, we conducted a thorough investigation of the fiber's molecular structure using solid-state NMR and other techniques. The molecules were found to be primarily composed of the α-helix structure, with some regions near the C- and N-terminal adopting a 310-helix structure. Furthermore, it was discovered that ß-sheet hydrogen bonds were formed between the molecules at both ends. These intermolecular interactions caused the molecules to assemble parallelly in the same direction, forming helical fibers. In contrast, we designed two molecules, CaRP2 and ßKE, that can form ß-sheet intermolecular hydrogen bonds using the entire molecule instead of just the ends. Cryo-EM and other measurements confirmed that the nanofibers formed in a cross ß structure, albeit at a slow rate, with the formation times ranging from 1 to 42 days. To create peptide nanofibers that instantaneously respond to changes in the external environment, we designed several molecules (HDM1-3) based on α3 by introducing metal-binding sites. One of these molecules was found to be highly responsive to the addition of metal ions, inducing α-helix formation and simultaneously assembling into nanofibers. The nanofibers lost their structure upon removal of the metal ion. The change occurred promptly and was reversible, demonstrating that the intended level of responsiveness was attained.

A Structure-Activity Relationship Study of SNAIL1 Peptides as Inhibitors of Lysine-Specific Demethylase 1.

Peptides have recently garnered attention as middle-molecular-weight drugs with the characteristics of small molecules and macromolecules. Lysine-specific demethylase 1 (LSD1) is a potential therapeutic target for lung cancer, neuroblastoma, and leukemia, and some peptide-based LSD1 inhibitors designed based on the N-terminus of SNAIL1, a member of the SNAIL/SCRATCH family of transcription factors, have been reported. The N-terminus of SNAIL1 peptide acts as a cap of the catalytic site of LSD1, inhibiting interactions with LSD1. However, the structure-activity relationship (SAR) of these inhibitors is not yet fully understood. Therefore, in the present study, we aimed to uncover the SAR and to identify novel SNAIL1 peptide-based LSD1 inhibitors. We synthesized peptide inhibitor candidates based on truncating the N-terminus of SNAIL1 or substituting its amino acid residues. In the truncation study, we found that SNAIL1 1-16 (2), which was composed of 16 residues, strongly inhibited LSD1. Furthermore, we investigated the SAR at residues-3 and -5 from the N-terminus and found that peptides 2j and 2k, in which leucine 5 of the parent peptide is substituted with unnatural amino acids, cyclohexylalanine and norleucine, respectively, strongly inhibited LSD1. This result suggests that the hydrophobic interaction between the inhibitor peptides and LSD1 affects the LSD1-inhibitory activity. We believe that this SAR information provides a basis for the development of more potent LSD1 inhibitors.