Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Characterization and preparation of food-derived peptides on improving osteoporosis: A review

Osteoporosis is a systemic bone disease characterized by reduced bone mass and deterioration of the microstructure of bone tissue, leading to an increased risk of fragility fractures and affecting human health worldwide. Food-derived peptides are widely used in functional foods due to their low toxicity, ease of digestion and absorption, and potential to improve osteoporosis. This review summarized and discussed methods of diagnosing osteoporosis, treatment approaches, specific peptides as alternatives to conventional drugs, and the laboratory preparation and identification methods of peptides. It was found that peptides interacting with RGD (arginine-glycine-aspartic acid)-binding active sites in integrin could alleviate osteoporosis, analyzed the interaction sites between these osteogenic peptides and integrin, and further discussed their effects on improving osteoporosis. These may provide new insights for rapid screening of osteogenic peptides, and provide a theoretical basis for their application in bone materials and functional foods.

Unveiling therapeutic frontiers: DON/DRP-104 as innovative Plasma kallikrein inhibitors against carcinoma-associated hereditary angioedema shocks - a comprehensive molecular dynamics exploration

Human plasma kallikrein (PKa) is a member of the serine protease family and serves as a key mediator of the kallikrein-kinin system (KKS), which is known for its regulatory roles in inflammation, vasodilation, blood pressure, and coagulation. Genetic dysregulation of KKS leads to Hereditary Angioedema (HAE), which is characterized by spontaneous, painful swelling in various body regions. Importantly, HAE frequently coexists with various cancers. Despite substantial efforts towards the development of PKa inhibitors for HAE, there remains a need for bifunctional agents addressing both anti-cancer and anti-HAE aspects, especially against carcinoma-associated comorbid HAE conditions. Consequently, we investigated the therapeutic potential of the anti-glutamine prodrug, isopropyl(S)-2-((S)-2-acetamido-3-(1H-indol-3-yl)-propanamido)-6-diazo-5-oxo-hexanoate (DRP-104), and its active form, 6-Diazo-5-oxo-l-norleucine (DON), recognized for their anti-cancer properties, as novel PKa inhibitors. Utilizing structure-based in silico methods, we conducted a comparative analysis with berotralstat, a clinically approved HAE prophylactic, and sebetralstat, an investigational HAE therapeutic agent, in Phase 3 clinical trials. Inhibiting PKa with DON resulted in relatively heightened structural stability, rigidity, restricted protein folding, and solvent-accessible loop exposure, contributing to increased intra-atomic hydrogen bond formation. Conversely, PKa inhibition with DRP-104 induced restricted residue flexibility and significantly disrupted the critical SER195-HIS57 arrangement in the catalytic triad. Both DON and DRP-104, along with the reference drugs, induced strong cooperative intra-residue motion and bidirectional displacement in the PKa architecture. The results revealed favorable binding kinetics of DON/DRP-104, showing thermodynamic profiles that were either superior or comparable to those of the reference drugs. These findings support their consideration for clinical investigations into the management of carcinoma-associated HAE.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Isolation, Characterization, Genome Annotation, and Evaluation of Tyrosinase Inhibitory Activity in Secondary Metabolites of Paenibacillus sp. JNUCC32: A Comprehensive Analysis through Molecular Docking and Molecular Dynamics Simulation

A potential strain, Paenibacillus sp. JNUCC32, was isolated and subjected to whole-genome sequencing. Genome functional annotation revealed its active metabolic capabilities. This study aimed to investigate the pivotal secondary metabolites in the biological system. Fermentation and extraction were performed, resulting in the isolation of seven known compounds: tryptophol (1), 3-(4-hydroxyphenyl)propionic acid (2), ferulic acid (3), maculosin (4), brevianamide F (5), indole-3-acetic acid (6), and butyric acid (7). Tryptophol exhibited favorable pharmacokinetic properties and demonstrated certain tyrosinase inhibitory activity (IC50 = 999 μM). For further analysis of its inhibition mechanism through molecular docking and molecular dynamics (MD) simulation, tryptophol formed three hydrogen bonds and a pro-Michaelis complex with tyrosinase (binding energy = -5.3 kcal/mol). The MD simulation indicated favorable stability for the tryptophol-mushroom tyrosinase complex, primarily governed by hydrogen bond interactions. The crucial residues VAL-283 and HIS-263 in the docking were also validated. This study suggests tryptophol as a potential candidate for antibrowning agents and dermatological research.

Unraveling the inhibitory mechanism of adenylyl cyclase 8E: New insights into regulatory pathways of cAMP signal integration

Adenylyl Cyclase 8E (AC8E), which lacks part of M1 transmembrane domain, has been previously shown to dimerize with AC3 and down-regulate its activity, but the molecular mechanism of this inhibitory effect has remained elusive. Here, we first show that AC8E also inhibits AC2 and AC6, highlighting the functional importance of this novel regulatory mechanism in the cAMP signaling pathway across AC families. We then completed the partial structure of Bos taurus AC9 using combinations of comparative modeling and fold recognition methods, and used this as a template to build the first full 3D-models of AC8 and AC8E. These models evidenced that the lack of M1 transmembrane domain of AC8E shifts the N-terminal domain, which impacts the orientation of the helical domains, thus affecting the catalytic site. This was confirmed in living cells with cAMP imaging, where we showed that the N-terminal domain is required for reducing cAMP production. Our data also show that AC8E prevents the translocation of other ACs towards the plasma membrane, further reducing the cAMP responsiveness to extracellular signals. This newly discovered dual inhibitory mechanism provides an additional level of regulation of cAMP-dependent signals integration.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Computational designing of the ligands of Protein L affinity chromatography based on molecular docking and molecular dynamics simulations

Protein L is a multidomain protein from Peptostreptococcus magnus with binding affinity to kappa light chain of human immunoglobulin (Ig) which is used for the purification of antibody fragments by affinity chromatography. The advances in protein engineering and computational biology approaches lead to the development of engineered affinity ligands with improved properties including binding affinity. In this study, molecular dynamics simulations (MDs) and Osprey software were used to design single B domains of the Protein L with higher affinity to antibody fragments. The modified B domains were then polymerized to ligand with six B domains by homology modeling methods. The results showed that single B domain mutants of MB1 (Thr865Trp) and MB2 (Thr847Met-Thr865Trp) had higher binding affinity to Fab compared to the wild single B domain. Also, MDs and molecular docking results showed that the polymerized Proteins L including the wild and mutated six B domains (6B0, 6B1, and 6B2) were stable during MDs and the two mutants of 6B1 and 6B2 showed higher binding affinity to Fab relative to the wild type.Communicated by Ramaswamy H. Sarma.

Toward a structural identification of metastable molecular conformations

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.

New insights into the protein stabilizing effects of trehalose by comparing with sucrose

Disaccharides are well known to be efficient stabilizers of proteins, for example in the case of lyophilization or cryopreservation. However, although all disaccharides seem to exhibit bioprotective and stabilizing properties, it is clear that trehalose is generally superior compared to other disaccharides. The aim of this study was to understand this by comparing how the structural and dynamical properties of aqueous trehalose and sucrose solutions influence the protein myoglobin (Mb). The structural studies were based on neutron and X-ray diffraction in combination with empirical potential structure refinement (EPSR) modeling, whereas the dynamical studies were based on quasielastic neutron scattering (QENS) and molecular dynamics (MD) simulations. The results show that the overall differences in the structure and dynamics of the two systems are small, but nevertheless there are some important differences which may explain the superior stabilizing effects of trehalose. It was found that in both systems the protein is preferentially hydrated by water, but that this effect is more pronounced for trehalose, i.e. trehalose forms less hydrogen bonds to the protein surface than sucrose. Furthermore, the rotational motion around dihedrals between the two glucose rings of trehalose is slower than in the case of the dihedrals between the glucose and fructose rings of sucrose. This leads to a less perturbed protein structure in the case of trehalose. The observations indicate that an aqueous environment closest to the protein molecules is beneficial for an efficient bioprotective solution.

Application of molecular dynamics simulation for exploring the roles of plant biomolecules in promoting environmental health

Understanding the dynamic changes of plant biomolecules is vital for exploring their mechanisms in the environment. Molecular dynamics (MD) simulation has been widely used to study structural evolution and corresponding properties of plant biomolecules at the microscopic scale. Here, this review (i) outlines structural properties of plant biomolecules, and the crucial role of MD simulation in advancing studies of the biomolecules; (ii) describes the development of MD simulation in plant biomolecules, determinants of simulation, and analysis parameters; (iii) introduces the applications of MD simulation in plant biomolecules, including the response of the biomolecules to multiple stresses, their roles in corrosive environments, and their contributions in improving environmental health; (iv) reviews techniques integrated with MD simulation, such as molecular biology, quantum mechanics, molecular docking, and machine learning modeling, which bridge gaps in MD simulation. Finally, we make suggestions on determination of force field types, investigation of plant biomolecule mechanisms, and use of MD simulation in combination with other techniques. This review provides comprehensive summaries of the mechanisms of plant biomolecules in the environment revealed by MD simulation and validates it as an applicable tool for bridging gaps between macroscopic and microscopic behavior, providing insights into the wide application of MD simulation in plant biomolecules.

Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes

Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na⁺ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.

Structural Basis of the Binding Mode of the Antineoplastic Compound Motixafortide (BL-8040) in the CXCR4 Chemokine Receptor

Modulation of the CXCL12-CXCR4 signaling axis is of the utmost importance due to its central involvement in several pathological disorders, including inflammatory diseases and cancer. Among the different currently available drugs that inhibit CXCR4 activation, motixafortide-a best-in-class antagonist of this GPCR receptor-has exhibited promising results in preclinical studies of pancreatic, breast, and lung cancers. However, detailed information on the interaction mechanism of motixafortide is still lacking. Here, we characterize the motixafortide/CXCR4 and CXCL12/CXCR4 protein complexes by using computational techniques including unbiased all-atom molecular dynamics simulations. Our microsecond-long simulations of the protein systems indicate that the agonist triggers changes associated with active-like GPCR conformations, while the antagonist favors inactive conformations of CXCR4. Detailed ligand-protein analysis indicates the importance of motixafortide's six cationic residues, all of which established charge-charge interactions with acidic CXCR4 residues. Furthermore, two synthetic bulky chemical moieties of motixafortide work in tandem to restrict the conformations of important residues associated with CXCR4 activation. Our results not only elucidate the molecular mechanism by which motixafortide interacts with the CXCR4 receptor and stabilizes its inactive states, but also provide essential information to rationally design CXCR4 inhibitors that preserve the outstanding pharmacological features of motixafortide.

Assessing protein homology models with docking reproducibility

Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able to produce structures that were similar enough to experimental structures that many described the problem of protein prediction solved. However, for such structures to be used for drug docking studies requires precision in the placement of side chain atoms as well. Here we built a library of 1334 small molecules and examined how reproducibly they bound to the same site on a protein using QuickVina-W, a branch of the program Autodock that is optimized for blind searches. We discovered that the higher the backbone quality of the homology model the greater the similarity between the small molecule docking to the experimental and modeled structures. Furthermore, we found that specific subsets of this library were particularly useful for identifying small differences between the best of the best modeled structures. Specifically, when the number of rotatable bonds in the small molecule increased, differences in binding sites became more apparent.

Intact Transition Epitope Mapping-Force Differences between Original and Unusual Residues (ITEM-FOUR)

Antibody-based point-of-care diagnostics have become indispensable for modern medicine. In-depth analysis of antibody recognition mechanisms is the key to tailoring the accuracy and precision of test results, which themselves are crucial for targeted and personalized therapy. A rapid and robust method is desired by which binding strengths between antigens and antibodies of concern can be fine-mapped with amino acid residue resolution to examine the assumedly serious effects of single amino acid polymorphisms on insufficiencies of antibody-based detection capabilities of, e.g., life-threatening conditions such as myocardial infarction. The experimental ITEM-FOUR approach makes use of modern mass spectrometry instrumentation to investigate intact immune complexes in the gas phase. ITEM-FOUR together with molecular dynamics simulations, enables the determination of the influences of individually exchanged amino acid residues within a defined epitope on an immune complex's binding strength. Wild-type and mutated epitope peptides were ranked according to their experimentally determined dissociation enthalpies relative to each other, thereby revealing which single amino acid polymorphism caused weakened, impaired, and even abolished antibody binding. Investigating a diagnostically relevant human cardiac Troponin I epitope for which seven nonsynonymous single nucleotide polymorphisms are known to exist in the human population tackles a medically relevant but hitherto unsolved problem of current antibody-based point-of-care diagnostics.

gmak: A Parameter-Space Mapping Strategy for Force-Field Calibration

In the context of classical molecular simulations, the accuracy of a force field is highly influenced by the values of the relevant simulation parameters. In this work, a parameter-space mapping (PSM) workflow is proposed to aid in the calibration of force-field parameters, based mainly on the following features: (i) regular-grid discretization of the search space; (ii) partial sampling of the search-space grid; (iii) training of surrogate models to predict the estimates of the target properties for nonsampled parameter sets; (iv) post hoc interpretation of the results in terms of multiobjective optimization concepts; (v) attenuation of statistical errors achieved via empiric extension of the duration of the simulations; (vi) iterative search-space translation according to a user-defined scalar objective function that measures the accuracy of the force field (e.g., the weighted root-mean-square deviation of the target properties relative to the reference data). This combination of features results in a hybrid of a single- and a multiobjective optimization strategy, allowing for the approximate determination of both a local minimum of the chosen objective function and its neighboring Pareto efficient points. The PSM workflow is implemented in the extensible Python program gmak, which is made available in the Git repository at http://github.com/mssm-labmmol/gmak. Using this implementation, the PSM workflow was tested in a proof-of-concept fashion in the recalibration of the Lennard-Jones parameters of the 3-point Optimal Point Charge (OPC3) water model for compatibility with the GROMOS treatment of nonbonded interactions. The recalibrated model reproduces typical pure-liquid properties with an accuracy similar to the original OPC3 model and represents a significant improvement relative to the Simple Point Charge (SPC) model, which is the official recommendation for simulations using GROMOS force fields.

Non-Invasive, Reliable, and Fast Quantification of DNA Loading on Gold Nanoparticles by a One-Step Optical Measurement

An exquisite, versatile, and reproducible quantification of DNA loading on gold nanoparticles (Au NPs) has long been pursued because this loading influences the analytical, therapeutic, and self-assembly behaviors of DNA-Au NPs. Nevertheless, the existing methods used thus far rely solely on the invasive detachment and subsequent spectroscopic quantification of DNA, which are error-prone and highly dependent on trained personnel. Here, we present a non-invasive optical framework that can determine the number of DNA strands on Au NPs by versatile one-step measurement of the visible absorption spectra of DNA-Au NP solutions without any invasive modifications or downstream processes. Using effective medium theory in conjunction with electromagnetic numerical calculation, the change in DNA loading density, resulting from varying the ion concentration, Au NP size, DNA strand length, and surrounding temperature, can be tracked in situ merely by the one-step measurement of visible absorption spectra, which is otherwise impossible to achieve. Moreover, the simplicity and robustness of this method promote reproducible DNA loading quantification regardless of experimental adeptness, which is in stark contrast with existing invasive and multistep methods. Overall, the optical framework outlined in this work can contribute to democratizing research on DNA-Au NPs and facilitating their rapid adoption in transformative applications.

In silico design of fusion keratinocyte growth factor containing collagen-binding domain for tissue engineering application

Keratinocyte growth factor (KGF) is a potential therapeutic factor in wound healing. However, its applications have been restricted due to its low stability, short half-life, and limited target specificity. We aimed to immobilize KGF on collagen-based biomaterials for long-lasting and targeted therapy by designing fusion forms of KGF with collagen-binding domains (CBD) from natural origins. Twelve fusion proteins were designed consisting of KGF and CBDs with different lengths and amino acid compositions. Three-dimensional (3D) structures of the fusions were predicted by homology modeling. Physiochemical properties and secondary structure of the fusions were evaluated by bioinformatics tools. Moreover, the effect of the CBDs on the 3D structure and dynamic behavior of the fusions was investigated by molecular dynamics (MD) simulation. The binding affinity of the fusions to collagen, KGF receptor, and heparin was assessed using docking tools. Our results demonstrated that fusions with small CBDs like CBD of mammalian collagenase and decapeptide CBD of von Willebrand factor (VWF) were more stable and properly folded than those with larger CBDs. On the other hand, the insertion of bulky CBDs, including Fibronectin CBD and CBD of Clostridium histolyticum collagenase, into KGF resulted in stronger binding to collagen. Therefore, very small or large CBDs are inappropriate for constructing KGF fusions because they suffer from low collagen affinity or poor stability. By comparing the results of MD simulation and docking, this study proposed that CBDs belonging to Vibrio mimicus metalloprotease and A3 domain of VWF would be good candidates to produce stable fusions with proper affinities toward collagen and KGF receptors. Moreover, the secondary structure analysis showed that the overall structure of KGF and CBDs was better preserved when CBDs were inserted at the C-terminal of KGF. This computational information about novel KGF fusions may help find the best constructs for experimental studies.

Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

Optical tweezers for drug discovery

The time taken and the cost of producing novel therapeutic drugs presents a significant burden - a typical target-based drug discovery process involves computational screening of drug libraries, compound assays and expensive clinical trials. This review summarises the value of dynamic conformational information obtained by optical tweezers and how this information can target 'undruggable' proteins. Optical tweezers provide insights into the link between biological mechanisms and structural conformations, which can be used in drug discovery. Developing workflows including software and sample preparation will improve throughput, enabling adoption of optical tweezers in biopharma. As a complementary tool, optical tweezers increase the number of drug candidates, improve the understanding of a target's complex structural dynamics and elucidate interactions between compounds and their targets.

Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases

Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics

Polymorphisms of Thiopurine S-methyltransferase (TPMT) are known to be associated with leukemia, inflammatory bowel diseases, and more. The objective of the present study was to identify novel deleterious missense SNPs of TPMT through a comprehensive in silico protocol. The initial SNP screening protocol used to identify deleterious SNPs from the pool of all TPMT SNPs in the dbSNP database yielded an accuracy of 83.33% in identifying extremely dangerous variants. Five novel deleterious missense SNPs (W33G, W78R, V89E, W150G, and L182P) of TPMT were identified through the aforementioned screening protocol. These 5 SNPs were then subjected to conservation analysis, interaction analysis, oncogenic and phenotypic analysis, structural analysis, PTM analysis, and molecular dynamics simulations (MDS) analysis to further assess and analyze their deleterious nature. Oncogenic analysis revealed that all five SNPs are oncogenic. MDS analysis revealed that all SNPs are deleterious due to the alterations they cause in the binding energy of the wild-type protein. Plasticity-induced instability caused by most of the mutations as indicated by the MDS results has been hypothesized to be the reason for this alteration. While in vivo or in vitro protocols are more conclusive, they are often more challenging and expensive. Hence, future research endeavors targeted at TPMT polymorphisms and/or their consequences in relevant disease progressions or treatments, through in vitro or in vivo means can give a higher priority to these SNPs rather than considering the massive pool of all SNPs of TPMT.

'Targeting' the search: An upgraded structural and functional repository of antimicrobial peptides for biofilm studies (B-AMP v2.0) with a focus on biofilm protein targets

Bacterial biofilms, often as multispecies communities, are recalcitrant to conventional antibiotics, making the treatment of biofilm infections a challenge. There is a push towards developing novel anti-biofilm approaches, such as antimicrobial peptides (AMPs), with activity against specific biofilm targets. In previous work, we developed Biofilm-AMP, a structural and functional repository of AMPs for biofilm studies (B-AMP v1.0) with more than 5000 structural models of AMPs and a vast library of AMP annotations to existing biofilm literature. In this study, we present an upgraded version of B-AMP, with a focus on existing and novel bacterial biofilm targets. B-AMP v2.0 hosts a curated collection of 2502 biofilm protein targets across 473 bacterial species, with structural protein models and functional annotations from PDB, UniProt, and PubMed databases. The biofilm targets can be searched for using the name of the source organism, and function and type of protein, and results include designated Target IDs (unique to B-AMP v2.0), UniProt IDs, 3D predicted protein structures, PDBQT files, pre-defined protein functions, and relevant scientific literature. To present an example of the combined applicability of both, the AMP and biofilm target libraries in the repository, we present two case studies. In the first case study, we expand an in silico pipeline to evaluate AMPs against a single biofilm target in the multidrug resistant, bacterial pathogen Corynebacterium striatum, using 3D protein-peptide docking models from previous work and Molecular Dynamics simulations (~1.2µs). In the second case study, we build an in silico pipeline to identify candidate AMPs (using AMPs with both anti-Gram positive and anti-Gram negative activity) against two biofilm targets with a common functional annotation in Pseudomonas aeruginosa and Staphylococcus aureus, widely-encountered bacterial co-pathogens. With its enhanced structural and functional capabilities, B-AMP v2.0 serves as a comprehensive resource for AMP investigations related to biofilm studies. B-AMP v2.0 is freely available at https://b-amp.karishmakaushiklab.com and will be regularly updated with structural models of AMPs and biofilm targets, as well as 3D protein-peptide interaction models for key biofilm-forming pathogens.

Accurate Prediction for Protein-Peptide Binding Based on High-Temperature Molecular Dynamics Simulations

The structural characterization of protein-peptide interactions is fundamental to elucidating biological processes and designing peptide drugs. Molecular dynamics (MD) simulations are extensively used to study biomolecular systems. However, simulating the protein-peptide binding process is usually quite expensive. Based on our previous studies, herein, we propose a simple and effective method to predict the binding site and pose of the peptide simultaneously using high-temperature (high-T) MD simulations with the RSFF2C force field. Thousands of binding events (nonspecific or specific) can be sampled during microseconds of high-T MD. From density-based clustering analysis, the structures of all of the 12 complexes (nine with linear peptides and three with cyclic peptides) can be successfully predicted with root-mean-square deviation (RMSD) < 2.5 Å. By directly simulating the process of the ligand binding onto the receptor, our method approaches experimental precision for the first time, significantly surpassing previous protein-peptide docking methods in terms of accuracy.

Structure-based rational design of hydroxysteroid dehydrogenases for improving and diversifying steroid synthesis

A group of steroidogenic enzymes, hydroxysteroid dehydrogenases are involved in steroid metabolism which is very important in the cell: signaling, growth, reproduction, and energy homeostasis. The enzymes show an inherent function in the interconversion of ketosteroids and hydroxysteroids in a position- and stereospecific manner on the steroid nucleus and side-chains. However, the biocatalysis of steroids reaction is a vital and demanding, yet challenging, task to produce the desired enantiopure products with non-natural substrates or non-natural cofactors, and/or in non-physiological conditions. This has driven the use of protein design strategies to improve their inherent biosynthetic efficiency or activate their silent catalytic ability. In this review, the innate features and catalytic characteristics of enzymes based on sequence-structure-function relationships of steroidogenic enzymes are reviewed. Combining structure information and catalytic mechanisms, progress in protein redesign to stimulate potential function, for example, substrate specificity, cofactor dependence, and catalytic stability are discussed.

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

Accelerating the Ensemble Convergence of RNA Hairpin Simulations with a Replica Exchange Structure Reservoir

RNA is a key participant in many biological processes, but studies of RNA using computer simulations lag behind those of proteins, largely due to less-developed force fields and the slow dynamics of RNA. Generating converged RNA ensembles for force field development and other studies remains a challenge. In this study, we explore the ability of replica exchange molecular dynamics to obtain well-converged conformational ensembles for two RNA hairpin systems in an implicit solvent. Even for these small model systems, standard REMD remains computationally costly, but coupling to a pre-generated structure library using the reservoir REMD approach provides a dramatic acceleration of ensemble convergence for both model systems. Such precise ensembles could facilitate RNA force field development and validation and applications of simulation to more complex RNA systems. The advantages and remaining challenges of applying R-REMD to RNA are investigated in detail.

Influencing factors and characterization methods of nanoparticles regulating amyloid aggregation

Human disorders associated with amyloid aggregation, such as Alzheimer's disease and Parkinson's disease, afflict the lives of millions worldwide. When peptides and proteins in the body are converted to amyloids, which have a tendency to aggregate, the toxic oligomers produced during the aggregation process can trigger a range of diseases. Nanoparticles (NPs) have been found to possess surface effects that can modulate the amyloid aggregation process and they have potential application value in the treatment of diseases related to amyloid aggregation and fibrillary tangles. In this review, we discuss recent progress relating to studies of nanoparticles that regulate amyloid aggregation. The review focuses on the factors influencing this regulation, which are important as guidelines for the future design of NPs for the treatment of amyloid aggregation. We describe the characterization methods that have been utilized so far in such studies. This review provides research information and characterization methods for the rational design of NPs, which should result in therapeutic strategies for amyloid diseases.

Single-molecule conductance of double-stranded RNA oligonucleotides

RNA oligonucleotides are crucial for a range of biological functions and in many biotechnological applications. Herein, we measured, for the first time, the conductance of individual double-stranded (ds)RNA molecules and compared it with the conductance of single DNA : RNA hybrids. The average conductance values are similar for both biomolecules, but the distribution of conductance values shows an order of magnitude higher variability for dsRNA, indicating higher molecular flexibility of dsRNA. Microsecond Molecular Dynamics simulations explain this difference and provide structural insights into the higher stability of DNA : RNA duplex with atomic level of detail. The rotations of 2'-OH groups of the ribose rings and the bases in RNA strands destabilize the duplex structure by weakening base stacking interactions, affecting charge transport, and making single-molecule conductance of dsRNA more variable (dynamic disorder). The results demonstrate that a powerful combination of state-of-the-art biomolecular electronics techniques and computational approaches can provide valuable insights into biomolecules' biophysics with unprecedented spatial resolution.

Simultaneous parametrization of torsional and third-neighbor interaction terms in force-field development: The LLS-SC algorithm

The calibration of torsional interaction terms by fitting relative gas-phase conformational energies against their quantum-mechanical values is a common procedure in force-field development. However, much less attention has been paid to the optimization of third-neighbor nonbonded interaction parameters, despite their strong coupling with the torsions. This article introduces an algorithm termed LLS-SC, aimed at simultaneously parametrizing torsional and third-neighbor interaction terms based on relative conformational energies. It relies on a self-consistent (SC) procedure where each iteration involves a linear least-squares (LLS) regression followed by a geometry optimization of the reference structures. As a proof-of-principle, this method is applied to obtain torsional and third-neighbor interaction parameters for aliphatic chains in the context of the GROMOS 53A6 united-atom force field. The optimized parameter set is compared to the original one, which has been fitted manually against thermodynamic properties for small linear alkanes. The LLS-SC implementation is freely available under http://github.com/mssm-labmmol/profiler.

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Immunoinformatics approach for designing a universal multiepitope vaccine against Chandipura Virus

Chandipura vesiculovirus (CHPV) is a fast-emerging virus that causes acute encephalitis with a high death rate. Because of its extensive prevalence in African and Asian countries, this infection has become a global hazard, and there is an urgent need to create an effective and non-allergenic vaccine or appropriate treatment to combat it. A vaccine candidate is offered utilizing a computational technique in this study. To build a potential vaccine candidate, viral protein sequences were acquired from the National Center for Biotechnology Information database and evaluated with several bioinformatics techniques to identify B-cell and T-cell epitopes. V1 was shown to be superior in terms of various physicochemical qualities, as well as highly immunogenic and non-allergic. Molecular docking revealed that the CHPV vaccine construct had a greater binding affinity with human Toll-like receptors (TLR-3 and TLR-8) and that it was stable in molecular dynamics simulations. MEC-CHPV was in silico cloned in the pET28a (+) expression vector using codon optimization. The current research identifies potential antigenic epitopes that could be used as vaccine candidates to eradicate the CHPV. This in-silico development of a CHPV vaccine with multiple epitopes could open the path for future rapid laboratory tests.

In-Silico Vaccine Design Based on a Novel Vaccine Candidate Against Infections Caused by Acinetobacter baumannii

Acinetobacter baumannii is notorious for causing serious infections of the skin, lungs, soft tissues, bloodstream, and urinary tract. Despite the overwhelming information available so far, there has still been no approved vaccine in the market to prevent these infections. Therefore, this study focuses on developing a rational vaccine design using the technique of epitope mapping to curb the infections caused by A. baumannii. An outer membrane protein with immunogenic potential as well as all the properties of a good vaccine candidate was selected and used to calculate epitopes for selection on the basis of a low percentile rank, high binding scores, good immunological properties, and non-allergenicity. Thus, a 240 amino-acid vaccine sequence was obtained by manually joining all the epitopes in sequence-wise manner with the appropriate linkers, namely AAY, GPGPG, and EAAAK. Additionally, a 50S ribosomal protein L7/L12, agonist to the human innate immune receptors was attached to the N-terminus to increase the overall immune response towards the vaccine. As a result, enhanced overall protein stability, expression, immunostimulatory capabilities, and solubility of the designed construct were observed. Molecular dynamic simulations revealed the compactness and stability of the polypeptide construct. Moreover, molecular docking exhibited strong binding of the designed vaccine with TLR-4 and TLR-9. In-silico immune simulations indicated an immense increment in T-cell and B-cell populations. Bioinformatic tools also significantly assisted with optimizing codons which allowed for successful cloning of constructs into desired host vectors. Using in-silico tools to design a vaccine against A. baumannii demonstrated that this construct could pave the way for successfully combating infections caused by multidrug-resistant bacteria.

Nobiletin, a hexamethoxyflavonoid from citrus pomace, attenuates G1 cell cycle arrest and apoptosis in hypoxia-induced human trophoblast cells of JEG-3 and BeWo via regulating the p53 signaling pathway

Background

Hypoxia is associated with abnormal cell apoptosis in trophoblast cells, which causes fetal growth restriction and related placental pathologies. Few effective methods for the prevention and treatment of placenta-related diseases exist. Natural products and functional foods have always been a rich source of potential anti-apoptotic drugs. Nobiletin (NOB), a hexamethoxyflavonoid derived from the citrus pomace, shows an anti-apoptotic activity, which is a non-toxic constituent of dietary phytochemicals approved by the Food and Drug Administration. However, their effects on hypoxia-induced human trophoblast cells have not been fully studied.

Objective

The aim of this study was to investigate the protective effects of NOB on hypoxia-induced apoptosis of human trophoblast JEG-3 and BeWo cells, and their underlying mechanisms.

Design

First, the protective effect of NOB on hypoxia-induced apoptosis of JEG-3 and BeWo cells was studied. Cell viability and membrane integrity were determined by CCK-8 assay and lactate dehydrogenase activity, respectively. Real Time Quantitative PCR (RT-qPCR) and Western blot analysis were used to detect the mRNA and protein levels of HIF1α. Propidium iodide (PI)-labeled flow cytometry was used to detect cell cycle distribution. Cell apoptosis was detected by flow cytometry with Annexin V-FITC and PI double staining, and the expression of apoptosis marker protein cl-PARP was detected by Western blot analysis. Then, the molecular mechanism of NOB against apoptosis was investigated. Computer molecular docking and dynamics were used to simulate the interaction between NOB and p53 protein, and this interaction was verified in vitro by Ultraviolet and visible spectrum (UV-visible spectroscopy), fluorescence spectroscopy and circular dichroism. Furthermore, the changes in the expression of p53 signaling pathway genes and proteins were detected by RT-qPCR and Western blot analysis, respectively.

Results

Hypoxia treatment resulted in a decreased cell viability and cell membrane integrity in JEG-3 and BeWo cell lines, and an increased expression of HIF1α, cell cycle arrest in the G1 phase, and massive cell apoptosis, which were alleviated after NOB treatment. Molecular docking and dynamics simulations found that NOB spontaneously bonded to human p53 protein, leading to the change of protein conformation. The intermolecular interaction between NOB and human p53 protein was further confirmed by UV-visible spectroscopy, fluorescence spectroscopy and circular dichroism. After the treatment of 100 μM NOB, a down-regulation of mRNA and protein levels of p53 and p21 and an up-regulation of BCL2/BAX mRNA and protein ratio were observed in JEG-3 cells; however, there was also a down-regulation of mRNA and protein levels observed for p53 and p21 in BeWo cells after the treatment of NOB. The BCL2/BAX ratio of BeWo cells did not change after the treatment of 100 μM NOB.

Conclusion

NOB attenuated hypoxia-induced apoptosis in JEG-3 and BeWo cell lines and might be a potential functional ingredient to prevent pregnancy-related diseases caused by hypoxia-induced apoptosis. These findings would also suggest the exploration and utilization of citrus resources, and the development of citrus industry.

Cyclosporin Structure and Permeability: From A to Z and Beyond

Cyclosporins are natural or synthetic undecapeptides with a wide range of actual and potential pharmaceutical applications. Several members of the cyclosporin compound family have remarkably high passive membrane permeabilities that are not well-described by simple structural metrics. Here we review experimental studies of cyclosporin structure and permeability, including cyclosporin-metal complexes. We also discuss models for the conformation-dependent permeability of cyclosporins and similar compounds. Finally, we identify current knowledge gaps in the literature and provide recommendations regarding future avenues of exploration.

NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview

BACKGROUND

NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems.

OBJECTIVE

This review provides a toolkit to investigate peptide conformational properties by NMR.

METHODS

Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well.

RESULTS

The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well.

CONCLUSION

NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

Structures of MERS-CoV macro domain in aqueous solution with dynamics: Impacts of parallel tempering simulation techniques and CHARMM36m and AMBER99SB force field parameters

A novel virus, severe acute respiratory syndrome Coronavirus 2 (SARS‐CoV‐2), causing coronavirus disease 2019 (COVID‐19) worldwide appeared in 2019. Detailed scientific knowledge of the members of the Coronaviridae family, including the Middle East Respiratory Syndrome Coronavirus (MERS‐CoV) is currently lacking. Structural studies of the MERS‐CoV proteins in the current literature are extremely limited. We present here detailed characterization of the structural properties of MERS‐CoV macro domain in aqueous solution. Additionally, we studied the impacts of chosen force field parameters and parallel tempering simulation techniques on the predicted structural properties of MERS‐CoV macro domain in aqueous solution. For this purpose, we conducted extensive Hamiltonian‐replica exchange molecular dynamics simulations and Temperature‐replica exchange molecular dynamics simulations using the CHARMM36m and AMBER99SB parameters for the macro domain. This study shows that the predicted secondary structure properties including their propensities depend on the chosen simulation technique and force field parameter. We perform structural clustering based on the radius of gyration and end‐to‐end distance of MERS‐CoV macro domain in aqueous solution. We also report and analyze the residue‐level intrinsic disorder features, flexibility and secondary structure. Furthermore, we study the propensities of this macro domain for protein‐protein interactions and for the RNA and DNA binding. Overall, results are in agreement with available nuclear magnetic resonance spectroscopy findings and present more detailed insights into the structural properties of MERS CoV macro domain in aqueous solution. All in all, we present the structural properties of the aqueous MERS‐CoV macro domain using different parallel tempering simulation techniques, force field parameters and bioinformatics tools.

Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization

Continuous assessment of transferable forcefields for molecular simulations is essential to identify their weaknesses and direct improvement efforts. The latest efforts focused on better describing disordered proteins while retaining proper description of folded domains, important because forcefields of the previous generations produce overly compact disordered states. Such improvements should additionally alleviate the related problem of over-stabilized protein–protein interactions, which has been largely overlooked. Here we evaluated three state-of-the-art forcefields, current flagships of their respective developers, optimized for ordered and disordered proteins: CHARMM36m with its recommended corrected TIP3P* water, ff19SB with the recommended OPC water, and the 2019 a99SBdisp forcefield by D. E. Shaw Research with its modified TIP4P water; plus ff14SB with TIP3P as an example of the former generation of forcefields. Our evaluation entailed simulations of (i) multiple copies of a protein that is highly soluble yet undergoes weak dimerization, (ii) a disordered peptide with low, well-characterized alpha helical propensity, and (iii) a peptide known to form insoluble β-aggregates. Our results recapitulate ff14SB-TIP3P over-stabilizing aggregates and secondary structures and place a99SBdisp-TIP4PD at the other end i.e. predicting overly weak intermolecular interactions despite reasonably predicting secondary structure propensities. In-between, CHARMM36m-TIP3P* still over-stabilizes aggregates but predicts residue-wise alpha helical propensities in solution slightly better than ff19SB-OPC, while ff19SB-OPC poses the best prediction of weak dimerization of the soluble protein still predicting aggregation of the β-peptides. This independent assessment shows that the claimed forcefield improvements are real, but also that a right balance between noncovalent attraction and repulsion has not yet been reached. We thus propose developers to consider systems like those tested here in their forcefield tuning protocols. Last, the good performance of CHARMM36m-TIP3P* further shows that tuning 3-point water models might still be an alternative to the more costly 4-point models like OPC and TIP4PD.

Nature-inspired dimerization as a strategy to modulate neuropeptide pharmacology exemplified with vasopressin and oxytocin

Vasopressin (VP) and oxytocin (OT) are cyclic neuropeptides that regulate fundamental physiological functions via four G protein-coupled receptors, V_1aR, V_1bR, V₂R, and OTR. Ligand development remains challenging for these receptors due to complex structure–activity relationships. Here, we investigated dimerization as a strategy for developing ligands with novel pharmacology. We regioselectively synthesised and systematically studied parallel, antiparallel and N- to C-terminal cyclized homo- and heterodimer constructs of VP, OT and dVDAVP (1-deamino-4-valine-8-d-arginine-VP). All disulfide-linked dimers, except for the head-to-tail cyclized constructs, retained nanomolar potency despite the structural implications of dimerization. Our results support a single chain interaction for receptor activation. Dimer orientation had little impact on activity, except for the dVDAVP homodimers, where an antagonist to agonist switch was observed at the V_1aR. This study provides novel insights into the structural requirements of VP/OT receptor activation and spotlights dimerization as a strategy to modulate pharmacology, a concept also frequently observed in nature.

Structural and pharmacological study of parallel, antiparallel and N- to C-terminal cyclized homo- and heterodimers of vasopressin and oxytocin. This study spotlights dimerization as a strategy to modulate the pharmacology of neuropeptides.

HeroMDAnalysis: an automagical tool for GROMACS-based molecular dynamics simulation analysis

Background & objective: Molecular dynamics simulations (MDS) using GROMACS are among the commonly used computational experiments in the area of molecular biology and drug discovery. This article presents a project called HeroMDAnalysis, an automagical tool to analyze the GROMACS-based MDS trajectories and generate plots as high-quality images for various parameters. Materials & methods: The tool was built using bash shell programming, and graphical user interface was built using Zenity engine. Results & conclusion: This tool offers a simple, semiautomated, and relatively fast framework for what was previously a complex, manual, time-consuming and error-prone task, presenting a useful method for biochemists and synthetic chemists with no prior experience of the command line interface.

Estimation of pore dimensions in lipid membranes induced by peptides and other biomolecules: A review

The cytoplasmic membrane is one of the most frequent cell targets of antimicrobial peptides (AMPs) and other biomolecules. Understanding the mechanism of action of AMPs at the molecular level is of utmost importance for designing of new membrane-specific molecules. In particular, the formation of pores, the structure and size of these pores are of great interest and require nanoscale resolution approaches, therefore, biophysical strategies are essential to achieve an understanding of these processes at this scale. In the case of membrane active peptides, pore formation or general membrane disruption is usually the last step before cell death, and so, pore size is generally directly associated to pore structure and stability and loss of cellular homeostasis, implicated in overall peptide activity. Up to date, there has not been a critical review discussing the methods that can be used specifically for estimating the pore dimensions induced by membrane active peptides. In this review we discuss the scope, relevance and popularity of the different biophysical techniques such as liposome leakage experiments, advanced microscopy, neutron or X-ray scattering, electrophysiological techniques and molecular dynamics studies, all of them useful for determining pore structure and dimension.

A Global Review on Short Peptides: Frontiers and Perspectives

Peptides are fragments of proteins that carry out biological functions. They act as signaling entities via all domains of life and interfere with protein-protein interactions, which are indispensable in bio-processes. Short peptides include fundamental molecular information for a prelude to the symphony of life. They have aroused considerable interest due to their unique features and great promise in innovative bio-therapies. This work focusing on the current state-of-the-art short peptide-based therapeutical developments is the first global review written by researchers from all continents, as a celebration of 100 years of peptide therapeutics since the commencement of insulin therapy in the 1920s. Peptide "drugs" initially played only the role of hormone analogs to balance disorders. Nowadays, they achieve numerous biomedical tasks, can cross membranes, or reach intracellular targets. The role of peptides in bio-processes can hardly be mimicked by other chemical substances. The article is divided into independent sections, which are related to either the progress in short peptide-based theranostics or the problems posing challenge to bio-medicine. In particular, the SWOT analysis of short peptides, their relevance in therapies of diverse diseases, improvements in (bio)synthesis platforms, advanced nano-supramolecular technologies, aptamers, altered peptide ligands and in silico methodologies to overcome peptide limitations, modern smart bio-functional materials, vaccines, and drug/gene-targeted delivery systems are discussed.

Membrane Permeability in Cyclic Peptides is Modulated by Core Conformations

Cyclic peptides have the potential to bind to challenging targets, which are undruggable with small molecules, but their application is limited by low membrane permeability. Here, using a series of cyclic pentapeptides, we showed that established physicochemical criteria of permeable peptides are heavily violated. We revealed that a dominant core conformation, stabilized by amides' shielding pattern, could guide the design of novel compounds. As a result, counter-intuitive strategies, such as incorporation of polar residues, can be beneficial for permeability. We further find that core globularity is a promising descriptor, which can extend the capability of standard predictive models.

Peptides and pseudopeptide ligands: a powerful toolbox for the affinity purification of current and next-generation biotherapeutics

Following the consolidation of therapeutic proteins in the fight against cancer, autoimmune, and neurodegenerative diseases, recent advancements in biochemistry and biotechnology have introduced a host of next-generation biotherapeutics, such as CRISPR-Cas nucleases, stem and car-T cells, and viral vectors for gene therapy. With these drugs entering the clinical pipeline, a new challenge lies ahead: how to manufacture large quantities of high-purity biotherapeutics that meet the growing demand by clinics and biotech companies worldwide. The protein ligands employed by the industry are inadequate to confront this challenge: while featuring high binding affinity and selectivity, these ligands require laborious engineering and expensive manufacturing, are prone to biochemical degradation, and pose safety concerns related to their bacterial origin. Peptides and pseudopeptides make excellent candidates to form a new cohort of ligands for the purification of next-generation biotherapeutics. Peptide-based ligands feature excellent target biorecognition, low or no toxicity and immunogenicity, and can be manufactured affordably at large scale. This work presents a comprehensive and systematic review of the literature on peptide-based ligands and their use in the affinity purification of established and upcoming biological drugs. A comparative analysis is first presented on peptide engineering principles, the development of ligands targeting different biomolecular targets, and the promises and challenges connected to the industrial implementation of peptide ligands. The reviewed literature is organized in (i) conventional (α-)peptides targeting antibodies and other therapeutic proteins, gene therapy products, and therapeutic cells; (ii) cyclic peptides and pseudo-peptides for protein purification and capture of viral and bacterial pathogens; and (iii) the forefront of peptide mimetics, such as β-/γ-peptides, peptoids, foldamers, and stimuli-responsive peptides for advanced processing of biologics.

Structure-guided rational design of the substrate specificity and catalytic activity of an enzyme

The quest for an enzyme with desired property is high for biocatalyic production of valuable products in industrial biotechnology. Synthetic biology and metabolic engineering also increasingly require an enzyme with unusual property in terms of substrate spectrum and catalytic activity for the construction of novel circuits and pathways. Structure-guided enzyme engineering has demonstrated a prominent utility and potential in generating such an enzyme, even though some limitations still remain. In this chapter, we present some issues regarding the implementation of the structural information to enzyme engineering, and exemplify the structure-guided rational approach to the design of an enzyme with desired functionality such as substrate specificity and catalytic efficiency.

Sequence-based protein structure optimization using enhanced simulated annealing algorithm on a coarse-grained model

The understanding of protein structure is vital to determine biological function. We presented an enhanced simulated annealing (ESA) algorithm to investigate protein three-dimensional (3D) structure on a coarse-grained model. Inside the algorithm, we adjusted exploration equations to achieve good search intensity. To that end, our algorithm used (i) a multivariable disturbance operator for diversification of solution, (ii) a sign function to improve randomness of solution, and (iii) taking remainder operation performed on floating-point number to tackle out-of-range solution. By monitoring energy value throughout the simulation, the energy-optimal state can be found. The ESA algorithm was tested on artificial and real protein sequences with different lengths. The results show that our algorithm outperforms conventional simulated annealing algorithm and can compete with the reported algorithms before. Especially, our algorithm can obtain folding conformations with specific structural features. Further analysis shows that simulating trajectory of seeking the lowest energy can exhibit thermodynamic behavior of protein folding. Graphical Abstract.