Empirical estimation of local dielectric constants: Toward atomistic design of collagen mimetic peptides

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Impact of vascular Ehlers-Danlos Syndrome-associated Gly substitutions on structure, function, and mechanics using bacterial collagen

Vascular Ehlers-Danlos syndrome (vEDS) arises from mutations in collagen-III, a major structural component of the extracellular matrix (ECM) in vascularized tissues, including blood vessels. Fibrillar collagens form a triple-helix that is characterized by a canonical (Gly-X-Y)n sequence. The substitution of another amino acid for Gly within this conserved repeating sequence is associated with several hereditary connective tissue disorders, including vEDS. The clinical severity of vEDS depends on the identity of the substituted amino acid and its location. In this study, we engineered recombinant bacterial collagen-like proteins (CLPs) with previously reported Gly→X (X=Ser or Arg) vEDS substitutions within the integrin-binding site. Employing a combination of biophysical techniques, enzymatic digestion assays, integrin binding affinity assays, and computational modeling, we assessed the impact of Gly→X substitutions on structure, stability, function, and mechanical properties. While constructs with Ser or Arg substitutions maintained a triple-helix structure, Arg substitution significantly reduced global thermal stability, heightened susceptibility to trypsin digestion, and altered integrin α2-inserted (α2I) domain binding. Molecular dynamics (MD) simulations also demonstrated distinct effects of different Gly substitutions on the triple-helix structure - Arg substitutions induced notable bulging at the substitution site and disrupted interchain hydrogen bonds compared to Ser substitutions. Additionally, steered MD simulations revealed that Arg substitution led to a significant decrease in the Young's modulus of the triple-helix. Bacterial CLPs have proved to be a powerful model for studying the underlying mechanisms of vEDS-causing mutations in collagen-III. Serine and arginine substitutions differentially perturb cell-matrix interactions and ECM in a manner consistent with clinical vEDS severity.

Preferred microenvironments of halogen bonds and hydrogen bonds revealed using statistics and QM/MM calculation studies

Hydrogen bonds (HBs) and halogen bonds (XBs) are two essential non-covalent interactions for molecular recognition and drug design. As proteins are heterogeneous in structure, the microenvironments of protein structures should have effects on the formation of HBs and XBs with ligands. However, there are no systematic studies reported on this effect to date. For quantitatively describing protein microenvironments, we defined the local hydrophobicities (LHs) and local dielectric constants (LDCs) in this study. With the defined parameters, we conducted an elaborate database survey on the basis of 22 011 ligand-protein structures to explore the microenvironmental preference of HBs (91 966 in total) and XBs (1436 in total). The statistics show that XBs prefer hydrophobic microenvironments compared to HBs. The polar residues like ASP are more likely to form HBs with ligands, while nonpolar residues such as PHE and MET prefer XBs. Both the LHs and LDCs (10.69 ± 4.36 for HBs; 8.86 ± 4.00 for XBs) demonstrate that XBs are prone to hydrophobic microenvironments compared with HBs with significant differences (p < 0.001), indicating that evaluating their strengths in the corresponding environments should be necessary. Quantum Mechanics-Molecular Mechanics (QM/MM) calculations reveal that in comparison with vacuum environments, the interaction energies of HBs and XBs are decreased to varying degrees given different microenvironments. In addition, the strengths of HBs are impaired more than those of XBs when the local dielectric constant's difference between the XB microenvironments and the HB microenvironments is large.

More than skin deep: cyclic peptides as wound healing and cytoprotective compounds

The prevalence and cost of wounds pose a challenge to patients as well as the healthcare system. Wounds can involve multiple tissue types and, in some cases, become chronic and difficult to treat. Comorbidities may also decrease the rate of tissue regeneration and complicate healing. Currently, treatment relies on optimizing healing factors rather than administering effective targeted therapies. Owing to their enormous diversity in structure and function, peptides are among the most prevalent and biologically important class of compounds and have been investigated for their wound healing bioactivities. A class of these peptides, called cyclic peptides, confer stability and improved pharmacokinetics, and are an ideal source of wound healing therapeutics. This review provides an overview of cyclic peptides that have been shown to promote wound healing in various tissues and in model organisms. In addition, we describe cytoprotective cyclic peptides that mitigate ischemic reperfusion injuries. Advantages and challenges in harnessing the healing potential for cyclic peptides from a clinical perspective are also discussed. Cyclic peptides are a potentially attractive category of wound healing compounds and more research in this field could not only rely on design as mimetics but also encompass de novo approaches as well.

Controlling the Trimerization of the Collagen Triple-Helix by Solvent Switching

Collagen hybridizing peptides (CHPs) are a powerful tool for targeting collagen damage in pathological tissues due to their ability to specifically form a hybrid collagen triple-helix with the denatured collagen chains. However, CHPs have a strong tendency to self-trimerize, requiring preheating or complicated chemical modifications to dissociate their homotrimers into monomers, which hinders their applications. To control the self-assembly of CHP monomers, we evaluated the effects of 22 cosolvents on the triple-helix structure: unlike typical globular proteins, the CHP homotrimers (as well as the hybrid CHP-collagen triple helix) cannot be destabilized by the hydrophobic alcohols and detergents (e.g., SDS) but can be effectively dissociated by the cosolvents that dominate hydrogen bonds (e.g., urea, guanidinium salts, and hexafluoroisopropanol). Our study provided a reference for the solvent effects on natural collagen and a simple effective solvent-switch method, enabling CHP utilization in automated histopathology staining and in vivo imaging and targeting of collagen damage.

Design of a minimal di-nickel hydrogenase peptide

Ancestral metabolic processes involve the reversible oxidation of molecular hydrogen by hydrogenase. Extant hydrogenase enzymes are complex, comprising hundreds of amino acids and multiple cofactors. We designed a 13-amino acid nickel-binding peptide capable of robustly producing molecular hydrogen from protons under a wide variety of conditions. The peptide forms a di-nickel cluster structurally analogous to a Ni-Fe cluster in [NiFe] hydrogenase and the Ni-Ni cluster in acetyl-CoA synthase, two ancient, extant proteins central to metabolism. These experimental results demonstrate that modern enzymes, despite their enormous complexity, likely evolved from simple peptide precursors on early Earth.

Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device

Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.

Computational design of a sensitive, selective phase-changing sensor protein for the VX nerve agent

The VX nerve agent is one of the deadliest chemical warfare agents. Specific, sensitive, real-time detection methods for this neurotoxin have not been reported. The creation of proteins that use biological recognition to fulfill these requirements using directed evolution or library screening methods has been hampered because its toxicity makes laboratory experimentation extraordinarily expensive. A pair of VX-binding proteins were designed using a supercharged scaffold that couples a large-scale phase change from unstructured to folded upon ligand binding, enabling fully internal binding sites that present the maximum surface area possible for high affinity and specificity in target recognition. Binding site residues were chosen using a new distributed evolutionary algorithm implementation in protCAD. Both designs detect VX at parts per billion concentrations with high specificity. Computational design of fully buried molecular recognition sites, in combination with supercharged phase-changing chassis proteins, enables the ready development of a new generation of small-molecule biosensors.

Recent Advances in Collagen Mimetic Peptide Structure and Design

Collagen mimetic peptides (CMPs) fold into a polyproline type II triple helix, allowing the study of the structure and function (or misfunction) of the collagen family of proteins. This Perspective will focus on recent developments in the use of CMPs toward understanding the structure and controlling the stability of the triple helix. Triple helix assembly is influenced by various factors, including the single amino acid propensity for the triple helix fold, pairwise interactions between these amino acids, and long-range effects observed across the helix, such as bend, twist, and fraying. Important progress in creating a comprehensive and predictive understanding of these factors for peptides with exclusively natural amino acids has been made. In contrast, several groups have successfully developed unnatural amino acids that are engineered to stabilize the triple helical structure. A third approach to controlling the triple helical structure includes covalent cross-linking of the triple helix to stabilize the assembly, which eliminates the problematic equilibrium of unfolding into monomers and enforces compositional control. Advances in all these areas have resulted in significant improvements to our understanding and control of this important class of protein, allowing for the design and application of more chemically complex and well-controlled collagen mimetic biomaterials.

De novo metalloprotein design

Natural metalloproteins perform many functions - ranging from sensing to electron transfer and catalysis - in which the position and property of each ligand and metal, is dictated by protein structure. De novo protein design aims to define an amino acid sequence that encodes a specific structure and function, providing a critical test of the hypothetical inner workings of (metallo)proteins. To date, de novo metalloproteins have used simple, symmetric tertiary structures - uncomplicated by the large size and evolutionary marks of natural proteins - to interrogate structure-function hypotheses. In this Review, we discuss de novo design applications, such as proteins that induce complex, increasingly asymmetric ligand geometries to achieve function, as well as the use of more canonical ligand geometries to achieve stability. De novo design has been used to explore how proteins fine-tune redox potentials and catalyse both oxidative and hydrolytic reactions. With an increased understanding of structure-function relationships, functional proteins including O2-dependent oxidases, fast hydrolases, and multi-proton/multi-electron reductases, have been created. In addition, proteins can now be designed using xeno-biological metals or cofactors and principles from inorganic chemistry to derive new-to-nature functions. These results and the advances in computational protein design suggest a bright future for the de novo design of diverse, functional metalloproteins.

Biophysical analysis of the structural evolution of substrate specificity in RuBisCO

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the most abundant enzyme on Earth. However, its catalytic rate per molecule of protein is extremely slow and the binding of the primary substrate, CO₂, is competitively displaced by O_2. Hence, carbon fixation by RuBisCO is highly inefficient; indeed, in higher C3 plants, about 30% of the time the enzyme mistakes CO₂ for O₂ Using genomic and structural analysis, we identify regions around the catalytic site that play key roles in discriminating between CO₂ and O₂ Our analysis identified positively charged cavities directly around the active site, which are expanded as the enzyme evolved with higher substrate specificity. The residues that extend these cavities have recently been under selective pressure, indicating that larger charged pockets are a feature of modern RuBisCOs, enabling greater specificity for CO₂ This paper identifies a key structural feature that enabled the enzyme to evolve improved CO₂ sequestration in an oxygen-rich atmosphere and may guide the engineering of more efficient RuBisCOs.

Design of a Fe4 S4 cluster into the core of a de novo four-helix bundle

We explore the capacity of the de novo protein, S824, to incorporate a multinuclear iron-sulfur cluster within the core of a single-chain four-helix bundle. This topology has a high intrinsic designability because sequences are constrained largely by the pattern of hydrophobic and hydrophilic amino acids, thereby allowing for the extensive substitution of individual side chains. Libraries of novel proteins based on these constraints have surprising functional potential and have been shown to complement the deletion of essential genes in E. coli. Our structure-based design of four first-shell cysteine ligands, one per helix, in S824 resulted in successful incorporation of a cubane Fe₄ S₄ cluster into the protein core. A number of challenges were encountered during the design and characterization process, including nonspecific metal-induced aggregation and the presence of competing metal-cluster stoichiometries. The introduction of buried iron-sulfur clusters into the helical bundle is an initial step toward converting libraries of designed structures into functional de novo proteins with catalytic or electron-transfer functionalities.

Recent Advances and Computational Approaches in Peptide Drug Discovery

BACKGROUND

Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases.

METHODS

There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor - ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides.

RESULTS

Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties.

CONCLUSION

This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

Comparative dynamics of tropomyosin in vertebrates and invertebrates

Tropomyosin (Tpm) is an extended α-helical coiled-coil homodimer that regulates actinomyosin interactions in muscle. Molecular simulations of four Tpms, two from the vertebrate class Mammalia (rat and pig), and two from the invertebrate class Malacostraca (shrimp and lobster), showed that despite extensive sequence and structural homology across metazoans, dynamic behavior-particularly long-range structural fluctuations-were clearly distinct. Vertebrate Tpms were more flexible and sampled complex, multi-state conformational landscapes. Invertebrate Tpms were more rigid, sampling a highly constrained harmonic landscape. Filtering of trajectories by principle component analysis into essential subspaces showed significant overlap within but not between phyla. In vertebrate Tpms, hinge-regions decoupled long-range interhelical motions and suggested distinct domains. In contrast, crustacean Tpms did not exhibit long-range dynamic correlations-behaving more like a single rigid rod on the nanosecond time scale. These observations suggest there may be divergent mechanisms for Tpm binding to actin filaments, where conformational flexibility in mammalian Tpm allows a preorganized shape complementary to the filament surface, and where rigidity in the crustacean Tpm requires concerted bending and binding.

Structural and Dynamic Properties of Allergen and Non-Allergen Forms of Tropomyosin

To what extent do structural and biophysical features of food allergen proteins distinguish them from other proteins in our diet? Invertebrate tropomyosins (Tpms) as a class are considered "pan-allergens," inducing food allergy to shellfish and respiratory allergy to dust mites. Vertebrate Tpms are not known to elicit allergy or cross-reactivity, despite their high structural similarity and sequence identity to invertebrate homologs. We expect allergens are sufficiently stable against gastrointestinal proteases to survive for immune sensitization in the intestines, and that proteolytic stability will correlate with thermodynamic stability. Thermal denaturation of shrimp Tpm shows that it is more stable than non-allergen vertebrate Tpm. Shrimp Tpm is also more resistant to digestion. Molecular dynamics uncover local dynamics that select epitopes and global differences in flexibility between shrimp and pig Tpm that discriminate allergens from non-allergens. Molecular determinants of allergenicity depend not only on sequence but on contributions of protein structure and dynamics.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Computational protein design for given backbone: recent progresses in general method-related aspects

To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.

Molecular Self-Assembly Strategy for Generating Catalytic Hybrid Polypeptides

Recently, catalytic peptides were introduced that mimicked protease activities and showed promising selectivity of products even in organic solvents where protease cannot perform well. However, their catalytic efficiency was extremely low compared to natural enzyme counterparts presumably due to the lack of stable tertiary fold. We hypothesized that assembling these peptides along with simple hydrophobic pockets, mimicking enzyme active sites, could enhance the catalytic activity. Here we fused the sequence of catalytic peptide CP4, capable of protease and esterase-like activities, into a short amyloidogenic peptide fragment of Aβ. When the fused CP4-Aβ construct assembled into antiparallel β-sheets and amyloid fibrils, a 4.0-fold increase in the hydrolysis rate of p-nitrophenyl acetate (p-NPA) compared to neat CP4 peptide was observed. The enhanced catalytic activity of CP4-Aβ assembly could be explained both by pre-organization of a catalytically competent Ser-His-acid triad and hydrophobic stabilization of a bound substrate between the triad and p-NPA, indicating that a design strategy for self-assembled peptides is important to accomplish the desired functionality.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

Metal Stabilization of Collagen and de Novo Designed Mimetic Peptides

We explore the design of metal binding sites to modulate triple-helix stability of collagen and collagen-mimetic peptides. Globular proteins commonly utilize metals to connect tertiary structural elements that are well separated in sequence, constraining structure and enhancing stability. It is more challenging to engineer structural metals into fibrous protein scaffolds, which lack the extensive tertiary contacts seen in globular proteins. In the collagen triple helix, the structural adjacency of the carboxy-termini of the three chains makes this region an attractive target for introducing metal binding sites. We engineered His3 sites based on structural modeling constraints into a series of designed homotrimeric and heterotrimeric peptides, assessing the capacity of metal binding to improve stability and in the case of heterotrimers, affect specificity of assembly. Notable enhancements in stability for both homo- and heteromeric systems were observed upon addition of zinc(II) and several other metal ions only when all three histidine ligands were present. Metal binding affinities were consistent with the expected Irving-Williams series for imidazole. Unlike other metals tested, copper(II) also bound to peptides lacking histidine ligands. Acetylation of the peptide N-termini prevented copper binding, indicating proline backbone amide metal-coordination at this site. Copper similarly stabilized animal extracted Type I collagen in a metal-specific fashion, highlighting the potential importance of metal homeostasis within the extracellular matrix.