Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome

High throughput virtual screening (HTVS) of peptide library: Technological advancement in ligand discovery

High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.

An unusually high substitution rate in transplant-associated BK polyomavirus in vivo is further concentrated in HLA-C-bound viral peptides

Infection with human BK polyomavirus, a small double-stranded DNA virus, potentially results in severe complications in immunocompromised patients. Here, we describe the in vivo variability and evolution of the BK polyomavirus by deep sequencing. Our data reveal the highest genomic evolutionary rate described in double-stranded DNA viruses, i.e., 10⁻³–10⁻⁵ substitutions per nucleotide site per year. High mutation rates in viruses allow their escape from immune surveillance and adaptation to new hosts. By combining mutational landscapes across viral genomes with in silico prediction of viral peptides, we demonstrate the presence of significantly more coding substitutions within predicted cognate HLA-C-bound viral peptides than outside. This finding suggests a role for HLA-C in antiviral immunity, perhaps through the action of killer cell immunoglobulin-like receptors. The present study provides a comprehensive view of viral evolution and immune escape in a DNA virus.

Little is known about the mechanisms of evolution and viral immune escape in double-stranded DNA (dsDNA) viruses. Here, we study the evolution of BK polyomavirus and observe the highest genomic evolutionary rate described so far for a dsDNA virus, in the range of RNA viruses, which usually evolve rapidly. Furthermore, the prediction of viral peptides to determine immune escape suggests a specific role of HLA-C in antiviral immunity. These findings are helpful for future advances in antiviral therapies and provide a step forward in our understanding of in vivo viral evolution in humans.

Computational design of peptide ligands to target the intermolecular interaction between viral envelope protein and pediatric receptor

The recognition and binding of viral envelope protein to pediatric receptor subverts the membrane-trafficking apparatus to mediate virion export in young children. Here, we described a successful computational design of peptide ligands to target the intermolecular interaction between the virus large envelope protein (LHB) and adaptin receptor (ADT). Based on the crystal structure of ADT in complex with an oligopeptide segment corresponding to the core binding site of LHB, a sequence-specific amino acid preference profile was determined systematically for the ADT-binding peptides using structural bioinformatics approach. With the information harvested from the profile, a genetic evolution procedure was run to improve the biological potency of a peptide population generated randomly from the LHB. A number of potential hits were obtained from the evolution, and four were measured to interact with ADT at micromolar level. A high-affinity hit peptide was then optimized according to computational structural analysis. It is revealed that a potent peptide can be divided into three regions, i.e. a negatively charged region at N-terminus, a hydrophobic core region in middle, and a small, polar region at C-terminal tail. In addition, the two termini of peptide are partially out of the active pocket of ADT, thus contributing moderately to the peptide binding.

Structure-based rational design of peptide hydroxamic acid inhibitors to target tumor necrosis factor-α converting enzyme as potential therapeutics for hepatitis

The human tumor necrosis factor-α converting enzyme (TACE) has recently been raised as a new and promising therapeutic target of hepatitis and other inflammatory diseases. Here, we reported a successful application of the solved crystal structure of TACE complex with a peptide-like ligand INN for rational design of novel peptide hydroxamic acid inhibitors with high potency and selectivity to target and inhibit TACE. First, the intermolecular interactions between TACE catalytic domain and INN were characterized through an integrated bioinformatics approach, with which the key substructures of INN that dominate ligand binding were identified. Subsequently, the INN molecular structure was simplified to a chemical sketch of peptide hydroxamic acid compound, which can be regarded as a linear tripeptide capped by a N-terminal carboxybenzyl group (chemically protective group) and a C-terminal hydroxamate moiety (coordinated to the Zn(2+) at TACE active site). Based on the sketch, a virtual combinatorial library containing 180 peptide hydroxamic acids was generated, from which seven samples were identified as promising candidates by using a knowledge-based protein-peptide affinity predictor and were then tested in vitro with a standard TACE activity assay protocol. Consequently, three designed peptide hydroxamic acids, i.e. Cbz-Pro-Ile-Gln-hydroxamic acid, Cbz-Leu-Ile-Val-hydroxamic acid and Cbz-Phe-Val-Met-hydroxamic acid, exhibited moderate or high inhibitory activity against TACE, with inhibition constants Ki of 36 ± 5, 510 ± 46 and 320 ± 26 nM, respectively. We also examined the structural basis and non-bonded profile of TACE interaction with a designed peptide hydroxamic acid inhibitor, and found that the inhibitor ligand is tightly buried in the active pocket of TACE, forming a number of hydrogen bonds, hydrophobic forces and van der Waals contacts at the interaction interface, conferring both stability and specificity for TACE-inhibitor complex architecture.

Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies

Two factors contribute to the inefficiency associated with screening pharmaceutical library collections as a means of identifying new drugs: [1] the limited success of virtual screening (VS) methods in identifying new scaffolds; [2] the limited accuracy of computational methods in predicting off-target effects. We recently introduced a 3D shape-based similarity algorithm of the SABRE program, which encodes a consensus molecular shape pattern of a set of active ligands into a 4D fingerprint descriptor. Here, we report a mathematical model for shape similarity comparisons and ligand database filtering using this 4D fingerprint method and benchmarked the scoring function HWK (Hamza–Wei–Korotkov), using the 81 targets of the DEKOIS database. Subsequently, we applied our combined 4D fingerprint and HWK scoring function VS approach in scaffold-hopping and drug repurposing using the National Cancer Institute (NCI) and Food and Drug Administration (FDA) databases, and we identified new inhibitors with different scaffolds of MycP₁ protease from the mycobacterial ESX-1 secretion system. Experimental evaluation of nine compounds from the NCI database and three from the FDA database displayed IC₅₀ values ranging from 70 to 100 μM against MycP₁ and possessed high structural diversity, which provides departure points for further structure–activity relationship (SAR) optimization. In addition, this study demonstrates that the combination of our 4D fingerprint algorithm and the HWK scoring function may provide a means for identifying repurposed drugs for the treatment of infectious diseases and may be used in the drug-target profile strategy.

A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model

In this work, we extend our previous ligand shape-based virtual screening approach by using the scoring function Hamza-Wei-Zhan (HWZ) score and an enhanced molecular shape-density model for the ligands. The performance of the method has been tested against the 40 targets in the Database of Useful Decoys and compared with the performance of our previous HWZ score method. The virtual screening results using the novel ligand shape-based approach demonstrated a favorable improvement (area under the receiver operator characteristics curve AUC = .89 ± .02) and effectiveness (hit rate HR(1%) = 53.0% ± 6.3 and HR(10%) = 71.1% ± 4.9). The comparison of the overall performance of our ligand shape-based method with the highest ligand shape-based virtual screening approach using the data fusion of multi queries showed that our strategy takes into account deeper the chemical information of the set of active ligands. Furthermore, the results indicated that our method are suitable for virtual screening and yields superior prediction accuracy than the other study derived from the data fusion using five queries. Therefore, our novel ligand shape-based screening method constitutes a robust and efficient approach to the 3D similarity screening of small compounds and open the door to a whole new approach to drug design by implementing the method in the structure-based virtual screening.

A rational approach to selective pharmacophore designing: an innovative strategy for specific recognition of Gsk3β

We propose a novel cheminformatics approach that combines structure and ligand-based design to identify target-specific pharmacophores with well-defined exclusion ability. Our strategy includes the prediction of selective interactions, developing structure, and knowledge-based selective pharmacophore models, followed by database screening and molecular docking. This unique strategy was employed in addressing the off-target toxicity of Gsk3β and CDKs. The connections of Gsk3β in eukaryotic cell apoptosis and the extensive potency of Gsk3β inhibitors to block cell death have made it a potential drug-discovery target for many grievous human disorders. Gsk3β is phylogenetically very closely related to the CDKs, such as CDK1 and CDK2, which are suggested to be the off-target proteins of Gsk3β inhibitors. Here, we have employed novel computational approaches in designing the ligand candidates that are potentially inhibitory against Gsk3β, with well-defined the exclusion ability to CDKs. A structure-ligand -based selective pharmacophore was modeled. This model was used to retrieve molecules from the zinc database. The hits retrieved were further screened by molecular docking and protein–ligand interaction fingerprints. Based on these results, four molecules were predicted as selective Gsk3β antagonists. It is anticipated that this unique approach can be extended to investigate any protein–ligand specificity.

Ligand-based virtual screening approach using a new scoring function

In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD; dud.docking.org/jahn/ ), and the virtual screening performance was evaluated in terms of the area under the receiver operator characteristic (ROC) curve (AUC), enrichment factor (EF), and hit rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at the top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.

Immunology in the Clinic Review Series; focus on host responses: T cell responses to herpes simplex viruses

Herpes virus infections are chronic and co-exist with acquired immune responses that generally prevent severe damage to the host, while allowing periodic shedding of virus and maintenance of its transmission in the community. Herpes simplex viruses type 1 and 2 (HSV-1, HSV-2) are typical in this regard and are representative of the viral subfamily Alphaherpesvirinae, which has a tropism for neuronal and epithelial cells. This review will emphasize recent progress in decoding the physiologically important CD8(+) and CD4(+) T cell responses to HSV in humans. The expanding data set is discussed in the context of the search for an effective HSV vaccine as therapy for existing infections and to prevent new infections.

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.