Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets

Engineering the Active Site Pocket to Enhance the Catalytic Efficiency of a Novel Feruloyl Esterase Derived From Human Intestinal Bacteria Dorea formicigenerans

The human gut microbiota play essential roles in metabolism and human health, especially by enzymatically utilizing dietary fiber that the host cannot directly digest and releasing functional components including short-chain fatty acids (SCFAs) and hydroxycinnamic acids (e.g., ferulic acid). In our previous study, seven potential feruloyl esterase (FAE) genes were identified from the gut microbiota. In the current work, one of the genes encoding a novel FAE (DfFAE) from Dorea formicigenerans of Firmicutes was bacterially expressed, purified and characterized. The 30.5 kDa type-A DfFAE has an optimum pH and temperature of 8.4 and 40 °C, respectively, exhibiting a higher substrate specificity toward short-chain acyl-ester substrate (pNPA). The AlphaFold2 based ab initio structural modeling revealed a five α-helices cap domain that shaped an unusually narrow and deep active site pocket containing a specific substrate access tunnel in DfFAE. Furthermore, rational design strategy was subjected to the active site pocket in an aim of improving its enzymatic activities. The mutants V252A, N156A, W255A, P149A, and P186A showed 1.8 to 5.7-fold increase in catalytic efficiency toward pNPA, while W255A also exhibited altered substrate preference toward long-chain substrate pNPO (45.5-fold). This study highlighted an unusual active site architecture in DfFAE that influenced its substrate selectivity and illustrated the applicability of rational design for enhanced enzymatic properties.

Fungal Enzymes Involved in Plastics Biodegradation

Plastic pollution is a growing environmental problem, in part due to the extremely stable and durable nature of this polymer. As recycling does not provide a complete solution, research has been focusing on alternative ways of degrading plastic. Fungi provide a wide array of enzymes specialized in the degradation of recalcitrant substances and are very promising candidates in the field of plastic degradation. This review examines the present literature for different fungal enzymes involved in plastic degradation, describing their characteristics, efficacy and biotechnological applications. Fungal laccases and peroxidases, generally used by fungi to degrade lignin, show good results in degrading polyethylene (PE) and polyvinyl chloride (PVC), while esterases such as cutinases and lipases were successfully used to degrade polyethylene terephthalate (PET) and polyurethane (PUR). Good results were also obtained on PUR by fungal proteases and ureases. All these enzymes were isolated from many different fungi, from both Basidiomycetes and Ascomycetes, and have shown remarkable efficiency in plastic biodegradation under laboratory conditions. Therefore, future research should focus on the interactions between the genes, proteins, metabolites and environmental conditions involved in the processes. Further steps such as the improvement in catalytic efficiency and genetic engineering could lead these enzymes to become biotechnological applications in the field of plastic degradation.

Clade-Specific Alterations within the HIV-1 Capsid Protein with Implications for Nuclear Translocation

The HIV-1 capsid (CA) protein has emerged as an attractive therapeutic target. However, all inhibitor designs and structural analyses for this essential HIV-1 protein have focused on the clade B HIV-1 (NL4-3) variant. This study creates, overproduces, purifies, and characterizes the CA proteins from clade A1, A2, B, C, and D isolates. These new CA constructs represent novel reagents that can be used in future CA-targeted inhibitor design and to investigate CA proteins’ structural and biochemical properties from genetically diverse HIV-1 subtypes. Moreover, we used surface plasmon resonance (SPR) spectrometry and computational modeling to examine inter-clade differences in CA assembly and binding of PF-74, CPSF-6, and NUP-153. Interestingly, we found that HIV-1 CA from clade A1 does not bind to NUP-153, suggesting that the import of CA core structures through the nuclear pore complex may be altered for viruses from this clade. Overall, we have demonstrated that in silico generated models of the HIV-1 CA protein from clades other than the prototypically used clade B have utility in understanding and predicting biology and antiviral drug design and mechanism of action.

Anti-Virulence Activity of 3,3′-Diindolylmethane (DIM): A Bioactive Cruciferous Phytochemical with Accelerated Wound Healing Benefits

Antimicrobial resistance is among the top global health problems with antibacterial resistance currently representing the major threat both in terms of occurrence and complexity. One reason current treatments of bacterial diseases are ineffective is the occurrence of protective and resistant biofilm structures. Phytochemicals are currently being reviewed for newer anti-virulence agents. In the present study, we aimed to investigate the anti-virulence activity of 3,3′-diindolylmethane (DIM), a bioactive cruciferous phytochemical. Using a series of in vitro assays on major Gram-negative pathogens, including transcriptomic analysis, and in vivo porcine wound studies as well as in silico experiments, we show that DIM has anti-biofilm activity. Following DIM treatment, our findings show that biofilm formation of two of the most prioritized bacterial pathogens Acinetobacter baumannii and Pseudomonas aeruginosa was inhibited respectively by 65% and 70%. Combining the antibiotic tobramycin with DIM enabled a high inhibition (94%) of P. aeruginosa biofilm. A DIM-based formulation, evaluated for its wound-healing efficacy on P. aeruginosa-infected wounds, showed a reduction in its bacterial bioburden, and wound size. RNA-seq was used to evaluate the molecular mechanism underlying the bacterial response to DIM. The gene expression profile encompassed shifts in virulence and biofilm-associated genes. A network regulation analysis showed the downregulation of 14 virulence-associated super-regulators. Quantitative real-time PCR verified and supported the transcriptomic results. Molecular docking and interaction profiling indicate that DIM can be accommodated in the autoinducer- or DNA-binding pockets of the virulence regulators making multiple non-covalent interactions with the key residues that are involved in ligand binding. DIM treatment prevented biofilm formation and destroyed existing biofilm without affecting microbial death rates. This study provides evidence for bacterial virulence attenuation by DIM.

ProteinsPlus: a comprehensive collection of web-based molecular modeling tools

Upon the ever-increasing number of publicly available experimentally determined and predicted protein and nucleic acid structures, the demand for easy-to-use tools to investigate these structural models is higher than ever before. The ProteinsPlus web server (https://proteins.plus) comprises a growing collection of molecular modeling tools focusing on protein–ligand interactions. It enables quick access to structural investigations ranging from structure analytics and search methods to molecular docking. It is by now well-established in the community and constantly extended. The server gives easy access not only to experts but also to students and occasional users from the field of life sciences. Here, we describe its recently added new features and tools, beyond them a novel method for on-the-fly molecular docking and a search method for single-residue substitutions in local regions of a protein structure throughout the whole Protein Data Bank. Finally, we provide a glimpse into new avenues for the annotation of AlphaFold structures which are directly accessible via a RESTful service on the ProteinsPlus web server.

Homology modeling, virtual screening and MD simulations for identification of NUAK1 and ULK1 potential dual inhibitors

Cancer cells produce more reactive oxygen species (ROS) due to their severe metabolic stress. SNF1 like kinase 1 (NUAK1) is the key part of the cellular antioxidant system. Inhibiting the...

Structure-Based Virtual Screening of Benzaldehyde Thiosemicarbazone Derivatives against DNA Gyrase B of Mycobacterium tuberculosis

Emergence of antibiotic-resistant Mycobacterium tuberculosis (M. tuberculosis) restricts the availability of drugs for the treatment of tuberculosis, which leads to the increased morbidity and mortality of the disease worldwide. There are many intrinsic and extrinsic factors that have been reported for the resistance mechanism. To overcome such mechanisms, chemically synthesized benzaldehyde thiosemicarbazone derivatives were screened against M. tuberculosis to find potential inhibitor for tuberculosis. Such filtering process resulted in compound 13, compound 21, and compound 20 as the best binding energy compounds against DNA gyrase B, an important protein in the replication process. The ADMET prediction has shown the oral bioavailability of the novel compounds.

AroCageDB: A Web-Based Resource for Aromatic Cage Binding Sites and Their Intrinsic Ligands

While aromatic cages have extensively been investigated in the context of structural biology, molecular recognition, and drug discovery, there exist to date no comprehensive resource for proteins sharing this conserved structural motif. To this end, we parsed the Protein Data Bank and thus constructed the Aromatic Cage Database (AroCageDB), a database for investigating the binding pocket descriptors and ligand binding space of aromatic-cage-containing proteins (ACCPs). AroCageDB contains 487 unique ACCPs bound to 890 unique ligands, for a total of 1636 complexes. This web-accessible database provides a user-friendly interface for the interactive visualization of ligand-bound ACCP structures, with a variety of search options that will open up opportunities for structural analyses and drug discovery campaigns. AroCageDB is freely available at http://www.pharmbioinf.uni-freiburg.de/arocagedb/.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

The Antagonist pGlu-βGlu-Pro-NH2 Binds to an Allosteric Site of the Thyrotropin-Releasing Hormone Receptor

After we identified pGlu-βGlu-Pro-NH2 as the first functional antagonist of the cholinergic central actions of the thyrotropin-releasing hormone (TRH, pGlu-His-Pro-NH2), we became interested in finding the receptor-associated mechanism responsible for this antagonism. By utilizing a human TRH receptor (hTRH-R) homology model, we first refined the active binding site within the transmembrane bundle of this receptor to enhance TRH's binding affinity. However, this binding site did not accommodate the TRH antagonist. This directed us to consider a potential allosteric binding site in the extracellular domain (ECD). Searches for ECD binding pockets prompted the remodeling of the extracellular loops and the N-terminus. We found that different trajectories of ECDs produced novel binding cavities that were then systematically probed with TRH, as well as its antagonist. This led us to establish not only a surface-recognition binding site for TRH, but also an allosteric site that exhibited a selective and high-affinity binding for pGlu-βGlu-Pro-NH2. The allosteric binding of this TRH antagonist is more robust than TRH's binding to its own active site. The findings reported here may shed light on the mechanisms and the multimodal roles by which the ECD of a TRH receptor is involved in agonist and/or antagonist actions.

Isohemigossypolone: Antiophidic properties of a naphthoquinone isolated from Pachira aquatica Aubl

We investigated the antiophidic properties of isohemigossypolone (ISO), a naphthoquinone isolated from the outer bark of the Pachira aquatic Aubl. The inhibition of phospholipase A₂, coagulant, fibrinogenolytic, hemorrhagic and myotoxic activities induced by Bothrops pauloensis venom (Pb) was investigated. For this, we use samples resulting from the incubation of Pb with ISO in different concentrations (1:1, 1:5 and 1:10 w/w), we also evaluated the condition of treatment using ISO after 15 min of venom inoculation. The activities of phospholipase A₂, coagulant, fibrinogenolytic, hemorrhagic and myotoxic induced by the B. pauloensis venom were significantly inhibited when the ISO was pre-incubated with the crude venom. For in vivo neutralization tests, the results were observed even when the ISO was applied after 15 min of inoculation of the venom or metalloprotease (BthMP). Also, to identify the inhibition mechanism, we performed in silico assays, across simulations of molecular coupling and molecular dynamics, it was possible to identify the modes of interaction between ISO and bothropic toxins BmooMPα-I, Jararacussin-I and BNSP-7. The present study shows that naphthoquinone isohemigossypolone isolated from the P. aquatica plant inhibited part of the local and systemic damage caused by venom proteins, demonstrating the pharmacological potential of this compound in neutralizing the harmful effects caused by snakebites.

Identification of known drugs as potential SARS-CoV-2 Mpro inhibitors using ligand- and structure-based virtual screening

Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment – in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy – application of ligand- and structure-based virtual screening – using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.

Finding Druggable Sites in Proteins Using TACTICS

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (trajectory-based analysis of conformations to identify cryptic sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.

Fungal enzymes for the degradation of polyethylene: Molecular docking simulation and biodegradation pathway proposal

Polyethylene (PE) is one of the most highly consumed petroleum-based polymers and its accumulation as waste causes environmental pollution. In this sense, the use of microorganisms and their enzymes represents the most ecofriendly and effective decontamination approach. In this work, molecular docking simulation for catalytic enzyme degradation of PE was carried out using individual enzymes: laccase (Lac), manganese peroxidase (MnP), lignin peroxidase (LiP) and unspecific peroxygenase (UnP). PE-binding energy, PE-binding affinity and dimensions of PE-binding sites in the enzyme cavity were calculated in each case. Four hypothetical PE biodegradation pathways were proposed using individual enzymes, and one pathway was proposed using a synergic enzyme combination. These results show that in nature, enzymes act in a synergic manner, using their specific features to undertake an extraordinarily effective sequential catalytic process for organopollutants degradation. In this process, Lac (oxidase) is crucial to provide hydrogen peroxide to the medium to ensure pollutant breakdown. UnP is a versatile enzyme that offers a promising practical application for the degradation of PE and other pollutants due to its cavity features. This is the first in silico report of PE enzymatic degradation, showing the mode of interaction of PE with enzymes as well as the degradation mechanism.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

RNR inhibitor binding studies of Chlamydia felis: insights from in silico molecular modeling, docking, and simulation studies

Chlamydia felis is the primary cause of chronic conjunctivitis without respiratory infections in cats, making conjunctiva as its primary target. It is a Gram-negative obligate intracellular bacterium that cannot survive outside the host cell. C. felis can be found worldwide and its zoonotic potential is a known phenomenon. The scope of zoonoses, its scale, and their impact experiencing today has no historical precedence. Among the identified 1415 human pathogens 868 have a zoonotic origin making it to 61%. Although with appropriate drug administration there are instances of re-occurrence of chlamydial infections, the emergence of heterotypic antimicrobial resistance to antibiotics targeting rRNA due to mutations has further complicated the diagnosis and treatment of chlamydial infections. Ribonucleotide-diphosphate reductase subunit beta (RNR) is one of the crucial target proteins of the bacterial pathogens essential in the synthesis of deoxyribonucleotides. Our current study primarily focuses on modeling the target structure through homology modeling. Further, the validated model is complexed with the specific inhibitor Cladribine through sequence-based ligand search. Docking of the identified ligand was performed to identify the different modes of interactions with amino acids present in the prioritized binding pockets. Validation of the binding modes is carried out through molecular dynamics (MD) simulations for the best binding pose with a high binding score. MD simulation study demonstrated the stability of the docked complex considered in this study. The findings from this study may be helpful in drug repurposing and novel drug research in the scenario of resistance to currently practiced antibiotics.Communicated by Ramaswamy H. Sarma.

Systematic in silico Evaluation of Leishmania spp. Proteomes for Drug Discovery

Leishmaniasis is a group of neglected infectious diseases, with approximately 1. 3 million new cases each year, for which the available therapies have serious limitations. Therefore, it is extremely important to apply efficient and low-cost methods capable of selecting the best therapeutic targets to speed up the development of new therapies against those diseases. Thus, we propose the use of integrated computational methods capable of evaluating the druggability of the predicted proteomes of Leishmania braziliensis and Leishmania infantum, species responsible for the different clinical manifestations of leishmaniasis in Brazil. The protein members of those proteomes were assessed based on their structural, chemical, and functional contexts applying methods that integrate data on molecular function, biological processes, subcellular localization, drug binding sites, druggability, and gene expression. These data were compared to those extracted from already known drug targets (BindingDB targets), which made it possible to evaluate Leishmania proteomes for their biological relevance and treatability. Through this methodology, we identified more than 100 proteins of each Leishmania species with druggability characteristics, and potential interaction with available drugs. Among those, 31 and 37 proteins of L. braziliensis and L. infantum, respectively, have never been tested as drug targets, and they have shown evidence of gene expression in the evolutionary stage of pharmacological interest. Also, some of those Leishmania targets showed an alignment similarity of <50% when compared to the human proteome, making these proteins pharmacologically attractive, as they present a reduced risk of side effects. The methodology used in this study also allowed the evaluation of opportunities for the repurposing of compounds as anti-leishmaniasis drugs, inferring potential interaction between Leishmania proteins and ~1,000 compounds, of which only 15 have already been tested as a treatment for leishmaniasis. Besides, a list of potential Leishmania targets to be tested using drugs described at BindingDB, such as the potential interaction of the DEAD box RNA helicase, TRYR, and PEPCK proteins with the Staurosporine compound, was made available to the public.

Assessing the Molecular Targets and Mode of Action of Furanone C-30 on Pseudomonas aeruginosa Quorum Sensing

Quorum sensing (QS), a sophisticated system of bacterial communication that depends on population density, is employed by many pathogenic bacteria to regulate virulence. In view of the current reality of antibiotic resistance, it is expected that interfering with QS can address bacterial pathogenicity without stimulating the incidence of resistance. Thus, harnessing QS inhibitors has been considered a promising approach to overriding bacterial infections and combating antibiotic resistance that has become a major threat to public healthcare around the globe. Pseudomonas aeruginosa is one of the most frequent multidrug-resistant bacteria that utilize QS to control virulence. Many natural compounds, including furanones, have demonstrated strong inhibitory effects on several pathogens via blocking or attenuating QS. While the natural furanones show no activity against P. aeruginosa, furanone C-30, a brominated derivative of natural furanone compounds, has been reported to be a potent inhibitor of the QS system of the notorious opportunistic pathogen. In the present study, we assess the molecular targets and mode of action of furanone C-30 on P. aeruginosa QS system. Our results suggest that furanone C-30 binds to LasR at the ligand-binding site but fails to establish interactions with the residues crucial for the protein's productive conformational changes and folding, thus rendering the protein dysfunctional. We also show that furanone C-30 inhibits RhlR, independent of LasR, suggesting a complex mechanism for the agent beyond what is known to date.

Mode of inhibitory binding of epigallocatechin gallate to the ubiquitin-activating enzyme Uba1 via accelerated molecular dynamics

The green tea polyphenol (−)-epigallocatechin-3-gallate (EGCG) and some of its analogs potently inhibit the ubiquitin-activating enzyme Uba1. In an effort to understand the possible molecular basis of inhibitory activity of EGCG, we conducted a molecular docking and molecular dynamics simulation study. We found that EGCG and its two selected analogs, (−)-epicatechin-3-gallate (ECG) and (−)-epigallocatechin (EGC), bind favorably at two likely hot spots for small-molecule ligand binding on human Uba1. The compounds bind with energetics that mirror their experimental potency for inhibition of Uba1∼ubiquitin thioester formation. The binding of EGCG, ECG, and EGC at one of the hot spots, in particular, recapitulates the rank order of potency determined experimentally and suggests a possible mechanism for inhibition. A hinge-like conformational change of the second catalytic cysteine domain and the opposing ubiquitin-fold domain observed during accelerated molecular dynamics simulations of the EGCG-bound Uba1 complex that results in disruption of the ubiquitin-binding interfaces could explain the compounds' inhibitory activity. These results shed light on the possible molecular mechanism of EGCG and related catechins in the inhibition of Uba1.

The hinge-like movement of the SCCH domain upon ligand binding closes the ubiquitin binding site and disrupts the interfaces crucial for thioester bond formation.

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods with a Focus on FunFOLD3

Proteins are essential molecules with a diverse range of functions; elucidating their biological and biochemical characteristics can be difficult and time consuming using in vitro and/or in vivo methods. Additionally, in vivo protein-ligand binding site elucidation is unable to keep place with current growth in sequencing, leaving the majority of new protein sequences without known functions. Therefore, the development of new methods, which aim to predict the protein-ligand interactions and ligand-binding site residues directly from amino acid sequences, is becoming increasingly important. In silico prediction can utilise either sequence information, structural information or a combination of both. In this chapter, we will discuss the broad range of methods for ligand-binding site prediction from protein structure and we will describe our method, FunFOLD3, for the prediction of protein-ligand interactions and ligand-binding sites based on template-based modelling. Additionally, we will describe the step-by-step instructions using the FunFOLD3 downloadable application along with examples from the Critical Assessment of Techniques for Protein Structure Prediction (CASP) where FunFOLD3 has been used to aid ligand and ligand-binding site prediction. Finally, we will introduce our newer method, FunFOLD3-D, a version of FunFOLD3 which aims to improve template-based protein-ligand binding site prediction through the integration of docking, using AutoDock Vina.

Computational study of pomegranate peel extract polyphenols as potential inhibitors of SARS-CoV-2 virus internalization

The search for effective coronavirus disease (COVID-19) therapy has attracted a great deal of scientific interest due to its unprecedented health care system overload worldwide. We have carried out a study to investigate the in silico effects of the most abundant pomegranate peel extract constituents on the multi-step process of serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) internalization in the host cells. Binding affinities and interactions of ellagic acid, gallic acid, punicalagin and punicalin were studied on four selected protein targets with a significant and confirmed role in the process of the entry of virus into a host cell. The protein targets used in this study were: SARS-CoV-2 spike glycoprotein, angiotensin-converting enzyme 2, furin and transmembrane serine protease 2. The results showed that the constituents of pomegranate peel extracts, namely punicalagin and punicalin had very promising potential for significant interactions with the selected protein targets and were therefore deemed good candidates for further in vitro and in vivo evaluation.

Redirecting cytotoxic T cells with chemically programmed antibodies

T-cell engaging bispecific antibodies (T-biAbs) mediate potent and selective cytotoxicity by combining specificities for target and effector cells in one molecule. Chemically programmed T-biAbs (cp-T-biAbs) are precisely assembled compositions of (i) small molecules that govern cancer cell surface targeting with high affinity and specificity and (ii) antibodies that recruit and activate T cells and equip the small molecule with confined biodistribution and longer circulatory half-life. Conceptually similar to cp-T-biAbs, switchable chimeric antigen receptor T cells (sCAR-Ts) can also be put under the control of small molecules by using a chemically programmed antibody as a bispecific adaptor molecule. As such, cp-T-biAbs and cp-sCAR-Ts can endow small molecules with the power of cancer immunotherapy. We here review the concept of chemically programmed antibodies for recruiting and activating T cells as a promising strategy for broadening the utility of small molecules in cancer therapy.

Computational characterisation of Toxoplasma gondii FabG (3-oxoacyl-[acyl-carrier-protein] reductase): a combined virtual screening and all-atom molecular dynamics simulation study

Toxoplasma gondii is an opportunistic obligate parasite, ubiquitous around the globe with seropositivity rates that range from 10% to 90% and infection by the parasite of pregnant women causes pre-natal death of the foetus in most cases and severe neurodegenerative syndromes in some. No vaccine is currently available, and since drug-resistance is common among T. gondii strains, discovering lead compounds for drug design using diverse tactics is necessary. In this study, the sole constituent isoform of an enzymatic 3-oxoacyl-[acyl-carrier-protein] reduction step in an apicoplast-located fatty acid biosynthesis pathway was chosen as a possible drug target. FASII is prokaryotic therefore, targeting it would pose fewer side-effects to human hosts. After a homology 3D modelling of TgFabG, a high-throughput virtual screening of 9867 compounds, the elimination of ligands was carried out by a flexible ligand molecular docking and 200 ns molecular dynamics simulations, with additional DCCM and PC plot analyses. Molecular Dynamics and related post-MD analyses of the top 3 TgFabG binders selected for optimal free binding energies, showed that L2 maintained strong H-bonds with TgFabG and facilitated structural reorientation expected of FabGs, namely an expansion of the Rossmann Fold and a flexible lid capping. The most flexible TgFabG sites were the α7 helix (the flexible lid region) and the β4-α4 and β5-α6 loops. For TgFabG-L2, the movements of these regions toward the active site enabled greater ligand stability. Thus, L2 ("Skimmine"; PubChem ID: 320361), was ultimately selected as the optimal candidate for the discovery of lead compounds for rational drug design.Communicated by Ramaswamy H. Sarma.

Unlocking COVID therapeutic targets: A structure-based rationale against SARS-CoV-2, SARS-CoV and MERS-CoV Spike

There are no approved target therapeutics against SARS-CoV-2 or other beta-CoVs. The beta-CoV Spike protein is a promising target considering the critical role in viral infection and pathogenesis and its surface exposed features. We performed a structure-based strategy targeting highly conserved druggable regions resulting from a comprehensive large-scale sequence analysis and structural characterization of Spike domains across SARSr- and MERSr-CoVs. We have disclosed 28 main consensus druggable pockets within the Spike. The RBD and SD1 (S1 subunit); and the CR, HR1 and CH (S2 subunit) represent the most promising conserved druggable regions. Additionally, we have identified 181 new potential hot spot residues for the hSARSr-CoVs and 72 new hot spot residues for the SARSr- and MERSr-CoVs, which have not been described before in the literature. These sites/residues exhibit advantageous structural features for targeted molecular and pharmacological modulation. This study establishes the Spike as a promising anti-CoV target using an approach with a potential higher resilience to resistance development and directed to a broad spectrum of Beta-CoVs, including the new SARS-CoV-2 responsible for COVID-19. This research also provides a structure-based rationale for the design and discovery of chemical inhibitors, antibodies or other therapeutic modalities successfully targeting the Beta-CoV Spike protein.

Novel method to identify group-specific non-catalytic pockets of human kinome for drug design

Kinase proteins have been intensively investigated as drug targets for decades because of their crucial involvement in many biological pathways. We developed hybrid approach to identify non-catalytic pockets and will benefit the kinome drug design.

HKPocket: human kinase pocket database for drug design

Background

The kinase pocket structural information is important for drug discovery targeting cancer or other diseases. Although some kinase sequence, structure or drug databases have been developed, the databases cannot be directly used in the kinase drug study. Therefore, a comprehensive database of human kinase protein pockets is urgently needed to be developed.

Results

Here, we have developed HKPocket, a comprehensive Human Kinase Pocket database. This database provides sequence, structure, hydrophilic-hydrophobic, critical interactions, and druggability information including 1717 pockets from 255 kinases. We further divided these pockets into 91 pocket clusters using structural and position features in each kinase group. The pocket structural information would be useful for preliminary drug screening. Then, the potential drugs can be further selected and optimized by analyzing the sequence conservation, critical interactions, and hydrophobicity of identified drug pockets. HKPocket also provides online visualization and pse files of all identified pockets.

Conclusion

The HKPocket database would be helpful for drug screening and optimization. Besides, drugs targeting the non-catalytic pockets would cause fewer side effects. HKPocket is available at http://zhaoserver.com.cn/HKPocket/HKPocket.html.

CavBench: A benchmark for protein cavity detection methods

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

PrankWeb: a web server for ligand binding site prediction and visualization

PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

Glycine-induced formation and druggability score prediction of protein surface pockets

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.

Polyurethanases: Three-dimensional structures and molecular dynamics simulations of enzymes that degrade polyurethane

The global production of plastics increases every year, because these materials are widely used in several segments of modern life. Polyurethanes are a very important class of polymers, used in many areas of everyday life, from automotive equipments to mattresses. The waste management usually involves accumulation in landfills, incineration, and reuse processes. However, bioremediation processes are being increasingly tested, due to the efficiency of enzymes in the degradation, besides adding value to the generated waste. Several experimental tests indicate that hydrolases, such as proteases, ureases, and esterases, are able to degrade polyurethanes. In this work, the three-dimensional structure of enzymes that are experimentally know to degrade polyurethanes were obtained for the first time, by the technique of homology modeling. The theoretical models showed good stereochemical quality and through molecular dynamics simulations analysis it was observed the stability of the structures. The molecular docking results indicated that all ligands, monomers of polyurethane, showed favorable interactions with the modeled enzymes.

P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

Background

Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.

Results

We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.

Conclusions

P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.

In silico identification and analysis of the binding site for aminocoumarin type inhibitors in the C-terminal domain of Hsp90

Hsp90 contains two Nucleotide Binding Sites (NBS): one each in its N-terminal domain (NTD) and C-terminal domain (CTD), respectively. Previously we used computational techniques to locate a nucleotide-binding site in the CTD. Nucleotide binding at this site stabilized the structurally labile region within this domain, thus providing a rationale for increased resistance to thermal denaturation and proteolysis. A scan for ligand-binding sites in CTD revealed four potential sites with the requisite volume to accommodate aminocoumarins and -derived inhibitors. Only one of these reproducibly formed docked complexes with inhibitors and showed excellent interactions with residues lining the site. Fortuitously, it was identical to the aforementioned nucleotide-binding site thus providing an explanation for the reported direct competition between inhibitors and nucleotides. Further studies with carefully chosen inhibitors and some inactive analogues provided an explanation for the known Structure-Activity Relationships (SAR) of aminocoumarin and -derived inhibitors. We also performed similar studies of the NTD to discern the reason(s) for its inability to bind aminocoumarins, given the family resemblance to prokaryotic Top-IV and Gyr-B. Our studies permitted the identification of the putative inhibitor binding site in the CTD, an explanation for increased resistance to thermal denaturation and proteolysis upon inhibitor binding as well as direct competition with ATP.

A Comparative Study of Human Saposins

Saposins are small proteins implicated in trafficking and loading of lipids onto Cluster of Differentiation 1 (CD1) receptor proteins that in turn present lipid antigens to T cells and a variety of T-cell receptors, thus playing a crucial role in innate and adaptive immune responses in humans. Despite their low sequence identity, the four types of human saposins share a similar folding pattern consisting of four helices linked by three conserved disulfide bridges. However, their lipid-binding abilities as well as their activities in extracting, transporting and loading onto CD1 molecules a variety of sphingo- and phospholipids in biological membranes display two striking characteristics: a strong pH-dependence and a structural change between a compact, closed conformation and an open conformation. In this work, we present a comparative computational study of structural, electrostatic, and dynamic features of human saposins based upon their available experimental structures. By means of structural alignments, surface analyses, calculation of pH-dependent protonation states, Poisson-Boltzmann electrostatic potentials, and molecular dynamics simulations at three pH values representative of biological media where saposins fulfill their function, our results shed light into their intrinsic features. The similarities and differences in this class of proteins depend on tiny variations of local structural details that allow saposins to be key players in triggering responses in the human immune system.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.

GPU-based detection of protein cavities using Gaussian surfaces

Background

Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity.

Results

We solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them.

Conclusion

This article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

Targeting Unoccupied Surfaces on Protein-Protein Interfaces

The use of peptidomimetic scaffolds to target protein-protein interfaces is a promising strategy for inhibitor design. The strategy relies on mimicry of protein motifs that exhibit a concentration of native hot spot residues. To address this constraint, we present a pocket-centric computational design strategy guided by AlphaSpace to identify high-quality pockets near the peptidomimetic motif that are both targetable and unoccupied. Alpha-clusters serve as a spatial representation of pocket space and are used to guide the selection of natural and non-natural amino acid mutations to design inhibitors that optimize pocket occupation across the interface. We tested the strategy against a challenging protein-protein interaction target, KIX/MLL, by optimizing a single helical motif within MLL to compete against the full-length wild-type MLL sequence. Molecular dynamics simulation and experimental fluorescence polarization assays are used to verify the efficacy of the optimized peptide sequence.

Investigating the Importance of the Pocket-estimation Method in Pocket-based Approaches: An Illustration Using Pocket-ligand Classification

Small molecules interact with their protein target on surface cavities known as binding pockets. Pocket-based approaches are very useful in all of the phases of drug design. Their first step is estimating the binding pocket based on protein structure. The available pocket-estimation methods produce different pockets for the same target. The aim of this work is to investigate the effects of different pocket-estimation methods on the results of pocket-based approaches. We focused on the effect of three pocket-estimation methods on a pocket-ligand (PL) classification. This pocket-based approach is useful for understanding the correspondence between the pocket and ligand spaces and to develop pharmacological profiling models. We found pocket-estimation methods yield different binding pockets in terms of boundaries and properties. These differences are responsible for the variation in the PL classification results that can have an impact on the detected correspondence between pocket and ligand profiles. Thus, we highlighted the importance of the pocket-estimation method choice in pocket-based approaches.