Automated protein structure homology modeling: a progress report

Potential molecular mechanism of reuterin on the inhibition of Aspergillus flavus conidial germination: An in silico study

Reuterin is a natural antifungal agent derived from certain strains of Limosilactobacillus reuteri. Our previous study revealed that 6 mM reuterin inhibited completely the conidial germination of aflatoxigenic Aspergillus flavus. This study investigated the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination, which was pre-assumed that it correlated to the inhibition of some essential enzyme activity involved in conidial germination, specifically 1,3-β-glucan synthase, chitin synthase, and catalases (catalase, bifunctional catalase-peroxidase, and spore-specific catalase). The complex of 1,3-β-glucan synthase and chitin synthase with reuterin had a lower binding affinity than that with the substrate. Conversely, the complex of catalases with reuterin had a higher binding affinity than that with the substrate. It was suggested that 1,3-β-glucan synthase and chitin synthase tended to bind the substrate rather than bind reuterin. In contrast, catalases tended to bind reuterin rather than bind the substrate. Therefore, reuterin could be a potential inhibitor of catalases but may not be an inhibitor of 1,3-β-glucan synthase and chitin synthase. In this in silico study, we predicted that the potential molecular mechanism of reuterin in inhibiting A. flavus conidial germination was due to the inhibition of catalases activities by competitively binding to the enzymes active sites, thus resulting in the accumulation of reactive oxygen species in cells, leading to cells damage. PRACTICAL APPLICATION: This in silico study revealed that reuterin is a potential inhibitor of catalases in A. flavus, thereby interfering with the antioxidant system during conidial germination. This finding shows that reuterin can be used as an antifungal agent in food or agricultural products, inhibiting conidial germination completely.

Reconnoitering the sequence and structural analysis of Staphylococcus aureus "A" protein

Background

The Staphylococcus aureus "A" protein plays an essential role in the pathogenicity and virulence of this bacterial species. To gain deeper insights into the protein's characteristics, we conducted an in-depth analysis of its sequence and structure.

Objective

This study aimed to unravel the underlying genetic and structural components that contribute to the protein's functional properties.

Results

Utilizing various bioinformatics tools and techniques, we first examined the protein's primary sequence, identifying key amino acid residues and potential functional domains. Additionally, we employed computational modeling and simulation approaches to determine the tertiary structure of the "A" protein. Through this comprehensive analysis, we discovered novel features and interactions within the protein's structure, shedding light on its potential mechanisms of action. Furthermore, we investigated the protein's evolutionary conservation and compared it with related proteins from other bacterial species.

Conclusions

Overall, our findings provide valuable insights into the sequence and structure of the Staphylococcus aureus "A" protein, which may have implications for understanding its role in pathogenicity and guiding the development of novel therapeutic strategies.

In silico design of epitope-based peptide vaccine against non-typhoidal Salmonella through immunoinformatic approaches

Non-typhoidal Salmonella (NTS) is one of the leading bacterial causes of many invasive human infections with a high antibiotic resistance profile. With this concern, the current study aimed to design an effective epitope-based peptide vaccine against NTS species as a successive and substitutive protective measure of invasive NTS disease. To design rationally, the current study considered a comprehensive in silico workflow combination of both immunoinformatics and molecular modeling approaches, including molecular docking and molecular dynamics (MD) simulation. We identified the two most promising T cell epitopes KVLYGIFAI and YGIFAITAL, and three B cell epitopes AAPVQVGEAAGS, TGGGDGSNT, and TGGGDGSNTGTTTT, in the outer membrane of NTS. Using these epitopes, a multiepitope vaccine was subsequently constructed along with appropriate adjuvant and linkers, which showed a good binding affinity and stability with toll-like receptor 2 (TLR2) in both molecular docking and MD simulation. Furthermore, in silico immune simulation described a strong immune response with a high number of antibodies, interferon-γ, and activated B and T cells. This study collectively suggests that predicted vaccine constructs could be considered potential vaccine candidates against common NTS species.Communicated by Ramaswamy H. Sarma.

Phylogenetic analyses, protein modeling and active site prediction of two pathogenesis related (PR2 and PR3) genes from bread wheat

Wheat is a major staple food and has been extensively grown around the globe. Sessile nature of plants has exposed them to a lot of biotic and abiotic stresses including fungal pathogen attack. Puccinia graminis f.sp. tritici causes stem rust in the wheat crop and leads to 70% decrease in its production. Pathogenesis-related (PR) proteins provide plants with defense against different fungal pathogens as these proteins have antifungal activities. This study was designed to screen Pakistani wheat varieties for PR2 and PR3 proteins and their in silico characterization. PR2 and PR3 genes were screened and isolated by PCR amplification from wheat variety Chenab-70 and Frontana, respectively. The nucleotide sequences of PR2 and PR3 genes were deposited in GenBank with accession numbers MT303867 and MZ766118, respectively. Physicochemical properties, secondary and tertiary structure predictions, and molecular docking of protein sequences of PR2 and PR3 were performed using different bioinformatics tools and software. PR2 and PR3 genes were identified to encode β-1,3-glucanase and chitinase proteins, respectively. Molecular docking of both PR2 and PR3 proteins with beta-glucan and chitin (i.e. their respective ligands) showed crucial amino acid residues involved in molecular interactions. Conclusively, molecular docking analysis of β-1,3-glucanase and chitinase proteins revealed crucial amino acid residues which are involved in ligand binding and important interactions which might have important role in plant defense against fungal pathogens. Moreover, the active residues in the active sties of these proteins can be identified through mutational studies and resulting information might help understanding how these proteins are involved in plant defense mechanisms.

A computer-based approach for developing linamarase inhibitory agents

Cassava is a strategic crop, especially for developing countries. However, the presence of cyanogenic compounds in cassava products limits the proper nutrients utilization. Due to the poor availability of structure discovery and elucidation in the Protein Data Bank is limiting the full understanding of the enzyme, how to inhibit it and applications in different fields. There is a need to solve the three-dimensional structure (3-D) of linamarase from cassava. The structural elucidation will allow the development of a competitive inhibitor and various industrial applications of the enzyme. The goal of this review is to summarize and present the available 3-D modeling structure of linamarase enzyme using different computational strategies. This approach could help in determining the structure of linamarase and later guide the structure elucidationin silicoand experimentally.

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy

BACKGROUND

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales.

OBJECTIVE

Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration.

METHODS

Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs.

RESULTS

This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed.

CONCLUSION

It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Protein-protein interaction (PPI) networks are known to be valuable targets for therapeutic intervention; yet the development of PPI modulators as next-generation drugs to target specific vertices, edges, and hubs has been impeded by the lack of structural information of many of the proteins and complexes involved. Building on recent advancements in cross-linking mass spectrometry (XL-MS), we describe an effective approach to obtain relevant structural data on R7BP, a master regulator of itch sensation, and its interfaces with other proteins in its network. This approach integrates XL-MS with a variety of modeling techniques to successfully develop antibody inhibitors of the R7BP and RGS7/Gβ5 duplex interaction. Binding and inhibitory efficiency are studied by surface plasmon resonance spectroscopy and through an R7BP-derived dominant negative construct. This approach may have broader applications as a tool to facilitate the development of PPI modulators in the absence of crystal structures or when structural information is limited.

Pharmacoinformatics and molecular docking reveal potential drug candidates against Schizophrenia to target TAAR6

Schizophrenia (SZ) is a complex disabling disorder that leads to the mental disability and afflicts 1% of the world's total population and placed in top ten medical disorders. In current work, bioinformatics analyses were carried out on Trace amine (TA)-associated receptor 6 (TAAR6) to recognize the potential drugs and compounds against SZ. Comparative modeling and threading-based approaches were utilized for the structure prediction of TAAR6. Fifty-nine predicted structures were evaluated by various model assessment techniques and final model having only eight amino acids in the outlier region and 98.5% overall quality factor was chosen for further pharmacoinformatics and molecular docking analyses. From an extensive literature review, 11 Food and Drug Administration (FDA) approved drugs were analyzed by computational techniques and Aripiprazole was found as the most effective drug against SZ by targeting TAAR6. Here, we report five novel molecules which exhibited the highest binding affinity, effective drug properties, and interestingly, observed better results than the approved selected drugs against SZ by targeting TAAR6. The docking analyses revealed that Arg-92, Trp-98, Gln-191, Thr-192, Ala-290, Cys-291, Tyr-293, and Glu-294 residues were observed as critical interacting residues in receptor-ligand interactions. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, Lipinski rule of five, highest binding affinity coupled with virtual screening (VS), and pharmacophore modeling approach illustrated that aripiprazole (-8.6 kcal/mol) and TAAR6_0094 (-9.3 kcal/mol) are potential inhibitors for targeting TAAR6. It is suggested that schizophrenic patients have to use Aripiprazole for the medication of SZ by targeting TAAR6 and develop effective therapies by utilizing scrutinized novel compound.

Homology modeling in a dynamical world

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

Synthesis and evaluation of novel thiazole-based derivatives as selective inhibitors of DNA-binding domain of the androgen receptor

A series of thiazole-based inhibitors selectively targeting DNA-binding domain of the androgen receptor (AR) were synthesized and evaluated, and the SAR data were summarized. We identified a novel compound SKLB-C2807 that effectively inhibited the human prostate cancer cell line LNCaP/AR with the IC₅₀ value of 0.38 μm without significant antiproliferative effects on other cell lines PC-3 (AR-negative), SW620, MCF-7 (ER-positive), and L-O2 (non-cancerous). This compound also considerably decreased the expression of prostate-specific antigen. Its binding mode to the AR-DBD was studied. These efforts lay the foundation for developing the next generation of anti-androgens.

The 3-D Structural Basis for the Pgi Genotypic Differences in the Performance of the Butterfly Melitaea cinxia at Different Temperatures

Although genotype-by-environment interaction has long been used to unveil the genetic variation that affects Darwinian fitness, the mechanisms underlying the interaction usually remain unknown. Genetic variation at the dimeric glycolytic enzyme phosphoglucoisomerase (Pgi) has been observed to interact with temperature to explain the variation in the individual performance of the butterfly Melitaea cinxia. At relatively high temperature, individuals with Pgi-non-f genotypes generally surpass those with Pgi-f genotypes, while the opposite applies at relatively low temperature. In this study, we did protein structure predictions and BlastP homology searches with the aim to understand the structural basis for this temperature-dependent difference in the performance of M. cinxia. Our results show that, at amino acid (AA) site 372, one of the two sites that distinguish Pgi-f (the translated polypeptide of the Pgi-f allele) from Pgi-non-f (the translated polypeptide of the Pgi-non-f allele), the Pgi-non-f-related residue strengthens an electrostatic attraction between a pair of residues (Glu373-Lys472) that are from different monomers, compared to the Pgi-f-related residue. Further, BlastP searches of animal protein sequences reveal a dramatic excess of electrostatically attractive combinations of the residues at the Pgi AA sites equivalent to sites 373 and 472 in M. cinxia. This suggests that factors enhancing the inter-monomer interaction between these two sites, and therefore helping the tight association of two Pgi monomers, are favourable. Our homology-modelling results also show that, at the second AA site that distinguishes Pgi-f from Pgi-non-f in M. cinxia, the Pgi-non-f-related residue is more entropy-favourable (leading to higher structural stability) than the Pgi-f-related residue. To sum up, this study suggests a higher structural stability of the protein products of the Pgi-non-f genotypes than those of the Pgi-f genotypes, which may explain why individuals carrying Pgi-non-f genotypes outperform those carrying Pgi-f genotypes at stressful high temerature.

ILP-2 modeling and virtual screening of an FDA-approved library:a possible anticancer therapy

BACKGROUND/AIM

The members of the inhibitors of apoptosis protein (IAP) family inhibit diverse components of the caspase signaling pathway, notably caspase 3, 7, and 9. ILP-2 (BIRC-8) is the most recently identified member of the IAPs, mainly interacting with caspase 9. This interaction would eventually lead to death resistance in the case of cancerous cells. Therefore, structural modeling of ILP-2 and finding applicable inhibitors of its interaction with caspase 9 are a compelling challenge.

MATERIALS AND METHODS

Three main protein modeling approaches along with various model refinement measures were harnessed to achieve a reliable 3D model, using state-of-the-art software. Thereafter, the selected model was employed to perform virtual screening of an FDA approved library.

RESULTS

A model built by a combinatorial approach (homology and ab initio approaches) was chosen as the best model. Model refinement processes successfully bolstered the model quality. Virtual screening of the compound library introduced several high affinity inhibitor candidates that interact with functional residues of ILP2.

CONCLUSION

Given the 3D structure of the ILP2 molecule, we found promising inhibitory molecules. In addition to high affinity towards the ILP2 molecule, these molecules interact with residues that play pivotal rules in ILP2-caspase interaction. These molecules would inhibit ILP2-caspase interaction and consequently would lead to reactivated cell apoptosis through the caspases pathway.

Modelling and Characterization of Glial Fibrillary Acidic Protein

Glial Fibrillary Acidic Protein (GFAP) is an intermediate-filament (IF) protein that maintains the astrocytes of the Central Nervous System in Human. This is differentially expressed during serological studies in inflamed condition such as Rheumatoid Arthritis (RA). Therefore, it is of interest to glean molecular insight using a model of GFAP (49.88 kDa) due to its crystallographic nonavailability. The present study has been taken into consideration to construct computational protein model using Modeller 9.11. The structural relevance of the protein was verified using Gromacs 4.5 followed by validation through PROCHECK, Verify 3D, WHAT-IF, ERRAT and PROVE for reliability. The constructed three dimensional (3D) model of GFAP protein had been scrutinized to reveal the associated functions by identifying ligand binding sites and active sites. Molecular level interaction study revealed five possible surface cavities as active sites. The model finds application in further computational analysis towards drug discovery in order to minimize the effect of inflammation.

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

BACKGROUND

The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals.

RESULTS

Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution.

CONCLUSION

Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.

MgtE is a dual-function protein in Pseudomonas aeruginosa

The opportunistic pathogen Pseudomonas aeruginosa causes a wide range of infections, including chronic biofilm infections in the lungs of individuals with cystic fibrosis. We previously found that the inner-membrane protein MgtE can function both as a magnesium transporter and a virulence modulator, although the exact mechanism governing these activities is unclear. To address this issue, we carried out an experimental characterization of P. aeruginosa MgtE and generated a computer-rendered model. Our in silico analysis demonstrated the structural similarity of P. aeruginosa MgtE to that of the crystal structure of MgtE in Thermus thermophilus. Experimentally, we verified that MgtE is not essential for growth and found that it may not be involved directly in biofilm formation, even under low-magnesium conditions. We demonstrated both magnesium transport and cytotoxicity-regulating functions, and showed that magnesium-binding sites in the connecting helix region of MgtE are vital in coupling these two functions. Furthermore, limiting magnesium environments stimulated mgtE transcriptional responses. Our results suggested that MgtE might play an important role in linking magnesium availability to P. aeruginosa pathogenesis.

A homology/ab initio hybrid algorithm for sampling near-native protein conformations

One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).

TiPs: a database of therapeutic targets in pathogens and associated tools

MOTIVATION

The need for new drugs and new targets is particularly compelling in an era that is witnessing an alarming increase of drug resistance in human pathogens. The identification of new targets of known drugs is a promising approach, which has proven successful in several cases. Here, we describe a database that includes information on 5153 putative drug-target pairs for 150 human pathogens derived from available drug-target crystallographic complexes.

AVAILABILITY AND IMPLEMENTATION

The TiPs database is freely available at http://biocomputing.it/tips.

CONTACT

anna.tramontano@uniroma1.it or allegra.via@uniroma1.it.

Insight into the 3D structure of ADP-glucose pyrophosphorylase from rice (Oryza sativa L.)

ADP-glucose pyrophosphorylase (E.C. 2.7.7.27; AGPase) is a key regulatory enzyme that catalyzes the rate-limiting step of starch biosynthesis in higher plants. AGPase consists of pair of small (SS) and large (LS) subunits thereby constituting a heterotetrameric structure. No crystal structure of the native heterotetrameric enzyme is available for any species, thus limiting the complete understanding of structure-function relationships of this enzyme. In this study, an attempt was made to deduce the heterotetrameric assembly of AGPase in rice. Homology modeling of the three-dimensional structure of the LS and SS was performed using the Swiss Model Server, and the models were evaluated and docked using GRAMM-X to obtain the stable heterodimer orientation (LS as receptor and SS as ligand) and then the heterotetrameric orientation. The initial heterotetrameric orientation was further refined using the RosettaDock Server. MD simulation of the representative heterodimer/tetramer was performed using NAMD, which indicated that the tail-to-tail interaction of LS and SS was more stable than the head-to-head orientation, and the heterotetramer energy was also minimized to -767,011 kcal mol(-1). Subunit-subunit interaction studies were then carried out using the programs NACCESS and Dimplot. A total of 57 interface residues were listed in SS and 63 in LS. The residues plotted by Dimplot were similar to those listed by NACCESS. Multiple sequence alignment of the sequences of LS and SS from potato, maize and rice validated the interactions inferred in the study. RMSD of 1.093 Å was obtained on superimposition of the deduced heterotetramer on the template homo-tetramer (1YP2), showing the similarity between the two structures.

Bioinformatics and variability in drug response: a protein structural perspective

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

A library of protein surface patches discriminates between native structures and decoys generated by structure prediction servers

Background

Protein surfaces serve as an interface with the molecular environment and are thus tightly bound to protein function. On the surface, geometric and chemical complementarity to other molecules provides interaction specificity for ligand binding, docking of bio-macromolecules, and enzymatic catalysis.

As of today, there is no accepted general scheme to represent protein surfaces. Furthermore, most of the research on protein surface focuses on regions of specific interest such as interaction, ligand binding, and docking sites. We present a first step toward a general purpose representation of protein surfaces: a novel surface patch library that represents most surface patches (~98%) in a data set regardless of their functional roles.

Results

Surface patches, in this work, are small fractions of the protein surface. Using a measure of inter-patch distance, we clustered patches extracted from a data set of high quality, non-redundant, proteins. The surface patch library is the collection of all the cluster centroids; thus, each of the data set patches is close to one of the elements in the library.

We demonstrate the biological significance of our method through the ability of the library to capture surface characteristics of native protein structures as opposed to those of decoy sets generated by state-of-the-art protein structure prediction methods. The patches of the decoys are significantly less compatible with the library than their corresponding native structures, allowing us to reliably distinguish native models from models generated by servers. This trend, however, does not extend to the decoys themselves, as their similarity to the native structures does not correlate with compatibility with the library.

Conclusions

We expect that this high-quality, generic surface patch library will add a new perspective to the description of protein structures and improve our ability to predict them. In particular, we expect that it will help improve the prediction of surface features that are apparently neglected by current techniques.

The surface patch libraries are publicly available at http://www.cs.bgu.ac.il/~keasar/patchLibrary.

Structure-based discovery of antibacterial drugs

The modern era of antibacterial chemotherapy began in the 1930s, and the next four decades saw the discovery of almost all the major classes of antibacterial agents that are currently in use. However, bacterial resistance to many of these drugs is becoming an increasing problem. As such, the discovery of drugs with novel modes of action will be vital to meet the threats created by the emergence of resistance. Success in discovering inhibitors using high-throughput screening of chemical libraries is rare. In this Review we explore the exciting opportunities for antibacterial-drug discovery arising from structure-based drug design.

Homology-modelling protein-ligand interactions: allowing for ligand-induced conformational change

Current homology-modelling methods do not consider small molecules in their automated processes. Therefore, the development of a reliable tool for protein-ligand homology modelling is an important next step in generating plausible models for molecular interactions. Two automated protein-ligand homology-modelling strategies, requiring no expert knowledge from the user, are investigated here. Both employ the "induced fit" concept with flexibility in side chains and ligand. The most successful strategy superimposes the new ligand over the original ligand before homology modelling, allowing the new ligand to be taken into consideration during protein modelling (rather than after), facilitating conformational change in the local backbone if necessary. We show that this approach results in successful modelling of the ligand and key binding-site residues of angiotensin-converting enzyme 2 (ACE2) from its homologue ACE, which is not possible via conventional homology modelling or by homology modelling followed by docking. Several other difficult target complexes are also successfully modelled, reproducing native protein-ligand contacts with significantly different biological substrates and different binding-site conformations. These include the modelling of Cdk5 (cyclin-dependent kinase 5) from Cdk2, thymidine phosphorylase from a bacterial homologue, and dihydrofolate reductase from a recombinant variant with a markedly different inhibitor. In terms of average modelling quality across 82 targets, the ligand RMSD with respect to the experimental structure is 1.4 A (and 2.0 A for the protein binding site) for "easy" cases and 2.9 A for the ligand (and 2.7 A for the protein binding site) in "hard" cases. This demonstrates the importance of selecting an optimal template. Ligand-modelling accuracy is strongly dependent on target-template ligand structural similarity, rather than target-template sequence identity. However, protein-modelling accuracy is dependent on both. Our automated protein-ligand homology-modelling strategy generates a higher degree of accuracy than homology modelling followed by docking, generating an average ligand RMSD that is 1-2 A better than docking with homology models.

A leucine-rich repeat assembly approach for homology modeling of the human TLR5-10 and mouse TLR11-13 ectodomains

So far, 13 groups of mammalian Toll-like receptors (TLRs) have been identified. Most TLRs have been shown to recognize pathogen-associated molecular patterns from a wide range of invading agents and initiate both innate and adaptive immune responses. The TLR ectodomains are composed of varying numbers and types of leucine-rich repeats (LRRs). As the crystal structures are currently missing for most TLR ligand-binding ectodomains, homology modeling enables first predictions of their three-dimensional structures on the basis of the determined crystal structures of TLR ectodomains. However, the quality of the predicted models that are generated from full-length templates can be limited due to low sequence identity between the target and templates. To obtain better templates for modeling, we have developed an LRR template assembly approach. Individual LRR templates that are locally optimal for the target sequence are assembled into multiple templates. This method was validated through the comparison of a predicted model with the crystal structure of mouse TLR3. With this method, we also constructed ectodomain models of human TLR5, TLR6, TLR7, TLR8, TLR9, and TLR10 and mouse TLR11, TLR12, and TLR13 that can be used as first passes for a computational simulation of ligand docking or to design mutation experiments. This template assembly approach can be extended to other repetitive proteins.

Basic protein structure prediction for the biologist: A review

As the field of protein structure prediction continues to expand at an exponential rate, the bench-biologist might feel overwhelmed by the sheer range of available applications. This review presents the three main approaches in computational structure prediction from a non-bioinformatician?s point of view and makes a selection of tools and servers freely available. These tools are evaluated from several aspects, such as number of citations, ease of usage and quality of the results. Finally, the applications of models generated by computational structure prediction are discussed.

Finding of residues crucial for supersecondary structure formation

This work evaluates the hypothesis that proteins with an identical supersecondary structure (SSS) share a unique set of residues--SSS-determining residues--even though they may belong to different protein families and have very low sequence similarities. This hypothesis was tested on two groups of sandwich-like proteins (SPs). Proteins in each group have an identical SSS, but their sequence similarity is below the "twilight zone." To find the SSS-determining residues specific to each group, a unique structure-based algorithm of multiple sequences alignment was developed. The units of alignment are individual strands and loops rather than whole sequences. The algorithm is based on the alignment of residues that form hydrogen bonds between corresponding strands. Structure-based alignment revealed that 30-35% of the positions in the sequences in each group of proteins are "conserved positions" occupied either by hydrophobic-only or hydrophilic-only residues. Moreover, each group of SPs is characterized by a unique set of SSS-determining residues found at the conserved positions. The set of SSS-determining residues has very high sensitivity and specificity for identifying proteins with a corresponding SSS: It is an "amino acid tag" that brands a sequence as having a particular SSS. Thus, the sets of SSS-determining residues can be used to classify proteins and to predict the SSS of a query amino acid sequence.

Homology modeling of human Toll-like receptors TLR7, 8, and 9 ligand-binding domains

Toll-like receptors (TLRs) play a key role in the innate immune system. The TLR7, 8, and 9 compose a family of intracellularly localized TLRs that signal in response to pathogen-derived nucleic acids. So far, there are no crystallographic structures for TLR7, 8, and 9. For this reason, their ligand-binding mechanisms are poorly understood. To enable first predictions of the receptor-ligand interaction sites, we developed three-dimensional structures for the leucine-rich repeat ectodomains of human TLR7, 8, and 9 based on homology modeling. To achieve a high sequence similarity between targets and templates, structural segments from all known TLR ectodomain structures (human TLR1/2/3/4 and mouse TLR3/4) were used as candidate templates for the modeling. The resulting models support previously reported essential ligand-binding residues. They also provide a basis to identify three potential receptor dimerization mechanisms. Additionally, potential ligand-binding residues are identified using combined procedures. We suggest further investigations of these residues through mutation experiments. Our modeling approach can be extended to other members of the TLR family or other repetitive proteins.

Structural relatedness via flow networks in protein sequence space

A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.

In silico mutagenesis: a case study of the melanocortin 4 receptor

The melanocortin 4 receptor (MC4R) is a G-protein-coupled receptor (GPCR) and a key molecule in the regulation of energy homeostasis. At least 159 substitutions in the coding region of human MC4R (hMC4R) have been described experimentally; over 80 of those occur naturally, and many have been implicated in obesity. However, assessment of the presumably functionally essential residues remains incomplete. Here we have performed a complete in silico mutagenesis analysis to assess the functional essentiality of all possible nonnative point mutants in the entire hMC4R protein (332 residues). We applied SNAP, which is a method for quantifying functional consequences of single amino acid (AA) substitutions, to calculate the effects of all possible substitutions at each position in the hMC4R AA sequence. We compiled a mutability score that reflects the degree to which a particular residue is likely to be functionally important. We performed the same experiment for a paralogue human melanocortin receptor (hMC1R) and a mouse orthologue (mMC4R) in order to compare computational evaluations of highly related sequences. Three results are most salient: 1) our predictions largely agree with the available experimental annotations; 2) this analysis identified several AAs that are likely to be functionally critical, but have not yet been studied experimentally; and 3) the differential analysis of the receptors implicates a number of residues as specifically important to MC4Rs vs. other GPCRs, such as hMC1R.—Bromberg, Y., Overton, J., Vaisse, C., Leibel, R. L., Rost, B. In silico mutagenesis: a case study of the melanocortin 4 receptor.

Protein structure homology modeling using SWISS-MODEL workspace

Homology modeling aims to build three-dimensional protein structure models using experimentally determined structures of related family members as templates. SWISS-MODEL workspace is an integrated Web-based modeling expert system. For a given target protein, a library of experimental protein structures is searched to identify suitable templates. On the basis of a sequence alignment between the target protein and the template structure, a three-dimensional model for the target protein is generated. Model quality assessment tools are used to estimate the reliability of the resulting models. Homology modeling is currently the most accurate computational method to generate reliable structural models and is routinely used in many biological applications. Typically, the computational effort for a modeling project is less than 2 h. However, this does not include the time required for visualization and interpretation of the model, which may vary depending on personal experience working with protein structures.

Substrate specificity of plant UDP-dependent glycosyltransferases predicted from crystal structures and homology modeling

Plant family 1 UDP-dependent glycosyltransferases (UGTs) catalyze the glycosylation of a plethora of bioactive natural products. In Arabidopsis thaliana, 120 UGT encoding genes have been identified. The crystal-based 3D structures of four plant UGTs have recently been published. Despite low sequence conservation, the UGTs show a highly conserved secondary and tertiary structure. The sugar acceptor and sugar donor substrates of UGTs are accommodated in the cleft formed between the N- and C-terminal domains. Several regions of the primary sequence contribute to the formation of the substrate binding pocket including structurally conserved domains as well as loop regions differing both with respect to their amino acid sequence and sequence length. In this review we provide a detailed analysis of the available plant UGT crystal structures to reveal structural features determining substrate specificity. The high 3D structural conservation of the plant UGTs render homology modeling an attractive tool for structure elucidation. The accuracy and utility of UGT structures obtained by homology modeling are discussed and quantitative assessments of model quality are performed by modeling of a plant UGT for which the 3D crystal structure is known. We conclude that homology modeling offers a high degree of accuracy. Shortcomings in homology modeling are also apparent with modeling of loop regions remaining as a particularly difficult task.

Protein structure prediction and model quality assessment

Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment.

Molecular modeling of A1 and A2A adenosine receptors: comparison of rhodopsin- and beta2-adrenergic-based homology models through the docking studies

Adenosine receptors (ARs) are members of the superfamily of G protein-coupled receptors. The homology models of adenosine A1 and A2A receptors were constructed. The high-resolution X-ray structure of bovine rhodopsin and crystal structure of beta2-adrenergic receptor were used as templates. The binding sites of the A1 and A2A ARs were constructed by using data obtained from mutagenesis experiments as well as docking simulations of the respective AR antagonsists DPCPX and XAC. To compare rhodopsin- and beta2-adrenergic-based models, the binding mode of A1 (KW-3902, LUF-5437) and A2A (KW-6002, ZM-241385) ARs antagonists were also examined. The differences in the binding ability of both models were noted during the study. The beta2-adrenergic-based A2A AR model was much more capable to stabilize the ligand in the binding site cavity than the corresponding rhodopsin-based A2A AR model, however, such differences were not so clear in case of A1 AR models. It was suggested that for the A1 AR it is possible to use the crystal structure of rhodopsin as a template as well as beta2-adrenergic receptor, but for A2A AR, with the now available beta2-adrenergic receptor X-ray structure, docking studies should be avoided on the rhodopsin-based model. However, taking into account that the beta2AR shares about 31% of the residues with the AR in comparison to 21% in case of bRho, we suggest using beta2-adrenergic-based models for the A1 and A2A ARs for further in silico ligand screening also because of their generally better ability to stabilize ligands inside the binding pocket.

A common "hot spot" confers hERG blockade activity to alpha-scorpion toxins affecting K+ channels

While alpha-KTx peptides are generally known for their modulation of the Shaker-type and the Ca(2+)-activated potassium channels, gamma-KTxs are associated with hERG channels modulation. An exception to the rule is BmTx3 which belongs to subfamily alpha-KTx15 and can block hERG channels. To explain the peculiar behavior of BmTx3, a tentative "hot spot" formed of 2 basic residues (R18 and K19) was suggested but never further studied [Huys I, et al. BmTx3, a scorpion toxin with two putative functional faces separately active on A-type K(+) and HERG currents. Biochem J 2004;378:745-52]. In this work, we investigated if the "hot spot" is a commonality in subfamily alpha-KTx15 by testing the effect of (AmmTx3, Aa1, discrepin). Furthermore, single mutations altering the "hot spot" in discrepin, have introduced for the very first time a hERG blocking activity to a previously non-active alpha-KTx. Additionally, we could extend our results to other alpha-KTx subfamily members belonging to alpha-KTx1, 4 and 6, therefore, the "hot spot" represents a common pharmacophore serving as a predictive tool for yet to be discovered alpha-KTxs.

A CD4 homologue in sea bass (Dicentrarchus labrax): molecular characterization and structural analysis

CD4 is a transmembrane glycoprotein fundamental for cell-mediated immunity. Its action as a T cell co-receptor increases the avidity of association between a T cell and an antigen-presenting cell by interacting with portions of the complex between MHC class II and TR molecules. In this paper we report the cDNA cloning, expression and structural analysis of a CD4 homologue from sea bass (Dicentrarchus labrax). The sea bass CD4 cDNA consists of 2071 bp that translates in one reading frame to give the entire molecule containing 480 amino acids. The analysis of the sequence shows the presence of four putative Ig-like domains and that some fundamental structural features, like a disulphide bond in domain D2 and the CXC signalling motif in the cytoplasmic tail, are conserved from sea bass to mammals. Real-time PCR analysis showed that very high levels of CD4 mRNA transcripts are present in thymus, followed by gut and gills. In vitro stimulation of head kidney leukocytes with LPS and PHA-L gave an increase of CD4 mRNA levels after 4h and a decrease after 24h. Homology modelling has been applied to create a 3D model of sea bass CD4 and to investigate its interaction with sea bass MHC-II. The analysis of the 3D complex between sea bass CD4 and sea bass MHC-II suggests that the absence of a disulfide bond in the CD4 D1 domain could make this molecule more flexible, inducing a different conformation and affecting the binding and the way of interaction between CD4 and MHC-II. Our results will add new insights into the sea bass T cell immune responses and will help in the identification of T cell subsets in teleost fishes to better understand the evolution of cell-mediated immunity from fish to mammals.

Computational methods in protein structure prediction

This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.

An evaluation of automated homology modelling methods at low target template sequence similarity

MOTIVATION

There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities </=50%.

RESULTS

When using Modeller as the common modelling program, 3D-Coffee outperforms Staccato and SAlign using both multiple templates and the best single template, and across the sequence identity range 20-50%. The mean model RMSD generated from 3D-Coffee using multiple templates is 15 and 28% (or using single templates, 3 and 13%) better than those generated by Staccato and Salign, respectively. 3D-Coffee gives equivalent modelling accuracy from multiple and single templates, but Staccato and SAlign are more successful with single templates, their quality deteriorating as additional lower sequence identity templates are added. Evaluating the different homology modelling programs, on average Modeller performs marginally better in overall modelling than the others tested. However, on average Nest produces the best loops with an 8% improvement by mean RMSD compared to the loops generated by Builder.