ProBiS: a web server for detection of structurally similar protein binding sites

An AlphaFold Structure Analysis of COQ2 as Key a Component of the Coenzyme Q Synthesis Complex

Coenzyme Q (CoQ) is a lipidic compound that is widely distributed in nature, with crucial functions in metabolism, protection against oxidative damage and ferroptosis and other processes. CoQ biosynthesis is a conserved and complex pathway involving several proteins. COQ2 is a member of the UbiA family of transmembrane prenyltransferases that catalyzes the condensation of the head and tail precursors of CoQ, which is a key step in the process, because its product is the first intermediate that will be modified in the head by the next components of the synthesis process. Mutations in this protein have been linked to primary CoQ deficiency in humans, a rare disease predominantly affecting organs with a high energy demand. The reaction catalyzed by COQ2 and its mechanism are still unknown. Here, we aimed at clarifying the COQ2 reaction by exploring possible substrate binding sites using a strategy based on homology, comprising the identification of available ligand-bound homologs with solved structures in the Protein Data Bank (PDB) and their subsequent structural superposition in the AlphaFold predicted model for COQ2. The results highlight some residues located on the central cavity or the matrix loops that may be involved in substrate interaction, some of which are mutated in primary CoQ deficiency patients. Furthermore, we analyze the structural modifications introduced by the pathogenic mutations found in humans. These findings shed new light on the understanding of COQ2's function and, thus, CoQ's biosynthesis and the pathogenicity of primary CoQ deficiency.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

A unique binding pocket induced by a noncanonical SAH mimic to develop potent and selective PRMT inhibitors

Protein arginine methyltransferases (PRMTs) are attractive targets for developing therapeutic agents, but selective PRMT inhibitors targeting the cofactor SAM binding site are limited. Herein, we report the discovery of a noncanonical but less polar SAH surrogate YD1113 by replacing the benzyl guanidine of a pan-PRMT inhibitor with a benzyl urea, potently and selectively inhibiting PRMT3/4/5. Importantly, crystal structures reveal that the benzyl urea moiety of YD1113 induces a unique and novel hydrophobic binding pocket in PRMT3/4, providing a structural basis for the selectivity. In addition, YD1113 can be modified by introducing a substrate mimic to form a "T-shaped" bisubstrate analogue YD1290 to engage both the SAM and substrate binding pockets, exhibiting potent and selective inhibition to type I PRMTs (IC50 < 5 nmol/L). In summary, we demonstrated the promise of YD1113 as a general SAH mimic to build potent and selective PRMT inhibitors.

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

A Molecular Docking Study of Human STEAP2 for the Discovery of New Potential Anti-Prostate Cancer Chemotherapeutic Candidates

Prostate cancer is a rising health concern and accounts for 3.8% of all cancer deaths globally. Uganda has one of the highest incidence rates of the disease in Africa at 5.2% with the majority of diagnosed patients found to have advanced disease. This study aimed to use the STEAP2 protein (prostate cancer–specific biomarker) for the discovery of new targeted therapy. To determine the most likely compound that can bind to the STEAP2 protein, we docked the modeled STEAP2 3D structure against 2466 FDA (Food and Drug Administration)-approved drug candidates using AutoDock Vina. Protein basic local alignment search tool (BLASTp) search, multiple sequence alignment (MSA), and phylogenetics were further carried out to analyze the diversity of this marker and determine its conserved domains as suitable target regions. Six promising drug candidates (ligands) were identified. Triptorelin had the highest binding energy (−12.1 kcal/mol) followed by leuprolide (docking energy: −11.2 kcal/mol). All the top two drug candidates interacted with residues Ser-372 and Gly-369 in close proximity with the iron-binding domain (an important catalyst of metal reduction). The two drugs had earlier been approved for the treatment of advanced prostate cancer with an elusive mode of action. Through this study, further insight into figuring out their interaction with STEAP2 might be important during treatment.

Computational Prediction of lncRNA-Protein Interactions using Machine learning

Long non-coding RNAs have generated much scientific interest because of their functional significance in regulating various biological processes and also their dysfunction has been implicated in disease progression. LncRNAs usually bind with proteins to perform their function. The experimental approaches for identifying these interactions are time taking and expensive. Lately, numerous method on predicting lncRNA-protein interactions have been reported yet, they all have some prevalent drawbacks that limit their prediction performance. In this research, we proposed a computational method based on a similarity scheme that integrates features derived from sequence and structure similarities. When compared with the state of the art, the proposed method has achieved highest performance with accuracy and F1 measure of 98.6% and 98.7% using XGBoost as classifier. Our results showed that by combining sequence and structure based features the lncRNA protein interactions can be better predicted and can also complement the experimental techniques for this task.Clinical Relevance- The lncRNA-protein interactions play significant role in regulating various biological processes. This can help in providing early diagnosis and better treatment for cancer related diseases.

Selective DNA Gyrase Inhibitors: Multi-Target in Silico Profiling with 3D-Pharmacophores

DNA gyrase is an important target for the development of novel antibiotics. Although ATP-competitive DNA gyrase (GyrB) inhibitors are a well-studied class of antibacterial agents, there is currently no representative used in therapy, largely due to unwanted off-target activities. Selectivity of GyrB inhibitors against closely related human ATP-binding enzymes should be evaluated early in development to avoid off-target binding to homologous binding domains. To address this challenge, we developed selective 3D-pharmacophore models for GyrB, human topoisomerase IIα (TopoII), and the Hsp90 N-terminal domain (NTD) to be used in in silico activity profiling paradigms to identify molecules selective for GyrB over TopoII and Hsp90, as starting points for hit expansion and lead optimization. The models were used to profile highly active GyrB, TopoII, and Hsp90 inhibitors. Selected compounds were tested in in vitro assays. GyrB inhibitors 1 and 2 were inactive against TopoII and Hsp90, while 3 and 4, potent Hsp90 inhibitors, displayed no inhibition of GyrB and TopoII, and TopoII inhibitors 5 and 6 were inactive at GyrB and Hsp90. The results provide a proof of concept for the use of target activity profiling methods to identify selective starting points for hit and lead identification.

PvP01-DB: computational structural and functional characterization of soluble proteome of PvP01 strain of Plasmodium vivax

Despite Plasmodium vivax being the main offender in the majority of malarial infections, very little information is available about its adaptation and development in humans. Its capability for activating relapsing infections through its dormant liver stage and resistance to antimalarial drugs makes it as one of the major challenges in eradicating malaria. Noting the immediate necessity for the availability of a comprehensive and reliable structural and functional repository for P. vivax proteome, here we developed a web resource for the new reference genome, PvP01, furnishing information on sequence, structure, functions, active sites and metabolic pathways compiled and predicted using some of the state-of-the-art methods in respective fields. The PvP01 web resource comprises organized data on the soluble proteome consisting of 3664 proteins in blood and liver stages of malarial cycle. The current public resources represent only 163 proteins of soluble proteome of PvP01, with complete information about their molecular function, biological process and cellular components. Also, only 46 proteins of P. vivax have experimentally determined structures. In this milieu of extreme scarcity of structural and functional information, PvP01 web resource offers meticulously validated structures of 3664 soluble proteins. The sequence and structure-based functional characterization led to a quantum leap from 163 proteins available presently to whole soluble proteome offered through PvP01 web resource. We believe PvP01 web resource will serve the researchers in identifying novel protein drug targets and in accelerating the development of structure-based new drug candidates to combat malaria.

Database Availability: http://www.scfbio-iitd.res.in/PvP01

Synthesis, Molecular Docking Screening and Anti-Proliferative Potency Evaluation of Some New Imidazo[2,1-b]Thiazole Linked Thiadiazole Conjugates

BACKGROUND

Imidazo[2,1-b]thiazole scaffolds were reported to possess various pharmaceutical activities.

RESULTS

The novel compound named methyl-2-(1-(3-methyl-6-(p-tolyl)imidazo[2,1-b]thiazol-2-yl)ethylidene)hydrazine-1-carbodithioate 3 acted as a predecessor molecule for the synthesis of new thiadiazole derivatives incorporating imidazo[2,1-b]thiazole moiety. The reaction of 3 with the appropriate hydrazonoyl halide derivatives 4a-j and 7-9 had produced the respective 1,3,4-thiadiazole derivatives 6a-j and 10-12. The chemical composition of all the newly synthesized derivatives were confirmed by their microanalytical and spectral data (FT-IR, mass spectrometry, ¹H-NMR and ¹³C-NMR). All the produced novel compounds were screened for their anti-proliferative efficacy on hepatic cancer cell lines (HepG₂). In addition, a computational molecular docking study was carried out to determine the ability of the synthesized thiadiazole molecules to interact with active site of the target Glypican-3 protein (GPC-3). Moreover, the physiochemical properties of the synthesized compounds were derived to determine the viability of the compounds as drug candidates for hepatic cancer.

CONCLUSION

All the tested compounds had exhibited good anti-proliferative efficacy against hepatic cancer cell lines. In addition, the molecular docking results showed strong binding interactions of the synthesized compounds with the target GPC-3 protein with lower energy scores. Thus, such novel compounds may act as promising candidates as drugs against hepatocellular carcinoma.

Synthesis, Characterization, Antibacterial Activity, and Computer-Aided Design of Novel Quinazolin-2,4-dione Derivatives as Potential Inhibitors Against Vibrio cholerae

Cholera is a bacterial disease featured by dehydration and severe diarrhea. It is mainly caused by alimentary infection with Vibrio cholerae. Due to the wide applicability of quinazolin-2,4-dione compounds in medicinal and pharmaceutical chemistry, a new series of N-containing heterocyclic compounds was synthesized. We used the in silico docking method to test the efficacy of quinazolin-2,4-dione compounds in the prevention of cholera in humans. The newly synthesized compounds showed strong interactions and good binding affinity to outer membrane protein OmpU. Moreover, the pharmacokinetic properties of the newly synthesized compounds, such as absorption, distribution, metabolic, excretion, and toxicity (ADMET), were predicted through in silico methods. Compounds with acceptable pharmacokinetic properties were tested as novel ligand molecules. The synthesized compounds were evaluated in vitro for their antibacterial activity properties against Gram-negative Escherichia coli O78 strain using the minimum inhibition concentration (MIC) method. Compounds 2 and 6 showed reproducible, effective antibacterial activity. Hence, our study concludes that the quinazolin-2,4-dione derivatives 1 to 8 may be used as promising drug candidates with potential value for the treatment of cholera disease.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation

Background

Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.

Results

In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I_rmsd value of 3.28Å and overall F_nat value of 63%, which improves upon I_rmsd of 3.89Å and F_nat of 49% for ZRANK, and I_rmsd of 3.99Å and F_nat of 46% for ClusPro. On CAPRI targets, our method achieves average I_rmsd value of 3.45Å and overall F_nat value of 46%, which improves upon I_rmsd of 4.18Å and F_nat of 40% for ZRANK, and I_rmsd of 5.12Å and F_nat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively.

Conclusion

Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

In Silico Laboratory: Tools for Similarity-Based Drug Discovery

Computational methods that predict and evaluate binding of ligands to receptors implicated in different pathologies have become crucial in modern drug design and discovery. Here, we describe protocols for using the recently developed package of computational tools for similarity-based drug discovery. The ProBiS stand-alone program and web server allow superimposition of protein structures against large protein databases and predict ligands based on detected binding site similarities. GenProBiS allows mapping of human somatic missense mutations related to cancer and non-synonymous single nucleotide polymorphisms and subsequent visual exploration of specific interactions in connection to these mutations. We describe protocols for using LiSiCA, a fast ligand-based virtual screening software that enables easy screening of large databases containing billions of small molecules. Finally, we show the use of BoBER, a web interface that enables user-friendly access to a large database of bioisosteric and scaffold hopping replacements.

Insight into the binding of a synthetic nitro-flavone derivative with human poly (ADP-ribose) polymerase 1

Flavones are important bioactive compounds, many of which are effective in cancer therapy for their ability to target enzymes related to DNA repair and cell proliferation. In this report, the interaction of a synthetic nitroflavone, 2,4-nitrophenylchromen-4-one (4NCO) with human poly (ADP-ribose) polymerase 1 (hPARP1) was investigated to explore its inhibitory action. Its interaction with hPARP1 was compared with that of other inhibitors through molecular docking studies. Further insight into the 4NCO-hPARP1 interaction was obtained from competitive docking and molecular dynamic simulation studies. In silico mutagenesis studies and per-residue interaction energy calculations were carried out. Quantitative Structure Activity Relationship analysis was also performed to calculate its predictive percent inhibitory activity. Our results indicated that 4NCO exhibited competitive mode of binding to hPARP1. It formed a stable interaction with the protein thereby hindering any further molecular interaction to render it inactive with a predictive inhibition of 96%. It also had good ADMET properties and showed best Autodock binding free energy values compared to other known inhibitors. 4NCO showed good hPARP1 inhibitory properties with higher bioavailability and lower probability of getting effluxed. Development of inhibitors against hPARP1 is important for cell proliferative disorders, where 4NCO can be predicted as a potential new drug.

Computational drug repurposing to predict approved and novel drug-disease associations

The Drug often binds to more than one targets defined as polypharmacology, one application of which is drug repurposing also referred as drug repositioning or therapeutic switching. The traditional drug discovery and development is a high-priced and tedious process, thus making drug repurposing a popular alternate strategy. We proposed an integrative method based on similarity scheme that predicts approved and novel Drug targets with new disease associations. We combined PPI, biological pathways, binding site structural similarities and disease-disease similarity measures. The results showed 94% Accuracy with 0.93 Recall and 0.94 Precision measure in predicting the approved and novel targets surpassing the existing methods. All these parameters help in elucidating the unknown associations between drug and diseases for finding the new uses for old drugs.

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson's Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity ( K_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.

PvaxDB: a comprehensive structural repository of Plasmodium vivax proteome

The severity of malaria caused by Plasmodium vivax worldwide and its resistance against the available general antimalarial drugs has created an urgent need for a comprehensive insight into its biology and biochemistry for developing some novel potential vaccines and therapeutics. P.vivax comprises 5392 proteins mostly predicted, out of which 4211 are soluble proteins and 2205 of these belong to blood and liver stages of malarial cycle. Presently available public resources report functional annotation (gene ontology) of only 28% (627 proteins) of the enzymatic soluble proteins and experimental structures are determined for only 42 proteins P. vivax proteome. In this milieu of severe paucity of structural and functional data, we have generated structures of 2205 soluble proteins, validated them thoroughly, identified their binding pockets (including active sites) and annotated their function increasing the coverage from the existing 28% to 100%. We have pooled all this information together and created a database christened as PvaxDB, which furnishes extensive sequence, structure, ligand binding site and functional information. We believe PvaxDB could be helpful in identifying novel protein drug targets, expediting development of new drugs to combat malaria. This is also the first attempt to create a reliable comprehensive computational structural repository of all the soluble proteins of P. vivax.

Database URL: http://www.scfbio-iitd.res.in/PvaxDB

BoBER: web interface to the base of bioisosterically exchangeable replacements

We describe a novel freely available web server Base of Bioisosterically Exchangeable Replacements (BoBER), which implements an interface to a database of bioisosteric and scaffold hopping replacements. Bioisosterism and scaffold hopping are key concepts in drug design and optimization, and can be defined as replacements of biologically active compound's fragments with other fragments to improve activity, reduce toxicity, change bioavailability or to diversify the scaffold space. Our web server enables fast and user-friendly searches for bioisosteric and scaffold replacements which were obtained by mining the whole Protein Data Bank. The working of the web server is presented on an existing MurF inhibitor as example. BoBER web server enables medicinal chemists to quickly search for and get new and unique ideas about possible bioisosteric or scaffold hopping replacements that could be used to improve hit or lead drug-like compounds.

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov.

Elucidating a chemical defense mechanism of Antarctic sponges: A computational study

In 2000, a novel secondary metabolite (erebusinone, Ereb) was isolated from the Antarctic sea sponge, Isodictya erinacea. The bioactivity of Ereb was investigated, and it was found to inhibit molting when fed to the arthropod species Orchomene plebs. Xanthurenic acid (XA) is a known endogenous molt regulator present in arthropods. Experimental studies have confirmed that XA inhibits molting by binding to either (or both) of two P450 enzymes (CYP315a1 or CYP314a1) that are responsible for the final two hydroxylations in the production of the molt-inducing hormone, 20-hydroxyecdysone (20E). The lack of crystal structures and biochemical assays for CYP315a1 or CYP314a1, has prevented further experimental exploration of XA and Ereb's molt inhibition mechanisms. Herein, a wide array of computational techniques - homology modeling, molecular dynamics simulations, binding site bioinformatics, flexible receptor-flexible ligand docking, and molecular mechanics-generalized Born surface area calculations - have been employed to elucidate the structure-function relationships between the aforementioned P450s and the two described small molecule inhibitors (Ereb and XA). Results indicate that Ereb likely targets CYP315a1 by interacting with a network of aromatic residues in the binding site, while XA may inhibit both CYP315a1 and CYP314a1 because of its aromatic, as well as charged nature.

Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach

Background

Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures.

Results

Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins.

Conclusions

Geomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0131-9) contains supplementary material, which is available to authorized users.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery

As a follow up to the antimycobacterial screening exercise and the release of GSK´s first Tres Cantos Antimycobacterial Set (TCAMS-TB), this paper presents the results of a second antitubercular screening effort of two hundred and fifty thousand compounds recently added to the GSK collection. The compounds were further prioritized based on not only antitubercular potency but also on physicochemical characteristics. The 50 most attractive compounds were then progressed for evaluation in three different predictive computational biology algorithms based on structural similarity or GSK historical biological assay data in order to determine their possible mechanisms of action. This effort has resulted in the identification of novel compounds and their hypothesized targets that will hopefully fuel future TB drug discovery and target validation programs alike.

Modeling enzyme-ligand binding in drug discovery

Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.

Structural neighboring property for identifying protein-protein binding sites

Background

The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface.

Results

We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall F_nat value by at least 3%. On Benchmark v4.0, our method has average I_rmsd value of 3.31Å and overall F_nat value of 63%, which improves upon I_rmsd of 3.89 Å and F_nat of 49% for ZRANK, and I_rmsd of 3.99Å and F_nat of 46% for ClusPro. On the CAPRI targets, our method has average I_rmsd value of 3.46 Å and overall F_nat value of 45%, which improves upon I_rmsd of 4.18 Å and F_nat of 40% for ZRANK, and I_rmsd of 5.12 Å and F_nat of 32% for ClusPro.

Conclusions

Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria.

Evolution of Macromolecular Docking Techniques: The Case Study of Nickel and Iron Metabolism in Pathogenic Bacteria

The interaction between macromolecules is a fundamental aspect of most biological processes. The computational techniques used to study protein-protein and protein-nucleic acid interactions have evolved in the last few years because of the development of new algorithms that allow the a priori incorporation, in the docking process, of experimentally derived information, together with the possibility of accounting for the flexibility of the interacting molecules. Here we review the results and the evolution of the techniques used to study the interaction between metallo-proteins and DNA operators, all involved in the nickel and iron metabolism of pathogenic bacteria, focusing in particular on Helicobacter pylori (Hp). In the first part of the article we discuss the methods used to calculate the structure of complexes of proteins involved in the activation of the nickel-dependent enzyme urease. In the second part of the article, we concentrate on two applications of protein-DNA docking conducted on the transcription factors HpFur (ferric uptake regulator) and HpNikR (nickel regulator). In both cases we discuss the technical expedients used to take into account the conformational variability of the multi-domain proteins involved in the calculations.

Structural and functional influences of coagulation factor XIII subunit B heterozygous missense mutants

The coagulation factor XIII(FXIII) is a plasma circulating heterotetrameric protransglutaminase that acts at the end of the coagulation cascade by covalently cross-linking preformed fibrin clots (to themselves and to fibrinolytic inhibitors) in order to stabilize them against fibrinolysis. It circulates in the plasma as a heterotetramer composed of two homomeric catalytic Factor XIIIA₂ (FXIIIA₂) and two homomeric protective/carrier Factor XIIIB₂ subunit (FXIIIB₂). Congenital deficiency of FXIII is of two types: severe homozygous/compound heterozygous FXIII deficiency which results in severe bleeding symptoms and mild heterozygous FXIII deficiency which is associated with mild bleeding (only upon trauma) or an asymptomatic phenotype. Defects in the F13B gene (Factor XIIIB subunit) occur more frequently in mild FXIII deficiency patients than in severe FXIII deficiency. We had recently reported secretion-related defects for seven previously reported F13B missense mutations. In the present study we further analyze the underlying molecular pathological mechanisms as well as the heterozygous expression phenotype for these mutations using a combination of in vitro heterologous expression (in HEK293T cells) and confocal microscopy. In combination with the in vitro work we have also performed an in silico solvated molecular dynamic simulation study on previously reported FXIIIB subunit sushi domain homology models in order to predict the putative structure-functional impact of these mutations. We were able to categorize the mutations into the following functional groups that: (1) affect antigenic stability as well as binding to FXIIIA subunit, that is, Cys5Arg, Cys316Phe, and Pro428Ser (2) affect binding to FXIIIA subunit with little or no influence on antigenic stability, that is, Ile81Asn and Val401Gln c) influence neither aspects and are most likely causality linked polymorphisms or functional polymorphisms, that is, Leu116Phe and Val217Ile. The Cys5Arg mutation was the only mutation to show a direct secretion-based defect since the mutated protein was observed to accumulate in the endoplasmic reticulum.

Ligand-target prediction by structural network biology using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development.

Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process. Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compound’s binding mode in the binding site of the target protein. Here, we introduce a new computational method, called nAnnoLyze, which uses graph theory principles to relate compounds and target proteins based on comparative principles. nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization. Our results suggest that the nAnnoLyze accuracy and proteome-wide applicability enables the large-scale annotation and analysis of compound–protein interaction and thus may benefit drug development.

Methods for predicting protein-ligand binding sites

Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or to facilitate docking computations in virtual screening based drug design. The prediction usually requires only the three-dimensional structure (experimentally determined or computationally modeled) of the target protein to be searched for ligand binding site(s), and Web servers have been built, allowing the free and simple use of prediction tools. In this chapter, we review the underlying concepts of the methods used by various tools, and discuss their different features and the related issues of ligand binding site prediction. Some cautionary notes about the use of these tools are also provided.

Fosfomycin induced structural change in fosfomycin resistance kinases FomA: molecular dynamics and molecular docking studies

Fosfomycin resistance kinases FomA, one of the key enzymes responsible for bacterial resistances to fosfomycin, has gained much attention recently due to the raising public concern for multi-drug resistant bacteria. Using molecular docking followed by molecular dynamics simulations, our group illustrated the process of fosfomycin induced conformational change of FomA. The detailed roles of the catalytic residues (Lys18, His58 and Thr210) during the formation of the enzyme-substrate complex were shown in our research. The organization functions of Gly53, Gly54, Ile61 and Leu75 were also highlighted. Furthermore, the cation-π interaction between Arg62 and Trp207 was observed and speculated to play an auxiliary role in the conformation change process of the enzyme. This detailed molecular level illustration of the formation of FomA·ATP·Mg·Fosfomycin complex could provide insight for both anti-biotic discovery and improvement of fosfomycin in the future.

Identification and Characterization of Noncovalent Interactions That Drive Binding and Specificity in DD-Peptidases and β-Lactamases

Bacterial resistance to standard (i.e., β-lactam-based) antibiotics has become a global pandemic. Simultaneously, research into the underlying causes of resistance has slowed substantially, although its importance is universally recognized. Key to unraveling critical details is characterization of the noncovalent interactions that govern binding and specificity (DD-peptidases, antibiotic targets, versus β-lactamases, the evolutionarily derived enzymes that play a major role in resistance) and ultimately resistance as a whole. Herein, we describe a detailed investigation that elicits new chemical insights into these underlying intermolecular interactions. Benzylpenicillin and a novel β-lactam peptidomimetic complexed to the Stremptomyces R61 peptidase are examined using an arsenal of computational techniques: MD simulations, QM/MM calculations, charge perturbation analysis, QM/MM orbital analysis, bioinformatics, flexible receptor/flexible ligand docking, and computational ADME predictions. Several key molecular level interactions are identified that not only shed light onto fundamental resistance mechanisms, but also offer explanations for observed specificity. Specifically, an extended π–π network is elucidated that suggests antibacterial resistance has evolved, in part, due to stabilizing aromatic interactions. Additionally, interactions between the protein and peptidomimetic substrate are identified and characterized. Of particular interest is a water-mediated salt bridge between Asp217 and the positively charged N-terminus of the peptidomimetic, revealing an interaction that may significantly contribute to β-lactam specificity. Finally, interaction information is used to suggest modifications to current β-lactam compounds that should both improve binding and specificity in DD-peptidases and their physiochemical properties.

How does catalase release nitric oxide? A computational structure-activity relationship study

Hydroxyurea (HU) is the only FDA approved medication for treating sickle cell disease in adults. The primary mechanism of action is pharmacological elevation of nitric oxide (NO) levels which induces propagation of fetal hemoglobin. HU is known to undergo redox reactions with heme based enzymes like hemoglobin and catalase to produce NO. However, specific details about the HU based NO release remain unknown. Experimental studies indicate that interaction of HU with human catalase compound I produces NO. Presently, we combine flexible receptor-flexible substrate induced fit docking (IFD) with energy decomposition analyses to examine the atomic level details of a possible key step in the clinical conversion of HU to NO. Substrate binding modes of nine HU analogs with catalase compound I were investigated to determine the essential properties necessary for effective NO release. Three major binding orientations were found that provide insight into the possible reaction mechanisms for producing NO. Further results show that anion/radical intermediates produced as part of these mechanisms would be stabilized by hydrogen bonding interactions from distal residues His75, Asn148, Gln168, and oxoferryl-heme. These details will ideally contribute to both a clearer mechanistic picture and provide insights for future structure based drug design efforts.

Structurally conserved binding sites of hemagglutinin as targets for influenza drug and vaccine development

ProBiS is a new method to identify the binding site of protein through local structural alignment against the nonredundant Protein Data Bank (PDB), which may result in unique findings compared to the energy-based, geometry-based, and sequence-based predictors. In this work, binding sites of Hemagglutinin (HA), which is an important target for drugs and vaccines in influenza treatment, have been revisited by ProBiS. For the first time, the identification of conserved binding sites by local structural alignment across all subtypes and strains of HA available in PDB is presented. ProBiS finds three distinctive conserved sites on HA's structure (named Site 1, Site 2, and Site 3). Compared to other predictors, ProBiS is the only one that accurately defines the receptor binding site (Site 1). Apart from that, Site 2, which is located slightly above the TBHQ binding site, is proposed as a potential novel conserved target for membrane fusion inhibitor. Lastly, Site 3, located around Helix A at the stem domain and recently targeted by cross-reactive antibodies, is predicted to be conserved in the latest H7N9 China 2013 strain as well. The further exploration of these three sites provides valuable insight in optimizing the influenza drug and vaccine development.

Catalytic site identification--a web server to identify catalytic site structural matches throughout PDB

The catalytic site identification web server provides the innovative capability to find structural matches to a user-specified catalytic site among all Protein Data Bank proteins rapidly (in less than a minute). The server also can examine a user-specified protein structure or model to identify structural matches to a library of catalytic sites. Finally, the server provides a database of pre-calculated matches between all Protein Data Bank proteins and the library of catalytic sites. The database has been used to derive a set of hypothesized novel enzymatic function annotations. In all cases, matches and putative binding sites (protein structure and surfaces) can be visualized interactively online. The website can be accessed at http://catsid.llnl.gov.

Structure of the UreD-UreF-UreG-UreE complex in Helicobacter pylori: a model study

The molecular details of the protein complex formed by UreD, UreF, UreG, and UreE, accessory proteins for urease activation in the carcinogenic bacterium Helicobacter pylori, have been elucidated using computational modeling. The calculated structure of the complex supports the hypothesis of UreF acting as a GTPase activation protein that facilitates GTP hydrolysis by UreG during urease maturation, and provides a rationale for the design of new drugs against infections by ureolytic bacterial pathogens.

The Effect of CYP2B6, CYP2D6, and CYP3A4 Alleles on Methadone Binding: A Molecular Docking Study

Current methadone maintenance therapy (MMT) is yet to ensure 100% successful treatment as the optimum dosage has yet to be determined. Overdose leads to death while lower dose causes the opioid withdrawal effect. Single-nucleotide polymorphisms (SNP) in cytochrome P450s (CYPs), the methadone metabolizers, have been showen to be the main factor for the interindividual variability of methadone clinical effects. In this study, we investigated the effect of SNPs in three major methadone metabolizers (CYP2B6, CYP2D6, and CYP3A4) on methadone binding affinity. Results showed thatCYP2B6*11,CYP2B6*12,CYP2B6*18, andCYP3A4*12have significantly higher binding affinity toR-methadone compared to wild type.S-methadone has higher binding affinity inCYP3A4*3,CYP3A4*11, andCYP3A4*12compared to wild type.R-methadone was shown to be the active form of methadone; thus individuals with CYP alleles that binds better toR-methadone will have higher methadone metabolism rate. Therefore, a higher dosage of methadone is necessary to obtain the opiate effect compared to a normal individual and vice versa. These results provide an initial prediction on methadone metabolism rate for individuals with mutant type CYP which enables prescription of optimum methadone dosage for individuals with CYP alleles.

Theoretical investigation on structural, functional and epitope of a 12 kDa excretory-secretory protein from Toxoplasma gondii

BACKGROUND

Toxoplasma gondii is an intracellular coccidian parasite that causes toxoplasmosis. It was estimated that more than one third of the world population is infected by T. gondii, and the disease is critical in fetuses and immunosuppressed patients. Thus, early detection is crucial for disease diagnosis and therapy. However, the current available toxoplasmosis diagnostic tests vary in their accuracy and the better ones are costly.

RESULTS

An earlier published work discovered a highly antigenic 12 kDa excretory-secretory (ES) protein of T. gondii which may potentially be used for the development of an antigen detection test for toxoplasmosis. However, the three-dimensional structure of the protein is unknown. Since epitope identification is important prior to designing of a specific antibody for an antigen-detection based diagnostic test, the structural elucidation of this protein is essential. In this study, we constructed a three dimensional model of the 12 kDa ES protein. The built structure possesses a thioredoxin backbone which consists of four α-helices flanking five β-strands at the center. Three potential epitopes (6-8 residues) which can be combined into one "single" epitope have been identified from the built structure as the most potential antibody binding site.

CONCLUSION

Together with specific antibody design, this work could contribute towards future development of an antigen detection test for toxoplasmosis.

Correlating protein hot spot surface analysis using ProBiS with simulated free energies of protein-protein interfacial residues

A protocol was developed for the computational determination of the contribution of interfacial amino acid residues to the free energy of protein-protein binding. Thermodynamic integration, based on molecular dynamics simulation in CHARMM, was used to determine the free energy associated with single point mutations to glycine in a protein-protein interface. The hot spot amino acids found in this way were then correlated to structural similarity scores detected by the ProBiS algorithm for local structural alignment. We find that amino acids with high structural similarity scores contribute on average -3.19 kcal/mol to the free energy of protein-protein binding and are thus correlated with hot spot residues, while residues with low similarity scores contribute on average only -0.43 kcal/mol. This suggests that the local structural alignment method provides a good approximation of the contribution of a residue to the free energy of binding and is particularly useful for detection of hot spots in proteins with known structures but undetermined protein-protein complexes.

Parallel-ProBiS: fast parallel algorithm for local structural comparison of protein structures and binding sites

The ProBiS algorithm performs a local structural comparison of the query protein surface against the nonredundant database of protein structures. It finds proteins that have binding sites in common with the query protein. Here, we present a new parallelized algorithm, Parallel-ProBiS, for detecting similar binding sites on clusters of computers. The obtained speedups of the parallel ProBiS scale almost ideally with the number of computing cores up to about 64 computing cores. Scaling is better for larger than for smaller query proteins. For a protein with almost 600 amino acids, the maximum speedup of 180 was achieved on two interconnected clusters with 248 computing cores. Source code of Parallel-ProBiS is available for download free for academic users at http://probis.cmm.ki.si/download.

DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment

MOTIVATION

Many drug discovery projects fail because the underlying target is finally found to be undruggable. Progress in structure elucidation of proteins now opens up a route to automatic structure-based target assessment. DoGSiteScorer is a newly developed automatic tool combining pocket prediction, characterization and druggability estimation and is now available through a web server.

AVAILABILITY

The DoGSiteScorer web server is freely available for academic use at http://dogsite.zbh.uni-hamburg.de

CONTACT

rarey@zbh.uni-hamburg.de.

PocketAnnotate: towards site-based function annotation

A computational pipeline PocketAnnotate for functional annotation of proteins at the level of binding sites has been proposed in this study. The pipeline integrates three in-house algorithms for site-based function annotation: PocketDepth, for prediction of binding sites in protein structures; PocketMatch, for rapid comparison of binding sites and PocketAlign, to obtain detailed alignment between pair of binding sites. A novel scheme has been developed to rapidly generate a database of non-redundant binding sites. For a given input protein structure, putative ligand-binding sites are identified, matched in real time against the database and the query substructure aligned with the promising hits, to obtain a set of possible ligands that the given protein could bind to. The input can be either whole protein structures or merely the substructures corresponding to possible binding sites. Structure-based function annotation at the level of binding sites thus achieved could prove very useful for cases where no obvious functional inference can be obtained based purely on sequence or fold-level analyses. An attempt has also been made to analyse proteins of no known function from Protein Data Bank. PocketAnnotate would be a valuable tool for the scientific community and contribute towards structure-based functional inference. The web server can be freely accessed at http://proline.biochem.iisc.ernet.in/pocketannotate/.