Homology modeling in the time of collective and artificial intelligence

An evaluation of the precision of computational methods used in drug development initiatives

Computational approaches are commonly employed to expedite and provide decision-making for the drug development process. Drug development programs that involve targets without known crystal structures can be quite challenging. In many cases, a viable approach is to generate reliable homology models using the amino acid sequence of the target. This is followed by a series of validation steps, druggable pocket detection, and then moving forward with lead identification and validation. This study commenced by conducting an initial benchmark exercise using a series of computationally designed sequences for steroid-binding proteins. By conducting an unbiased comparison with the released X-ray crystal structures, the homology models that were generated demonstrated reliable outcomes. The aligned homology models showed a root mean square deviation (RMSD) of less than 0.6 Å when compared to the corresponding X-ray structures. Three different methods were used to detect the druggable cavities for comparison, and the identified pockets closely resembled those of the crystal structures. The achievement of near-native pose prediction was made possible by utilizing the comprehensive binding energy function that characterizes the interaction between each pose and the neighboring residues. In order to address the issue of limited correlation between entropy and internal energy in docking, an alternative was devised by incorporating entropy as a post-docking optimization step to enhance the accuracy of ligand binding affinity predictions and improve the overall quality of the results.

Structural and functional prediction, evaluation, and validation in the post-sequencing era

The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.

Unraveling the physiochemical characteristics and molecular insights of Zein protein through structural modeling and conformational dynamics: a synergistic approach between machine learning and molecular dynamics simulations

This research article presents a comprehensive investigation into the three-dimensional structure, physicochemical characteristics and conformational stability of the Zein protein. Machine learning (ML) based homology modeling approach, was employed to predict the 3D structure of Zein protein. Convolutional neural networks (CNNs) were utilized for refining the model, capturing complex spatial features and improving decoy refinement. The predicted 3D structure of Zein protein showed a high-confidence score, i.e. C-score of 0.96. Physiochemical characteristic was also analyzed to investigate its protonation and deprotonation behavior across a range of pH values. A comprehensive analysis of the titration curve and electrostatic charges was performed to uncover valuable molecular insights into the zein protein's charge distribution, electrostatic interactions and potential conformational changes. Molecular dynamics (MD) simulations were performed to analyze the zein structural behavior under different pH values (2.0, 4.5, 6.8, 10.0 and 12.5), ionic strengths (0 mM, 25 mM, 50 mM, 75 mM, 100 mM) and temperatures (300K, 350K, 375K). Our results demonstrated the influence of these factors on zein protein's stability and conformational dynamics. At extreme pH values of 2.0 and 12.5, the Zein protein exhibited increased structural deviations and potential unfolding, while intermediate pH values closer to the protein's isoelectric point (pI) demonstrated more compact and stable conformations. Analysis of root mean square deviation, radius of gyration, solvent accessible surface area and Ramachandran plot provided clear understandings of the protein's compactness and surface exposure, confirming the impact of pH, ionic strength and temperature on the protein's conformation.

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

An overview on olfaction in the biological, analytical, computational, and machine learning fields

Recently, the comprehension of odor perception has advanced, unveiling the mysteries of the molecular receptors within the nasal passages and the intricate mechanisms governing signal transmission between these receptors, the olfactory bulb, and the brain. This review provides a comprehensive panorama of odors, encompassing various topics ranging from the structural and molecular underpinnings of odorous substances to the physiological intricacies of olfactory perception. It extends to elucidate the analytical methods used for their identification and explores the frontiers of computational methodologies.

A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance

Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01355-x.

Structure-based drug design for TSPO: Challenges and opportunities

The translocator protein 18 kDa (TSPO) is an evolutionarily conserved mitochondrial transmembrane protein implicated in various neuropathologies and inflammatory conditions, making it a longstanding diagnostic and therapeutic target of interest. Despite the development of various classes of TSPO ligand chemotypes, and the elucidation of bacterial and non-human mammalian experimental structures, many unknowns exist surrounding its differential structural and functional features in health and disease. There are several limitations associated with currently used computational methodologies for modelling the native structure and ligand-binding behaviour of this enigmatic protein. In this perspective, we provide a critical analysis of the developments in the uses of these methods, outlining their uses, inherent limitations, and continuing challenges. We offer suggestions of unexplored opportunities that exist in the use of computational methodologies which offer promise for enhancing our understanding of the TSPO.

Multicopper Oxidase from Lactobacillus hilgardii: Mechanism of Degradation of Tyramine and Phenylethylamine in Fermented Food

Elevated levels of biogenic amines (BAs) in fermented food can have negative effects on both the flavor and health. Mining enzymes that degrade BAs is an effective strategy for controlling their content. The study screened a strain of Lactobacillus hilgardii 1614 from fermented food system that can degrade BAs. The multiple copper oxidase genes LHMCO1614 were successfully mined after the whole genome protein sequences of homologous strains were clustered and followed by homology modeling. The enzyme molecules can interact with BAs to stabilize composite structures for catalytic degradation, as shown by molecular docking results. Ingeniously, the kinetic data showed that purified LHMCO1614 was less sensitive to the substrate inhibition of tyramine and phenylethylamine. The degradation rates of tyramine and phenylethylamine in huangjiu (18% vol) after adding LHMCO1614 were 41.35 and 40.21%, respectively. Furthermore, LHMCO1614 demonstrated universality in degrading tyramine and phenylethylamine present in other fermented foods as well. HS-SPME-GC-MS analysis revealed that, except for aldehydes, the addition of enzyme treatment did not significantly alter the levels of major flavor compounds in enzymatically treated fermented foods (p > 0.05). This study presents an enzymatic approach for regulating tyramine and phenylethylamine levels in fermented foods with potential applications both targeted and universal.

Epigenetic target identification strategy based on multi-feature learning

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

Development, validation and analysis of a human profurin 3D model using comparative modeling and molecular dynamics simulations

The emergence of new viruses can lead to the outbreak of pandemics as occurred at the end of 2019 with the coronavirus disease (or COVID-19). The fastest way to effectively control viral infections is to develop broad-spectrum antivirals that can fight at least an entire class of viruses. Profurin, the furin precursor propeptide, is responsible for the autoactivation step which is crucial for the maturation of several viral substrates. This role makes the study of furin and profurin interactions interesting for the development of new potential broad-spectrum antivirals for the treatment against several human viral diseases. Since there is no 3D model of profurin published in the literature or deposited in a database, this work reports the development, validation and analysis of a profurin 3D model using comparative modeling and molecular dynamics. The model is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-ct8l7. The usage of this model will make possible further studies of molecular docking and MD simulations of the profurin-furin system, in the design of new potential broad-spectrum antivirals for the treatment against several human viral diseases.Communicated by Ramaswamy H. Sarma.

Quercetin increases the antidepressant-like effects of sclareol and antagonizes diazepam in thiopental sodium-induced sleeping mice: A possible GABAergic transmission intervention

Quercetin is the most common polyphenolic flavonoid present in fruits and vegetables demonstrating versatile health-promoting effects. This study aimed to examine the effects of quercetin (QR) and sclareol (SCL) on the thiopental sodium (TS)-induced sleeping and forced swimming test (FST) mouse models. SCL (1, 5, and 10 mg/kg, p.o.) or QR (50 mg/kg, p.o.) and/or diazepam (DZP) (3 mg/kg, i.p.) were employed. After 30 min of TS induction, individual or combined effects on the animals were checked. In the FST test, the animals were subjected to forced swimming after 30 min of administration of the test and/or controls for 5 min. In this case, immobility time was measured. In silico studies were conducted to evaluate the involvement of GABA receptors. SCL (5 and 10 mg/kg) significantly increased the latency and decreased sleeping time compared to the control in the TS-induced sleeping time study. DZP (3 mg/kg) showed a sedative-like effect in animals in both sleeping and FST studies. QR (50 mg/kg) exhibited a similar pattern of activity as SCL. However, its effects were more prominent than those of SCL groups. SCL (10 mg/kg) altered the DZP-3-mediated effects. SCL-10 co-treated with QR-50 significantly (p < 0.05) increased the latency and decreased sleep time and immobility time, suggesting possible synergistic antidepressant-like effects. In silico studies revealed that SCL and QR demonstrated better binding affinities with GABAA receptor, especially α2, α3, and α5 subunits. Both compounds also exhibited good ADMET and drug-like properties. In animal studies, the both compounds worked synergistically to provide antidepressant-like effects in a slightly different fashion. As a conclusion, the combined administration of SCL and QR may be used in upcoming neurological clinical trials, according to in vivo and in silico findings. However, additional investigation is necessary to verify this behavior and clarify the potential mechanism of action.

Truncated variants of thyroid hormone receptor beta display disease-inflicting malfunctioning at cellular level

Thyroid hormone receptor β (THRβ) is a member of the nuclear receptor superfamily of ligand-modulated transcription factors. Upon ligand binding, THRβ sequentially recruits the components of transcriptional machinery to modulate target gene expression. In addition to regulating diverse physiological processes, THRβ plays a crucial role in hypothalamus-pituitary-thyroid axis feedback regulation. Anomalies in THRβ gene/protein structure are associated with onset of diverse disease states. In this study, we investigated disease-inflicting truncated variants of THRβ using in-silico analysis and cell-based assays. We examined the THRβ truncated variants on multiple test parameters, including subcellular localization, ligand-receptor interactions, transcriptional functions, interaction with heterodimeric partner RXR, and receptor-chromatin interactions. Moreover, molecular dynamic simulation approaches predicted that shortened THRβ-LBD due to point mutations contributes proportionally to the loss of structural integrity and receptor stability. Deviant subcellular localization and compromised transcriptional function were apparent with these truncated variants. Present study shows that 'mitotic bookmarking' property of some THRβ variants is also affected. The study highlights that structural and conformational attributes of THRβ are necessary for normal receptor functioning, and any deviations may contribute to the underlying cause of the inflicted diseases. We anticipate that insights derived herein may contribute to improved mechanistic understanding to assess disease predisposition.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

iBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models.

Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

MOTIVATION

The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection.

RESULTS

We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models.

AVAILABILITY AND IMPLEMENTATION

ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.

A tetravalent bispecific antibody outperforms the combination of its parental antibodies and neutralizes diverse SARS-CoV-2 variants

The devastating impact of COVID-19 on global health shows the need to increase our pandemic preparedness. Recombinant therapeutic antibodies were successfully used to treat and protect at-risk patients from COVID-19. However, the currently circulating Omicron subvariants of SARS-CoV-2 are largely resistant to therapeutic antibodies, and novel approaches to generate broadly neutralizing antibodies are urgently needed. Here, we describe a tetravalent bispecific antibody, A7A9 TVB, which actively neutralized many SARS-CoV-2 variants of concern, including early Omicron subvariants. Interestingly, A7A9 TVB neutralized more variants at lower concentration as compared to the combination of its parental monoclonal antibodies, A7K and A9L. A7A9 also reduced the viral load of authentic Omicron BA.1 virus in infected pseudostratified primary human nasal epithelial cells. Overall, A7A9 displayed the characteristics of a potent broadly neutralizing antibody, which may be suitable for prophylactic and therapeutic applications in the clinics, thus highlighting the usefulness of an effective antibody-designing approach.

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

Sclareol exerts synergistic antidepressant effects with quercetin and caffeine, possibly suppressing GABAergic transmission in chicks

This study evaluated the effects of sclareol (SCL) with or without caffeine (CAF) and quercetin (QUR) using in-vivo and in-silico studies. For this, 5-day-old chicks weighing between 45 and 48 g were randomly divided into five groups and treated accordingly. The chicks were monitored to compare the occurrence, latency, and duration of sleep as well as the loss and gain of righting reflex in response to SCL-10 mg/kg, CAF-10 mg/kg, and QUR-50 mg/kg using a thiopental sodium (TS)-induced sleeping model. Data were analyzed by one-way ANOVA followed by t-Student-Newman-Keuls' as a posthoc test at 95% confidence intervals with multiple comparisons. An in-silico study was also performed to investigate the possible antidepressant mechanisms of the test and/or standard drugs with different subunits of GABAA receptors. In comparison to the SCL, CAF, and QUR individual groups, SCL+CAF+QUR significantly increased the latency while decreasing the length of sleep. The incidence of loss and gain of the righting reflex was also modulated in the combination group. SCL showed better interaction with GABAA (α2 and α5) subunits than QUR with α2, α3, and α5. All these compounds showed stronger interactions with the GABAA receptor subunits than the standard CAF. Taken together, SCL, CAF, and QUR reduced the TS-induced righting reflex and sleeping time in the combination group more than in the individual treatments. SCL may show its antidepressant effects, possibly through interactions with GABAA receptor subunits.

Genetic insights into obesity: in silico identification of pathogenic SNPs in MBOAT4 gene and their structural molecular dynamics consequences

Membrane Bound O-Acyltransferase Domain-Containing 4 (MBOAT4) protein catalyzes ghrelin acylation, leading to prominent ghrelin activity, hence characterizing its role as an anti-obesity target. We extracted 625 exonic SNPs from the ENSEMBL database and one phenotype-based missense mutation associated with obesity (A46T) from the HGMD (Human Gene Mutation Database). These were differentiated on deleterious missense SNPs of the MBOAT4 gene through MAF (minor allele frequency: <0.01) cut-off criteria in relation to some bioinformatics-based supervised machine learning tools. We found 8 rare-coding and harmful missense SNPs. The consensus classifier (PredictSNP) tool predicted that the SNP (G57S, C: rs561065025) was the most pathogenic. Several trained in silico algorithms have predicted decreased protein stability [ΔΔG (kcal/mol)] function in the presence of these rare-coding pathogenic mutations in the MBOAT4 gene. Then, a stereochemical quality check (i.e. validation and assessment) of the 3D model was performed, followed by a blind cavity docking approach, used to search for druggable cavities and molecular interactions with citrus flavonoids of the Rutaceae family, ranked with energetic estimations. Significant interactions with Phloretin 3',5'-Di-C-Glucoside were also observed at R304, W306, N307, A311, L314 and H338 with (iGEMDOCK: -95.82 kcal/mol and AutoDock: -7.80 kcal/mol). The RMSD values and other variables of MD simulation analyses on this protein further validated its significant interactions with the above flavonoids. The MBOAT4 gene and its molecular interactions could serve as an interventional future anti-obesity target. The current study's findings will benefit future prospects for large population-based studies and drug development, particularly for generating personalized medicine.Communicated by Ramaswamy H. Sarma.

The influence of tag sequence on recombinant humanized collagen (rhCol) and the evaluation of rhCol on Schwann cell behaviors

Recombinant humanized collagen (rhCol) was an extracellular matrix (ECM)-inspired biomimetic biomaterial prepared by biosynthesis technology, which was considered non-allergenic and could possibly activate tissue regeneration. The influence of tag sequence on both structures and performances of rhCol type III (rhCol III) was investigated, and the effect of rhCol III on cell behaviors was evaluated and discussed using Schwann cells (SCs) as in vitro model that was critical in the repair process after peripheral nerve injury. The results demonstrated that the introduction of tag sequence would influence both advanced structures and properties of rhCol III, while rhCol III regulated SCs adhesion, spreading, migration and proliferation. Also, both nerve growth factor and brain-derived neurotrophic factor increased when exposed to rhCol III. As the downstream proteins of integrin-mediated cell adhesions, phosphorylation of focal adhesion kinase and expression of vinculin was up-regulated along with the promotion of SCs adhesion and migration. The current findings contributed to a better knowledge of the interactions between rhCol III and SCs, and further offered a theoretical and experimental foundation for the development of rhCol III-based medical devices and clinical management of peripheral nerve injury.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

SARS-CoV-2 proteins structural studies using synchrotron radiation

In the process of the development of structural biology, both the size and the complexity of the determined macromolecular structures have grown significantly. As a result, the range of application areas for the results of structural studies of biological macromolecules has expanded. Significant progress in the development of structural biology methods has been largely achieved through the use of synchrotron radiation. Modern sources of synchrotron radiation allow to conduct high-performance structural studies with high temporal and spatial resolution. Thus, modern techniques make it possible to obtain not only static structures, but also to study dynamic processes, which play a key role in understanding biological mechanisms. One of the key directions in the development of structural research is the drug design based on the structures of biomolecules. Synchrotron radiation offers insights into the three-dimensional time-resolved structure of individual viral proteins and their complexes at atomic resolution. The rapid and accurate determination of protein structures is crucial for understanding viral pathogenicity and designing targeted therapeutics. Through the application of experimental techniques, including X-ray crystallography and small-angle X-ray scattering (SAXS), it is possible to elucidate the structural details of SARS-CoV-2 virion containing 4 structural, 16 nonstructural proteins (nsp), and several accessory proteins. The most studied potential targets for vaccines and drugs are the structural spike (S) protein, which is responsible for entering the host cell, as well as nonstructural proteins essential for replication and transcription, such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp). This article provides a brief overview of structural analysis techniques, with focus on synchrotron radiation-based methods applied to the analysis of SARS-CoV-2 proteins.

Protein Modelling Highlighted Key Catalytic Sites Involved in Position-Specific Glycosylation of Isoflavonoids

Uridine diphosphate glycosyltransferases (UGTs) are known for promiscuity towards sugar acceptors, a valuable characteristic for host plants but not desirable for heterologous biosynthesis. UGTs characterized for the O-glycosylation of isoflavonoids have shown a variable efficiency, substrate preference, and OH site specificity. Thus, 22 UGTs with reported isoflavonoid O-glycosylation activity were analyzed and ranked for OH site specificity and catalysis efficiency. Multiple-sequence alignment (MSA) showed a 33.2% pairwise identity and 4.5% identical sites among selected UGTs. MSA and phylogenetic analysis highlighted a comparatively higher amino acid substitution rate in the N-terminal domain that likely led to a higher specificity for isoflavonoids. Based on the docking score, OH site specificity, and physical and chemical features of active sites, selected UGTs were divided into three groups. A significantly high pairwise identity (67.4%) and identical sites (31.7%) were seen for group 1 UGTs. The structural and chemical composition of active sites highlighted key amino acids that likely define substrate preference, OH site specificity, and glycosylation efficiency towards selected (iso)flavonoids. In conclusion, physical and chemical parameters of active sites likely control the position-specific glycosylation of isoflavonoids. The present study will help the heterologous biosynthesis of glycosylated isoflavonoids and protein engineering efforts to improve the substrate and site specificity of UGTs.

Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs

Side-chain rotamer prediction is one of the most critical late stages in protein 3D structure building. Highly advanced and specialized algorithms (e.g., FASPR, RASP, SCWRL4, and SCWRL4v) optimize this process by use of rotamer libraries, combinatorial searches, and scoring functions. We seek to identify the sources of key rotamer errors as a basis for correcting and improving the accuracy of protein modeling going forward. In order to evaluate the aforementioned programs, we process 2496 high-quality single-chained all-atom filtered 30% homology protein 3D structures and use discretized rotamer analysis to compare original with calculated structures. Among 513,024 filtered residue records, increased amino acid residue-dependent rotamer errors─associated in particular with polar and charged amino acid residues (ARG, LYS, and GLN)─clearly correlate with increased amino acid residue solvent accessibility and an increased residue tendency toward the adoption of non-canonical off rotamers which modeling programs struggle to predict accurately. Understanding the impact of solvent accessibility now appears key to improved side-chain prediction accuracies.

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer

Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the following factors including (1) lack of ligand-binding pockets, (2) function based on protein-protein interactions (PPIs), (3) the highly specific conserved active sites among protein family members, and (4) the variability of tertiary docking structures. The current status of undruggable targets proteins such as KRAS, TP53, C-MYC, PTP, are carefully introduced in this review. Some novel techniques and drug designing strategies have been applicated for overcoming these undruggable proteins, and the most classic and well-known technology is proteolysis targeting chimeras (PROTACs). In this review, the novel drug development strategies including targeting protein degradation, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding are described, and we also discuss the potential of these strategies for overcoming the undruggable targets. Besides, intelligence-assisted technologies like Alpha-Fold help us a lot to predict the protein structure, which is beneficial for drug development. The discovery of new targets and the development of drugs targeting them, especially those undruggable targets, remain a huge challenge. New drug development strategies, better extraction processes that do not disrupt protein-protein interactions, and more precise artificial intelligence technologies may provide significant assistance in overcoming these undruggable targets.

Computational approaches for molecular characterization and structure-based functional elucidation of a hypothetical protein from Mycobacterium tuberculosis

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

Molecular characterization, structural and expression analysis of twelve CXC chemokines and eight CXC chemokine receptors in largemouth bass (Micropterus salmoides)

The chemokine-receptor system plays important roles in the leukocyte trafficking, inflammation, immune cell differentiation, cancer and other biological processes. In the present study, the sequence features, structures and expression patterns of twelve CXC chemokine ligands (CXCL8a.1, CXCL8a.2, CXCL8b.1, CXCL8b.2, CXCL12a, CXCL12b, CXCL13.1, CXCL13.2, CXCL14, CXCL18a, CXCL18b and CXCL19) and eight CXC chemokine receptors (CXCR1, CXCR2, CXCR3.1, CXCR3.2, CXCR3.3, CXCR4a, CXCR4b and CXCR5) of largemouth bass (Micropterus salmoides) were analyzed. All the CXCLs and CXCRs of largemouth bass shared high sequence identities with their teleost counterparts and possessed conserved motifs and structures of CXCLs and CXCRs family. Realtime qPCR revealed that these CXCLs and CXCRs were ubiquitously expressed in all examined tissues, with high expression levels in the immune-related tissues (spleen, head kidney, and gill). Following lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (polyI:C) stimulations, most of these CXCLs and CXCRs were significantly up-regulated in spleen. In addition, the potential interacted molecules of these CXCLs and CXCRs were analyzed by protein-protein interaction network analysis. To the best of our knowledge, this is the first study that in detail analyzes the CXCLs and CXCRs of largemouth bass. Our results provide valuable basis for study the function and mechanism of chemokine-receptor system in largemouth bass.

Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship

Microbes live in diverse habitats (i.e. biomes), yet their species and genes were biome-specific, forming enrichment patterns. These enrichment patterns have mirrored the biome-species-function relationship, which is shaped by ecological and evolutionary principles. However, a grand picture of these enrichment patterns, as well as the roles of external and internal factors in driving these enrichment patterns, remain largely unexamined. In this work, we have examined the enrichment patterns based on 1705 microbiome samples from four representative biomes (Engineered, Gut, Freshwater, and Soil). Moreover, an "enrichment sphere" model was constructed to elucidate the regulatory principles behind these patterns. The driving factors for this model were revealed based on two case studies: (1) The copper-resistance genes were enriched in Soil biomes, owing to the copper contamination and horizontal gene transfer. (2) The flagellum-related genes were enriched in the Freshwater biome, due to high fluidity and vertical gene accumulation. Furthermore, this enrichment sphere model has valuable applications, such as in biome identification for metagenome samples, and in guiding 3D structure modeling of proteins. In summary, the enrichment sphere model aims towards creating a bluebook of the biome-species-function relationships and be applied in many fields.

Replica-exchange optimization of antibody fragments

In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.

Structural characterization of a novel luciferase-like-monooxygenase fromPseudomonas meliae– an in-silico approach.. [DOI: 10.1101/2023.03.27.534437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Luciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.

Methods

The present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.

Results

The results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.

Conclusions

This study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.

Improving CBCA synthase activity through rational protein design

Global interest for the minor cannabinoid cannabichromene (CBC) is growing steadily, as potential pharmaceutical applications continue to emerge. Due to low-yielding and unspecific extraction processes from its plant host Cannabis sativa, a biotechnological production is desirable. The complete heterologous biosynthesis of several other cannabinoids has recently been demonstrated as an accessible platform. However, the enzyme involved in the biosynthesis of CBC precursor cannabichromenic acid (CBCA) suffers from comparatively low catalytic efficiency, has not been crystallized, and remains poorly characterized. This study contributes to overcoming these challenges in three unique aspects. A deep‑learning‑assisted prediction of the CBCA synthase crystal structure using DeepMinds AlphaFold is performed and evaluated. The predicted CBCA synthase structure scored considerably higher in various quality assessments than the alternative template‑based homology modeling approach. A robust and practical understanding of crucial structure-function relationships for CBCA synthase is provided and a new binding mode for the substrate uncovered. Rational design approaches and computational analyses to suggest CBCAS variants with facilitated activity are applied. Through subsequent screening the substrate conversion of those variants is compared to the native enzyme. The best variant presented in this study increases CBCA production from crude lysate 22-fold and is one of five positions where substitutions had a significantly favorable impact on product formation.

Structural and functional characterization of RNA dependent RNA polymerase of Macrobrachium rosenbergii nodavirus (MnRdRp)

Macrobrachium rosenbergii is a highly valued farmed freshwater species and its production has been affected globally by white tail disease caused by M. rosenbergii nodavirus (MrNV). MrNV is a single stranded positive sense RNA virus encoding RNA-dependent RNA polymerase (RdRp) for genome replication. Due to its essentiality for pathogenesis, it is an important drug target. The domain prediction of the complete sequence revealed the presence of two enzymatic regions namely methyl transferase and RdRp separated by transmembrane region. The predicted three-dimensional (3D) structure of MnRdRp using AlphaFold 2 shows that the structure is composed of three major sub-domains common for other polymerases namely fingers, palm and thumb. Structural similarity search revealed its similarity with other flaviviridea members especially with BVDV RdRp (BvdvRdRp). The structure of fingers and palm sub-domains is more conserved than the thumb sub-domain. A small α-helix named 'priming helix' having conserve Tyr was identified at position 829-833 with a potential role in de novo initiation. Analysis of electrostatic potential revealed that nucleotide and template channels are electropositive. Metal binding residues were identified as Asp599, Asp704 and Asp705. The α and β phosphates of incoming nucleotide interact with two Mn2+, Arg455 and Arg537. For recognition of 2'-OH of incoming rNTP, Asp604, Ser661 and Asn670 were identified which can form H-bond network with 2'-OH group. Docking study revealed that Dasabuvir can potentially inhibit MnRdRp. The study concluded that the overall structure and function of MnRdRp are similar to Flaviviridae polymerases and their inhibitors can work against this enzyme.Communicated by Ramaswamy H. Sarma.

Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1

With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L104^3.32 and L255^6.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.

Design, Production, and Characterization of Catalytically Active Inclusion Bodies

Catalytically active inclusion bodies (CatIBs) are promising biologically produced enzyme/protein immobilizates for application in biocatalysis, synthetic chemistry, and biomedicine. CatIB formation is commonly induced by fusion of suitable aggregation-inducing tags to a given target protein. Heterologous production of the fusion protein in turn yields CatIBs. This chapter presents the methodology needed to design, produce, and characterize CatIBs.

Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures

With the surge of the high-throughput sequencing technologies, many genetic variants have been identified in the past decade. The vast majority of these variants are defined as variants of uncertain significance (VUS), as their significance to the function or health of an organism is not known. It is urgently needed to develop intelligent models for the clinical interpretation of VUS. State-of-the-art artificial intelligence (AI)-based variant effect predictors only learn features from primary amino acid sequences, leaving out information about the most important three-dimensional structure that is more related to its function. Methods: We proposed a deep convolutional neural network model named variant effect recognition network for BRCA1 (vERnet-B) to recognize the clinical pathogenicity of missense single-nucleotide variants in the BRCT domain of BRCA1. vERnet-B learned features associated with the pathogenicity from the tertiary protein structures of variants predicted by AlphaFold2. Results: After performing a series of validation and analyses on vERnet-B, we discovered that it exhibited significant advances over previous works. Recognizing the phenotypic consequences of VUS is one of the most daunting challenges in genetic informatics; however, we achieved 85% accuracy in recognizing disease BRCA1 variants with an ideal balance of false-positive and true-positive detection rates. vERnet-B correctly recognized the pathogenicity of variant A1708E, which was poorly predicted by AlphaFold2 as previously described. The vERnet-B web server is freely available from URL: http://ai-lab.bjrz.org.cn/vERnet. Conclusions: We applied protein tertiary structures to successfully recognize the pathogenic missense SNVs, which were difficult to be addressed by classical approaches based on sequences. Our work demonstrated that AlphaFold2-predicted structures were expected to be used for rich feature learning and revealed unique insights into the clinical interpretation of VUS in disease-related genes, using vERnet-B as a discovery tool.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Betaine Attenuates Chronic Constriction Injury-Induced Neuropathic Pain in Rats by Inhibiting KIF17-Mediated Nociception

Kinesin superfamily proteins transport a diverse range of cargo, including excitatory receptors to the dendrite and axon of a neuron via retrograde and anterograde fashions along microtubules, causing central sensitization and neuropathic pain. In this study, we have performed in silico molecular dynamics simulation to delineate the dynamic interaction of betaine with KIF17, a kinesin protein, known to be involved in neuropathic pain. The results from the molecular dynamics study suggest that the betaine-KIF17 complex is stabilized through hydrogen bonding, polar interactions, and water bridges. Findings from in vivo studies suggest a significant increase in pain hypersensitivity, oxido-nitrosative stress, and KIF17 overexpression in the sciatic nerve, dorsal root ganglion (DRG), and spinal cord of nerve-injured rats, which was significantly attenuated on treatment with betaine. Betaine treatment also restored the increased NR2B expressions and levels of proinflammatory cytokines and neuropeptides in the DRG and spinal cord of nerve-injured rats. Findings from the current study suggest that betaine attenuates neuropathic pain in rats by inhibiting KIF17-NR2B-mediated neuroinflammatory signaling.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

Phylogenetic Analysis and Protein Modelling of Isoflavonoid Synthase Highlights Key Catalytic Sites towards Realising New Bioengineering Endeavours

Isoflavonoid synthase (IFS) is a critical enzyme for the biosynthesis of over 2400 isoflavonoids. Isoflavonoids are an important class of plant secondary metabolites that have a range of pharmaceutical and nutraceutical properties. With growing interest in isoflavonoids from both research and industrial perspectives, efforts are being forwarded to enhance isoflavonoid production in-planta and ex-planta; therefore, in-silico analysis and characterisation of available IFS protein sequences are needed. The present study is the first-ever attempt toward phylogenetic analysis and protein modelling of available IFS protein sequences. Phylogenetic analysis has shown that IFS amino acid sequences have 86.4% pairwise identity and 26.5% identical sites, and the sequences were grouped into six different clades. The presence of a β-hairpin and extra loop at catalytic sites of Trifolium pratense, Beta vulgaris and Medicago truncatula, respectively, compared with Glycyrrhiza echinata are critical structural differences that may affect catalytic function. Protein docking highlighted the preference of selected IFS for liquiritigenin compared with naringenin and has listed T. pratense as the most efficient candidate for heterologous biosynthesis of isoflavonoids. The in-silico characterisation of IFS represented in this study is vital in realising the new bioengineering endeavours and will help in the characterisation and selection of IFS candidate enzymes for heterologous biosynthesis of isoflavonoids.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

Molecular Modeling of ABHD5 Structure and Ligand Recognition

Alpha/beta hydrolase domain-containing 5 (ABHD5), also termed CGI-58, is the key upstream activator of adipose triglyceride lipase (ATGL), which plays an essential role in lipid metabolism and energy storage. Mutations in ABHD5 disrupt lipolysis and are known to cause the Chanarin-Dorfman syndrome. Despite its importance, the structure of ABHD5 remains unknown. In this work, we combine computational and experimental methods to build a 3D structure of ABHD5. Multiple comparative and machine learning-based homology modeling methods are used to obtain possible models of ABHD5. The results from Gaussian accelerated molecular dynamics and experimental data of the apo models and their mutants are used to select the most likely model. Moreover, ensemble docking is performed on representative conformations of ABHD5 to reveal the binding mechanism of ABHD5 and a series of synthetic ligands. Our study suggests that the ABHD5 models created by deep learning-based methods are the best candidate structures for the ABHD5 protein. The mutations of E41, R116, and G328 disturb the hydrogen bonding network with nearby residues and suppress membrane targeting or ATGL activation. The simulations also reveal that the hydrophobic interactions are responsible for binding sulfonyl piperazine ligands to ABHD5. Our work provides fundamental insight into the structure of ABHD5 and its ligand-binding mode, which can be further applied to develop ABHD5 as a therapeutic target for metabolic disease and cancer.