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

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult. In this regard, developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority. Antimicrobial peptides (AMPs), a ubiquitous class of naturally occurring compounds with broad-spectrum antipathogenic activity, hold significant promise as an effective solution to the current antimicrobial resistance (AMR) crisis. Several AMPs have been identified and evaluated for their therapeutic application, with many already in the drug development pipeline. Their distinct properties, such as high target specificity, potency, and ability to bypass microbial resistance mechanisms, make AMPs a promising alternative to traditional antibiotics. Nonetheless, several challenges, such as high toxicity, lability to proteolytic degradation, low stability, poor pharmacokinetics, and high production costs, continue to hamper their clinical applicability. Therefore, recent research has focused on optimizing the properties of AMPs to improve their performance. By understanding the physicochemical properties of AMPs that correspond to their activity, such as amphipathicity, hydrophobicity, structural conformation, amino acid distribution, and composition, researchers can design AMPs with desired and improved performance. In this review, we highlight some of the key strategies used to optimize the performance of AMPs, including rational design and de novo synthesis. We also discuss the growing role of predictive computational tools, utilizing artificial intelligence and machine learning, in the design and synthesis of highly efficacious lead drug candidates.

Iturin: A Promising Cyclic Lipopeptide with Diverse Applications

This comprehensive review examines iturin, a cyclic lipopeptide originating from Bacillus subtilis and related bacteria. These compounds are structurally diverse and possess potent inhibitory effects against plant disease-causing bacteria and fungi. Notably, Iturin A exhibits strong antifungal properties and low toxicity, making it valuable for bio-pesticides and mycosis treatment. Emerging research reveals additional capabilities, including anticancer and hemolytic features. Iturin finds applications across industries. In food, iturin as a biosurfactant serves beyond surface tension reduction, enhancing emulsions and texture. Biosurfactants are significant in soil remediation, agriculture, wound healing, and sustainability. They also show promise in Microbial Enhanced Oil Recovery (MEOR) in the petroleum industry. The pharmaceutical and cosmetic industries recognize iturin's diverse properties, such as antibacterial, antifungal, antiviral, anticancer, and anti-obesity effects. Cosmetic applications span emulsification, anti-wrinkle, and antibacterial use. Understanding iturin's structure, synthesis, and applications gains importance as biosurfactant and lipopeptide research advances. This review focuses on emphasizing iturin's structural characteristics, production methods, biological effects, and applications across industries. It probes iturin's antibacterial, antifungal potential, antiviral efficacy, and cancer treatment capabilities. It explores diverse applications in food, petroleum, pharmaceuticals, and cosmetics, considering recent developments, challenges, and prospects.

Research in the Field of Drug Design and Development

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.

Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides

Introduction

Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends.

Methods

Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs.

Results

EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis.

Conclusion

The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.

Non-Canonical Amino Acids as Building Blocks for Peptidomimetics: Structure, Function, and Applications

This review provides a fresh overview of non-canonical amino acids and their applications in the design of peptidomimetics. Non-canonical amino acids appear widely distributed in nature and are known to enhance the stability of specific secondary structures and/or biological function. Contrary to the ubiquitous DNA-encoded amino acids, the structure and function of these residues are not fully understood. Here, results from experimental and molecular modelling approaches are gathered to classify several classes of non-canonical amino acids according to their ability to induce specific secondary structures yielding different biological functions and improved stability. Regarding side-chain modifications, symmetrical and asymmetrical α,α-dialkyl glycines, Cα to Cα cyclized amino acids, proline analogues, β-substituted amino acids, and α,β-dehydro amino acids are some of the non-canonical representatives addressed. Backbone modifications were also examined, especially those that result in retro-inverso peptidomimetics and depsipeptides. All this knowledge has an important application in the field of peptidomimetics, which is in continuous progress and promises to deliver new biologically active molecules and new materials in the near future.

Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics

Computer-aided drug designing is a promising approach to defeating the dry pipeline of drug discovery. It aims at reduced experimental efforts with cost-effectiveness. Naturally occurring large molecules with molecular weight higher than 500 Dalton such as cationic peptides, cyclic peptides, glycopeptides and lipopeptides are a few examples of large molecules which have successful applications as the broad spectrum antibacterial, anticancer, antiviral, antifungal and antithrombotic drugs. Utilization of microbial metabolites as potential drug candidates incur cost effectiveness through large scale production of such molecules rather than a synthetic approach. Computational studies on such compounds generate tremendous possibilities to develop novel leads with challenges to handle these complex molecules with available computational tools. The opportunities begin with the desired structural modifications in the parent drug molecule. Virtual modifications followed by molecular interaction studies at the target site through molecular modeling simulations and identification of structure-activity relationship models to develop more prominent and potential drug molecules. Lead optimization studies to develop novel compounds with increased specificity and reduced off targeting is a big challenge computationally for large molecules. Prediction of optimized pharmacokinetic properties facilitates development of a compound with lower toxicity as compared to the natural compounds. Generating the library of compounds and studies for target specificity and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) for large molecules are laborious and incur huge cost and chemical wastage through in-vitro methods. Hence, computational methods need to be explored to develop novel compounds from natural large molecules with higher specificity. This review article is focusing on possible challenges and opportunities in the pathway of computer-aided drug discovery of large molecule therapeutics.

What a Difference an Amino Acid Makes: An All-Atom Simulation Study of Nonameric Peptides in Inhibitory HLA-E/NKG2A/CD94 Immune Complexes

MHC class I antigen E (HLA-E), a ligand for the inhibitory NKG2A/CD94 receptor of the immune system, is responsible for evading the immune surveillance in several settings, including senescent cell accumulation and tumor persistence. The formation of this ligand-receptor interaction promotes the inhibition of the cytolytic action of immune system natural killer (NK) cells and CD8⁺ T-cells expressing this receptor. The final outcome of the HLA-E/NKG2A/CD94 interaction on target cells is also highly dependent on the identity of the nonameric peptide incorporated into the HLA-E ligand. To better understand the role played by a nonameric peptide in these immune complexes, we performed a series of multi-microsecond all-atom molecular dynamics simulations. We generated natural and alternative variants of the nonameric peptide bound to the HLA-E ligand alone or in the HLA-E/NKG2A/CD94 complexes. A systematic study of molecular recognition between HLA-E and peptides led to the development of new variants that differ at the strategic 6th position (P6) of the peptide and have favorable in silico properties comparable to those of natural binding peptides. Further examination of a selected subset of peptides in full complexes revealed a new variant that, according to our previously derived atomistic model, can interfere with the signal transduction via HLA-E/NKG2A/CD94 and thus prevent the target cell from evading immune clearance by NK and CD8⁺ T-cells. These simulations provide an atomistic picture of how a small change in amino acid sequence can lead to a profound effect on binding and molecular recognition. Furthermore, our study also provides new data on the peptide interaction motifs as well as the energetic and conformational properties of the binding interface, laying the structure-based foundation for future development of potential therapeutic peptides, peptidomimetics, or even small molecules that would bind to the HLA-E ligand and abrogate NKG2A/CD94 recognition. Such external intervention would be useful in the emerging field of targeting senescent cells in a variety of age-related diseases, as well as in novel cancer immunotherapies.

A transcytotic transport mechanism across the tympanic membrane

Drug treatments for middle ear diseases are currently delivered systemically, or locally after opening the impermeable tympanic membrane (TM). We previously used bacteriophage display to discover novel peptides that are actively transported across the intact TM, with a variety of transport rates. Peptide structures were analyzed for evidence regarding the mechanism for this unexpected transport, which was then tested by the application of chemical inhibitors. Primary sequences indicated that trans-TM peptides share one of two amino acid motifs. Secondary structures revealed that linear configurations associate with higher transport rates than coiled structures. Tertiary analysis indicated that the shared sequence motifs are prominently displayed at the free ends of rapidly transported peptide phage. The shared motifs were evaluated for similarity to known motifs. The highest probability matches were for protein motifs involved in transmembrane transport and exosomes. Overall, structural findings suggest that the shared motifs represent binding sequences. They also implicate transcytosis, a polarized cell transport mechanism consisting of endocytosis, transcellular transport, and exocytosis. Inhibitor studies indicated that macropinocytosis, retrograde transport through Golgi and exocytosis participate in transport across the TM, consistent with transcytosis. This process can be harnessed to noninvasively deliver therapeutics to the middle ear.

Effect of Strain Rate on Single Tau, Dimerized Tau and Tau-Microtubule Interface: A Molecular Dynamics Simulation Study

Microtubule-associated protein (MAP) tau is a cross-linking molecule that provides structural stability to axonal microtubules (MT). It is considered a potential biomarker for Alzheimer's disease (AD), dementia, and other neurological disorders. It is also a signature protein for Traumatic Brain Injury (TBI) assessment. In the case of TBI, extreme dynamic mechanical energies can be felt by the axonal cytoskeletal members. As such, fundamental understandings of the responses of single tau protein, polymerized tau protein, and tau-microtubule interfaces under high-rate mechanical forces are important. This study attempts to determine the high-strain rate mechanical behavior of single tau, dimerized tau, and tau-MT interface using molecular dynamics (MD) simulation. The results show that a single tau protein is a highly stretchable soft polymer. During deformation, first, it significantly unfolds against van der Waals and electrostatic bonds. Then it stretches against strong covalent bonds. We found that tau acts as a viscoelastic material, and its stiffness increases with the strain rate. The unfolding stiffness can be ~50-500 MPa, while pure stretching stiffness can be >2 GPa. The dimerized tau model exhibits similar behavior under similar strain rates, and tau sliding from another tau is not observed until it is stretched to >7 times of original length, depending on the strain rate. The tau-MT interface simulations show that very high strain and strain rates are required to separate tau from MT suggesting Tau-MT bonding is stronger than MT subunit bonding between themselves. The dimerized tau-MT interface simulations suggest that tau-tau bonding is stronger than tau-MT bonding. In summary, this study focuses on the structural response of individual cytoskeletal components, namely microtubule (MT) and tau protein. Furthermore, we consider not only the individual response of a component, but also their interaction with each other (such as tau with tau or tau with MT). This study will eventually pave the way to build a bottom-up multiscale brain model and analyze TBI more comprehensively.

In silico Screening and Behavioral Validation of a Novel Peptide, LCGA-17, With Anxiolytic-Like Properties

The aim of the study was to develop better anxiolytics and antidepressants. We focused on GABA_A receptors and the α2δ auxiliary subunit of V-gated Ca²⁺ channels as putative targets because they are established as mediators of efficacious anxiolytics, antidepressants, and anticonvulsants. We further focused on short peptides as candidate ligands because of their high safety and tolerability profiles. We employed a structural bioinformatics approach to develop novel tetrapeptides with predicted affinity to GABA_A receptors and α2δ. In silico docking studies of one of these peptides, LCGA-17, showed a high binding score for both GABA_A receptors and α2δ, combined with anxiolytic-like properties in a Danio rerio behavioral screen. LCGA-17 showed anxiolytic-like effects in the novel tank test, the light–dark box, and the social preference test, with efficacy comparable to fluvoxamine and diazepam. In binding assays using rat brain membranes, [³H]-LCGA-17 was competed more effectively by gabapentinoid ligands of α2δ than ligands of GABA_A receptors, suggesting that α2δ represents a likely target for LCGA-17. [³H]-LCGA-17 binding to brain lysates was unaffected by competition with ligands for GABA_B, glutamate, dopamine, serotonin, and other receptors, suggesting specific interaction with α2δ. Dose-finding studies in mice using acute administration of LCGA-17 (i.p.) demonstrated anxiolytic-like effects in the open field test, elevated plus maze, and marble burying tests, as well as antidepressant-like properties in the forced swim test. The anxiolytic effects were effectively blocked by bicuculline. Therefore, LCGA-17 is a novel candidate anxiolytic and antidepressant that may act through α2δ, with possible synergism by GABA_A receptors.

CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method

Background

Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.

Results

The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization.

Conclusion

With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04214-4.

Antimicrobial Peptides Derived From Insects Offer a Novel Therapeutic Option to Combat Biofilm: A Review

Biofilms form a complex layer with defined structures, that attach on biotic or abiotic surfaces, are tough to eradicate and tend to cause some resistance against most antibiotics. Several studies confirmed that biofilm-producing bacteria exhibit higher resistance compared to the planktonic form of the same species. Antibiotic resistance factors are well understood in planktonic bacteria which is not so in case of biofilm producing forms. This may be due to the lack of available drugs with known resistance mechanisms for biofilms. Existing antibiotics cannot eradicate most biofilms, especially of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species). Insects produce complex and diverse set of chemicals for survival and defense. Antimicrobial peptides (AMPs), produced by most insects, generally have a broad spectrum of activity and the potential to bypass the resistance mechanisms of classical antibiotics. Besides, AMPs may well act synergistically with classical antibiotics for a double-pronged attack on infections. Thus, AMPs could be promising alternatives to overcome medically important biofilms, decrease the possibility of acquired resistance and treatment of multidrug-resistant pathogens including ESKAPE. The present review focuses on insect-derived AMPs with special reference to anti-biofilm-based strategies. It covers the AMP composition, pathways and mechanisms of action, the formation of biofilms, impact of biofilms on human diseases, current strategies as well as therapeutic options to combat biofilm with antimicrobial peptides from insects. In addition, the review also illustrates the importance of bioinformatics tools and molecular docking studies to boost the importance of select bioactive peptides those can be developed as drugs, as well as suggestions for further basic and clinical research.

Fish and Shellfish-Derived Anti-Inflammatory Protein Products: Properties and Mechanisms

The interest in utilizing food-derived compounds therapeutically has been rising. With the growing prevalence of systematic chronic inflammation (SCI), efforts to find treatments that do not result in the side effects of current anti-inflammatory drugs are underway. Bioactive peptides (BAPs) are a particularly promising class of compounds for the treatment of SCI, and the abundance of high-quality seafood processing byproducts (SPB) makes it a favorable material to derive anti-inflammatory BAPs. Recent research into the structural properties of anti-inflammatory BAPs has found a few key tendencies including they tend to be short and of low molecular weight (LMW), have an overall positive charge, contain hydrophobic amino acids (AAs), and be rich in radical scavenging AAs. SPB-derived anti-inflammatory BAPs have been observed to work via inhibition of the NF-κB and MAPK pathways by disrupting the phosphorylation of IκBα and one or more kinases (ERK, JNK, and p38), respectively. Radical scavenging capacity has also been shown to play a significant role in the efficacy of SPB-derived anti-inflammatory BAPs. To determine if SPB-derived BAPs can serve as an effective treatment for SCI it will be important to understand their properties and mechanisms of action, and this review highlights such findings in recent research.

Biomedical applications of the peptide decorated gold nanoparticles

Currently, peptide-nanoparticle (NP) conjugates have been demonstrated to be efficient and powerful tools for the treatment and the diagnosis of various diseases as well as in the bioimaging application. Several bioconjugation strategies have been adopted to formulate the peptide-NP conjugates. In this review, we discuss the exciting applications of peptide-gold (Au) NP conjugates in the area of drug delivery, targeting, cancer therapy, brain diseases, vaccines, immune modulation, biosensor, colorimetric detection of heavy metals, and bio-labeling in vitro and in vivo models. Within this framework, various approaches such as radiotherapy, photothermal therapy, photodynamic therapy and chemo-photothermal therapy have been demonstrated for the treatment of several diseases. Moreover, we highlight how the morphology, size, density of peptide and the protein corona influence the biological activity, biodistribution and biological fate of peptide-AuNP conjugates. In the end, we discuss the future outlook and the challenges being faced in the clinical translation of the peptide-AuNP conjugates. Overall, this review emphasizes that the peptide-AuNP conjugates might be used as potential theranostic agents for the treatment of life-threatening diseases in an economical fashion in the future.

Understanding the Targeting Mechanisms of Multi-Specific Biologics in Immunotherapy with Multiscale Modeling

Immunotherapeutics are frequently associated with adverse side effects due to the elicitation of global immune modulation. To lower the risk of these side effects, recombinant DNA technology is employed to enhance the selectivity of cell targeting by genetically fusing different biomolecules, yielding new species referred to as multi-specific biologics. The design of new multi-specific biologics is a central challenge for the realization of new immunotherapies. To understand the molecular determinants responsible for regulating the binding between multi-specific biologics and surface-bound membrane receptors, we developed a multiscale computational framework that integrates various simulation approaches covering different timescales and spatial resolutions. Our model system of multi-specific biologics contains two natural ligands of immune receptors, which are covalently tethered by a peptide linker. Using this method, a number of interesting features of multi-specific biologics were identified. Our study therefore provides an important strategy to design the next-generation biologics for immunotherapy.

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Two proteins are connected by different linkers as a model of bispecific biologics

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Conformational dynamics of biologics are captured by microsecond MD simulations

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Coarse-grained simulations are used to test binding between biologics and receptors

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Biologics with long and flexible linkers are more efficient in targeting receptors

Structural stability of antimicrobial peptides rich in tryptophan, proline and arginine: a computational study

The host defense peptides or antimicrobial peptides (AMPs) often contain short sequence of amino acids, either positive or negatively charged and express broad-spectrum antibacterial, antiviral and antifungal activity. Many researchers had reported that tryptophan, arginine and proline rich AMPs have a promising source of next-generation antibiotics. Nowadays, AMPs are used as a possible therapeutic source for future antibiotics. In the present study, the amino acid sequences of 2924 AMPs belonging to various sources rich in Tryptophan, Proline and Arginine was chosen for investigation. The AMPs were further categorized according to their source, structure and antimicrobial activities. The AMPs with tryptophan, arginine, proline residues in abundance with maximum sequence length of 20 amino acids alone was obtained. Homology modeling was performed with PEP-FOLD and the modeled structures were evaluated using RAMPAGE to identify the structural information. Further, the stability of peptide in aqueous condition was probed using molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Peptidomimetics in Silico

Endogenous peptides as part of physiological processes are targets of interest when it comes to finding desirable therapeutics which are able to modulate molecular interactions. The major limits presented by peptides when they are used as drugs have motivated the research of the synthesis of peptidomimetics obtained through chemical modification and the use of in silico approaches. Here recent works on the discovery of peptidomimetics by computational methods are reported. Together with molecular dynamic simulations, the use of pharmacophore research simulations helps to gain insight into and understand the molecular determinants underlying the physiological processes.

Virtual screening for potential inhibitors of β(1,3)-D-glucan synthase as drug candidates against fungal cell wall

Background

To enhance the outcome in patients with invasive candidiasis, initiation of an efficient antifungal treatment in a suitable dosage is necessary. Echinocandins (e.g. caspofungin) inhibit the enzyme β(1,3)-D-glucan synthase of the fungal cell wall. Compared to azoles and other antifungal agents, echinocandins have lower adverse effects and toxicity in humans. Echinocandins are available in injectable (intravenous) form.

Methods

In this study, to identify the novel oral drug-like compounds that affect the fungal cell wall, downloaded oral drug-like compounds from the ZINC database were processed with a virtual screening procedure. The docking free energies were calculated and compared with the known inhibitor caspofungin. Four molecules were selected as the most potent ligands and subjected to hydrogen bonds analysis.

Results

Considering the hydrogen bond analysis, two compounds (ZINC71336662 and ZINC40910772) were predicted to better interact with the active site of β(1,3)-D-glucan synthase compared with caspofungin.

Conclusion

The introduced compound in this study may be valuable to analyze experimentally as a novel oral drug candidate targeting fungal cell walls.

Synthesis, experimental and in silico studies of N-fluorenylmethoxycarbonyl-O-tert-butyl-N-methyltyrosine, coupled with CSD data: a survey of interactions in the crystal structures of Fmoc-amino acids

Recently, fluorenylmethoxycarbonyl (Fmoc) amino acids (e.g. Fmoc-tyrosine or Fmoc-phenylalanine) have attracted growing interest in biomedical research and industry, with special emphasis directed towards the design and development of novel effective hydrogelators, biomaterials or therapeutics. With this in mind, a systematic knowledge of the structural and supramolecular features in recognition of those properties is essential. This work is the first comprehensive summary of noncovalent interactions combined with a library of supramolecular synthon patterns in all crystal structures of amino acids with the Fmoc moiety reported so far. Moreover, a new Fmoc-protected amino acid, namely, 2-{[(9H-fluoren-9-ylmethoxy)carbonyl](methyl)amino}-3-{4-[(2-hydroxypropan-2-yl)oxy]phenyl}propanoic acid or N-fluorenylmethoxycarbonyl-O-tert-butyl-N-methyltyrosine, Fmoc-N-Me-Tyr(t-Bu)-OH, C₂₉H₃₁NO₅, was successfully synthesized and the structure of its unsolvated form was determined by single-crystal X-ray diffraction. The structural, conformational and energy landscape was investigated in detail by combined experimental and in silico approaches, and further compared to N-Fmoc-phenylalanine [Draper et al. (2015). CrystEngComm, 42, 8047-8057]. Geometries were optimized by the density functional theory (DFT) method either in vacuo or in solutio. The polarizable conductor calculation model was exploited for the evaluation of the hydration effect. Hirshfeld surface analysis revealed that H...H, C...H/H...C and O...H/H...O interactions constitute the major contributions to the total Hirshfeld surface area in all the investigated systems. The molecular electrostatic potentials mapped over the surfaces identified the electrostatic complementarities in the crystal packing. The prediction of weak hydrogen-bonded patterns via Full Interaction Maps was computed. Supramolecular motifs formed via C-H...O, C-H...π, (fluorenyl)C-H...Cl(I), C-Br...π(fluorenyl) and C-I...π(fluorenyl) interactions are observed. Basic synthons, in combination with the Long-Range Synthon Aufbau Modules, further supported by energy-framework calculations, are discussed. Furthermore, the relevance of Fmoc-based supramolecular hydrogen-bonding patterns in biocomplexes are emphasized, for the first time.

Protein Engineering for Improved Health: Technological Perspectives

Protein engineering has enabled development of novel proteins aimed at disease diagnosis, alleviation and improved health attributes. The present article provides an overview of recent approaches and techniques used to modify proteins at diverse levels, which find therapeutically relevant applications. There is immense interest among researchers to discover new and increasingly valuable solutions for various health related issues and protein engineering could be a possible venue to sort out such problems. In this mini review we have tried to decipher some of the novel aspects of protein engineering in terms of protein-based therapeutics and diagnostics, in-silico tools and related approaches. A special emphasis has been given for some innovative aspects of protein-nanoparticle conjugates; use of artificial intelligence (AI)- based tools and post-translational modifications. Utilization of such approaches in protein engineering might be ground breaking in future research endeavor of researchers across the world.

Design, synthesis and biological evaluation of novel 2-(5-aryl-1H-imidazol-1-yl) derivatives as potential inhibitors of the HIV-1 Vpu and host BST-2 protein interaction

Novel ethyl 2-(5-aryl-1H-imidazol-1-yl)-acetates 17 and propionates 18, together with their acetic acid 19 and acetohydrazide 20 derivatives, were designed and synthesized using TosMIC chemistry. Biological evaluation of these newly synthesized scaffolds in the HIV-1 Vpu- Host BST-2 ELISA assay identified seven hits (17a, 17b, 17c, 17g, 18a, 20f and 20g) with greater than 50% inhibitory activity. These hits were validated in the HIV-1 Vpu- Host BST-2 AlphaScreen™ and six of the seven compounds were found to have comparable percentage inhibitory activities to those of the ELISA assay. Compounds 17b and 20g, with consistent percentage inhibitory activities across the two assays, had IC₅₀ values of 11.6 ± 1.1 μM and 17.6 ± 0.9 μM in a dose response AlphaScreen™ assay. In a cell-based HIV-1 antiviral assay, compound 17b exhibited an EC₅₀ = 6.3 ± 0.7 μM at non-toxic concentrations (CC₅₀ = 184.5 ± 0.8 μM), whereas compound 20g displayed antiviral activity roughly equivalent to its toxicity (CC₅₀ = 159.5 ± 0.9 μM). This data suggests that compound 17b, active in both cell-based and biochemical assays, provides a good starting point for the design of possible lead compounds for prevention of HIV-1 Vpu and host BST-2 protein binding in new anti-HIV therapeutics.

Mimicking Strategy for Protein-Protein Interaction Inhibitor Discovery by Virtual Screening

As protein–protein interactions (PPIs) are highly involved in most cellular processes, the discovery of PPI inhibitors that mimic the structure of the natural protein partners is a promising strategy toward the discovery of PPI inhibitors. In this review, we discuss recent advances in the application of virtual screening for identifying mimics of protein partners. The classification and function of the mimicking protein partner inhibitor discovery by virtual screening are described. We anticipate that this review would be of interest to medicinal chemists and chemical biologists working in the field of protein–protein interaction inhibitors or probes.

Fosfomycin: the characteristics, activity, and use in critical care

Fosfomycin (C₃H₇O₄P) is a phosphonic acid derivative representing an epoxide class of antibiotics. The drug is a re-emerging bactericidal antibiotic with a wide range of actions against several Gram-positive and Gram-negative bacteria. Among the existing antibacterial agents, fosfomycin has the lowest molecular weight (138 Da), which is not structurally associated with other classes of antibiotics. In intensive care unit (ICU) patients, severe soft tissue infections (STIs) may lead to serious life-threatening problems, and therefore, appropriate antibiotic therapy and often intensive care management (ICM) coupled with surgical intervention are necessary. Fosfomycin is an antibiotic primarily utilized for the treatment of STIs in ICUs. Recently, fosfomycin has attracted renewed interest for the treatment of serious systemic infections caused by multidrug-resistant Enterobacteriaceae. In some countries, intravenous fosfomycin has been prescribed for various serious systemic infections, such as acute osteomyelitis, nosocomial lower respiratory tract infections, complicated urinary tract infections, bacterial meningitis, and bacteremia. Administration of intravenous fosfomycin can result in a sufficient concentration of the drug at different body regions. Dose modification is not required in hepatic deficiency because fosfomycin is not subjected to enterohepatic circulation.