Efficacy of the combination of monoclonal antibodies against the SARS-CoV-2 Beta and Delta variants.

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the biggest healthcare issue worldwide. This study aimed to develop a monoclonal antibody against SARS-CoV-2 from B cells of recovered COVID-19 patients, which might have beneficial therapeutic purposes for COVID-19 patients. We successfully generated human monoclonal antibodies (hmAbs) against the receptor binding domain (RBD) protein of SARS-CoV-2 using developed hybridoma technology. The isolated hmAbs against the RBD protein (wild-type) showed high binding activity and neutralized the interaction between the RBD and the cellular receptor angiotensin-converting enzyme 2 (ACE2) protein. Epitope binning and crystallography results displayed target epitopes of these antibodies in distinct regions beneficial in the mix as a cocktail. The 3D2 binds to conserved epitopes among multi-variants. Pseudovirion-based neutralization results revealed that the antibody cocktail, 1D1 and 3D2, showed high potency in multiple variants of SARS-CoV-2 infection. In vivo studies showed the ability of the antibody cocktail treatment (intraperitoneal (i.p.) administration) to reduce viral load (Beta variant) in blood and various tissues. While the antibody cocktail treatment (intranasal (i.n.) administration) could not significantly reduce the viral load in nasal turbinate and lung tissue, it could reduce the viral load in blood, kidney, and brain tissue. These findings revealed that the efficacy of the antibody cocktail, 1D1 and 3D2, should be further studied in animal models in terms of timing of administration, optimal dose, and efficacy to mitigate inflammation in targeted tissue such as nasal turbinate and lung.

PARP1pred: a web server for screening the bioactivity of inhibitors against DNA repair enzyme PARP-1.

Cancer is the leading cause of death worldwide, resulting in the mortality of more than 10 million people in 2020, according to Global Cancer Statistics 2020. A potential cancer therapy involves targeting the DNA repair process by inhibiting PARP-1. In this study, classification models were constructed using a non-redundant set of 2018 PARP-1 inhibitors. Briefly, compounds were described by 12 fingerprint types and built using the random forest algorithm concomitant with various sampling approaches. Results indicated that PubChem with an oversampling approach yielded the best performance, with a Matthews correlation coefficient > 0.7 while also affording interpretable molecular features. Moreover, feature importance, as determined from the Gini index, revealed that the aromatic/cyclic/heterocyclic moiety, nitrogen-containing fingerprints, and the ether/aldehyde/alcohol moiety were important for PARP-1 inhibition. Finally, our predictive model was deployed as a web application called PARP1pred and is publicly available at https://parp1pred.streamlitapp.com, allowing users to predict the biological activity of query compounds using their SMILES notation as the input. It is anticipated that the model described herein will aid in the discovery of effective PARP-1 inhibitors.

Clinical relevance of molecular testing methods in the diagnosis and guidance of therapy in patients with staphylococcal empyema: a systematic review and meta-analysis.

Background: Efficient detection tools for determining staphylococcal pleural infection are critical for its eradication. The objective of this meta-analysis was to assess the diagnostic utility of nucleic acid amplification tests (NAAT) in suspected empyema cases to identify staphylococcal strains and avoid unnecessary empiric methicillin-resistant Staphylococcus aureus (MRSA) therapy. Methods: From inception to July 24, 2021, relevant records were retrieved from PubMed, Embase, Scopus, Web of Science, and the Cochrane Library. The quality of studies was determined using the QUADAS-2 tool. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (HSROC) curve for NAAT's diagnostic performance were evaluated using an HSROC model. Results: Eight studies comprising 424 samples evaluated NAAT accuracy for Staphylococcus aureus (SA) identification, while four studies comprising 317 samples evaluated methicillin-resistant Staphylococcus aureus (MRSA) identification. The pooled NAAT summary estimates for detection of both SA (sensitivity: 0.35 (95% CI 0.19-0.55), specificity: 0.95 (95% CI 0.92-0.97), PLR: 7.92 (95% CI 4.98-12.59), NLR: 0.44 (95% CI 0.14-1.46), and DOR: 24.0 (95% CI 6.59-87.61) ) and MRSA (sensitivity: 0.45 (95% CI 0.15-0.78), specificity: 0.93 (95% CI 0.89-0.95), PLR: 10.06 (95% CI 1.49-67.69), NLR: 0.69 (95% CI 0.41-1.15), and DOR: 27.18 (95% CI 2.97-248.6) ) were comparable. The I2 statistical scores for MRSA and SA identification sensitivity were 13.7% and 74.9%, respectively, indicating mild to substantial heterogeneity. PCR was frequently used among NAA tests, and its diagnostic accuracy coincided well with the overall summary estimates. A meta-regression and subgroup analysis of country, setting, study design, patient selection, and sample condition could not explain the heterogeneity (meta-regression P = 0.66, P = 0.46, P = 0.98, P = 0.68, and P = 0.79, respectively) in diagnostic effectiveness. Conclusions: Our study suggested that the diagnostic accuracy of NAA tests is currently inadequate to substitute culture as a principal screening test. NAAT could be used in conjunction with microbiological culture due to the advantage of faster results and in situations where culture tests are not doable.

Pragmatic Applications and Universality of DNA Barcoding for Substantial Organisms at Species Level: A Review to Explore a Way Forward.

DNA barcodes are regarded as hereditary succession codes that serve as a recognition marker to address several queries relating to the identification, classification, community ecology, and evolution of certain functional traits in organisms. The mitochondrial cytochrome c oxidase 1 (CO1) gene as a DNA barcode is highly efficient for discriminating vertebrate and invertebrate animal species. Similarly, different specific markers are used for other organisms, including ribulose bisphosphate carboxylase (rbcL), maturase kinase (matK), transfer RNA-H and photosystem II D1-ApbsArabidopsis thaliana (trnH-psbA), and internal transcribed spacer (ITS) for plant species; 16S ribosomal RNA (16S rRNA), elongation factor Tu gene (Tuf gene), and chaperonin for bacterial strains; and nuclear ITS for fungal strains. Nevertheless, the taxon coverage of reference sequences is far from complete for genus or species-level identification. Applying the next-generation sequencing approach to the parallel acquisition of DNA barcode sequences could greatly expand the potential for library preparation or accurate identification in biodiversity research. Overall, this review articulates on the DNA barcoding technology as applied to different organisms, its universality, applicability, and innovative approach to handling DNA-based species identification.

ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors.

Alzheimer's disease (AD) is one of the most common forms of dementia and is associated with a decline in cognitive function and language ability. The deficiency of the cholinergic neurotransmitter known as acetylcholine (ACh) is associated with AD. Acetylcholinesterase (AChE) hydrolyses ACh and inhibits the cholinergic transmission. Furthermore, both AChE and butyrylcholinesterase (BChE) plays important roles in early and late stages of AD. Therefore, the inhibition of either or both cholinesterase enzymes represent a promising therapeutic route for treating AD. In this study, a large-scale classification structure-activity relationship model was developed to predict cholinesterase inhibitory activities as well as revealing important substructures governing their activities. Herein, a non-redundant dataset constituting 985 and 1056 compounds for AChE and BChE, respectively, was obtained from the ChEMBL database. These inhibitors were described by 12 sets of molecular fingerprints and predictive models were developed using the random forest algorithm. Evaluation of the model performance by means of Matthews correlation coefficient and consideration of the model's interpretability indicated that the SubstructureCount fingerprint was the most robust with five-fold cross-validated MCC of [0.76, 0.82] for AChE and BChE, respectively, and test MCC of [0.73, 0.97]. Feature interpretation revealed that the aromatic ring system, heterocyclic nitrogen containing compounds and amines are important for cholinesterase inhibition. Finally, the model was deployed as a publicly available webserver called the ABCpred at http://codes.bio/abcpred/ .

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

Clinical Utility of Molecular Tests for Guiding Therapeutic Decisions in Bloodstream Staphylococcal Infections: A Meta-Analysis.

Background: Treatment of bloodstream staphylococcal infections (BSI) necessitates the prompt initiation of appropriate antimicrobial agents and the rapid de-escalation of excessive broad-spectrum coverage to reduce the risk of mortality. We, therefore, aimed to demonstrate the diagnostic accuracy of nucleic acid amplification tests (NAAT) for the identification of methicillin-resistant S. aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) in clinically suspected patients. Methods: Until November 23, 2020, databases including PubMed, Scopus, Embase, and Web of Science were scanned for eligible studies. A bivariate random-effects model was used for meta-analysis of the 33 included studies obtained from 1606 citations, and pooled summary estimates with 95% confidence intervals (CI) were generated. Results: Twenty-three studies (n = 8,547) assessed NAAT accuracy for MSSA detection, while three studies (n = 479) evaluated MRSA detection in adults. The pooled NAAT sensitivity and specificity for MRSA in adults was higher [sensitivity: 0.83 (95% CI 0.59-0.96), specificity: 0.99 (95% CI 0.98-1.0)] as compared to MSSA [sensitivity: 0.76 (95% CI 0.69-0.82), specificity: 0.98 (95% CI 0.98-0.99)]. Similarly, eight studies (n = 4,089) investigating MSSA in pediatric population reported higher NAAT accuracy [sensitivity: 0.89 (95% CI 0.76-0.96), specificity: 0.98 (95% CI 0.97-0.98)] compared to adults. Among NAA tests, SeptiFast (real-time PCR, commercial) was frequently applied, and its diagnostic accuracy corresponded well to the overall summary estimates. A meta-regression and subgroup analysis of study design, sample condition, and patient selection method could not explain the heterogeneity (P > 0.05) in the diagnostic efficiency. Conclusions: NAAT could be applied as the preferred initial tests for timely diagnosis and BSI management.

ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists.

Estrogen receptors alpha and beta (ERα and ERß) are responsible for breast cancer metastasis through their involvement of clinical outcomes. Estradiol and hormone replacement therapy targets both ERs, but this often leads to an increased risk of breast and endometrial cancers as well as thromboembolism. A major challenge is posed for the development of compounds possessing ER subtype specificity. Herein, we present a large-scale classification structure-activity relationship (CSAR) study of inhibitors from the ChEMBL database which consisted of an initial set of 11,618 compounds for ERα and 7,810 compounds for ERß. The IC50 was selected as the bioactivity unit for further investigation and after the data curation process, this led to a final data set of 1,593 and 1,281 compounds for ERα and ERß, respectively. We employed the random forest (RF) algorithm for model building and of the 12 fingerprint types, models built using the PubChem fingerprint was the most robust (Ac of 94.65% and 92.25% and Matthews correlation coefficient (MCC) of 89% and 76% for ERα and ERß, respectively) and therefore selected for feature interpretation. Results indicated the importance of features pertaining to aromatic rings, nitrogen-containing functional groups and aliphatic hydrocarbons. Finally, the model was deployed as the publicly available web server called ERpred at http://codes.bio/erpred where users can submit SMILES notation as the input query for prediction of the bioactivity against ERα and ERß.

Production and characterization of antibody against Opisthorchis viverrini via phage display and molecular simulation.

In this study, a key issue to be addressed is the safe disposal of hybridoma instability. Hybridoma technology was used to produce anti-O. viverrini monoclonal antibody. Previous studies have shown that antibody production via antibody phage display can sustain the hybridoma technique. This paper presents the utility of antibody phage display technology for producing the phage displayed KKU505 Fab fragment and using experiments in concomitant with molecular simulation for characterization. The phage displayed KKU505 Fab fragment and characterization were successfully carried out. The KKU505 hybridoma cell line producing anti-O. viverrini antibody predicted to bind to myosin was used to synthesize cDNA so as to amplify the heavy chain and the light chain sequences. The KKU505 displayed phage was constructed and characterized by a molecular modeling in which the KKU505 Fab fragment and -O. viverrini myosin head were docked computationally and it is assumed that the Fab fragment was specific to -O. viverrini on the basis of mass spectrometry and Western blot. This complex interaction was confirmed by molecular simulation. Furthermore, the KKU505 displayed phage was validated using indirect enzyme-linked immunosorbent assays (ELISA) and immunohistochemistry. It is worthy to note that ELISA and immunohistochemistry results confirmed that the Fab fragment was specific to the -O. viverrini antigen. Results indicated that the approach presented herein can generate anti-O. viverrini antibody via the phage display technology. This study integrates the use of phage display technology together with molecular simulation for further development of monoclonal antibody production. Furthermore, the presented work has profound implications for antibody production, particularly by solving the problem of hybridoma stability issues.

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors.

Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure-activity relationship (CSAR) model for identifying substructures giving rise to anti-HCV activities among a set of 578 non-redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in-depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease.

The continual increase of the aging population worldwide renders Alzheimer's disease (AD) a global prime concern. Several attempts have been focused on understanding the intricate complexity of the disease's development along with the on- andgoing search for novel therapeutic strategies. Incapability of existing AD drugs to effectively modulate the pathogenesis or to delay the progression of the disease leads to a shift in the paradigm of AD drug discovery. Efforts aimed at identifying AD drugs have mostly focused on the development of disease-modifying agents in which effects are believed to be long lasting. Of particular note, the secretase enzymes, a group of proteases responsible for the metabolism of the ß-amyloid precursor protein (ßAPP) and ß-amyloid (Aß) peptides production, have been underlined for their promising therapeutic potential. This review article attempts to comprehensively cover aspects related to the identification and use of drugs targeting the secretase enzymes. Particularly, the roles of secretases in the pathogenesis of AD and their therapeutic modulation are provided herein. Moreover, an overview of the drug development process and the contribution of computational (in silico) approaches for facilitating successful drug discovery are also highlighted along with examples of relevant computational works. Promising chemical scaffolds, inhibitors, and modulators against each class of secretases are also summarized herein. Additionally, multitarget secretase modulators are also taken into consideration in light of the current growing interest in the polypharmacology of complex diseases. Finally, challenging issues and future outlook relevant to the discovery of drugs targeting secretases are also discussed.

Tackling the Antibiotic Resistance Caused by Class A ß-Lactamases through the Use of ß-Lactamase Inhibitory Protein.

ß-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. One of the major resistance mechanisms involves the production of ß-lactamase that hydrolyzes the ß-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule ß-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with ß-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum ß-lactamase inhibitors that sufficiently work against ß-lactamase producing bacteria. ß-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium Streptomyces spp. BLIPs bind and inhibit a wide range of class A ß-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on ß-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from ß-lactamase⁻BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.

Publisher Correction: Human single chain-transbodies that bound to domain-I of non-structural protein 5A (NS5A) of hepatitis C virus.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Identification and production of mouse scFv to specific epitope of enterovirus-71 virion protein-2 (VP2).

Enterovirus-71 (EV71) and coxsackievirus-A16 (CA16) frequently cause hand-foot-mouth disease (HFMD) epidemics among infants and young children. CA16 infections are usually mild, while EV71 disease may be fatal due to neurologic complications. As such, the ability to rapidly and specifically recognize EV71 is needed to facilitate proper case management and epidemic control. Accordingly, the aim of this study was to generate antibodies to EV71-virion protein-2 (VP2) by phage display technology for further use in specific detection of EV71. A recombinant peptide sequence of EV71-VP2, carrying a predicted conserved B cell epitope fused to glutathione-S-transferase (GST) (designated GST-EV71-VP2/131-160), was produced. The fusion protein was used as bait in in-solution biopanning to separate protein-bound phages from a murine scFv (MuscFv) phage display library constructed from an immunoglobulin gene repertoire from naïve ICR mice. Three phage-transformed E. coli clones (clones 63, 82, and 83) produced MuscFvs that bound to the GST-EV71-VP2/131-160 peptide. The MuscFv of clone 83 (MuscFv83), which produced the highest ELISA signal to the target antigen, was further tested. MuscFv83 also bound to full-length EV71-VP2 and EV71 particles, but did not bind to GST, full-length EV71-VP1, or the antigenically related CA16. MuscFv83 could be a suitable reagent for rapid antigen-based immunoassay, such as immunochromatography (ICT), for the specific detection and/or diagnosis of EV71 infection as well as epidemic surveillance.

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study.

Estrogen is an important component for the sustenance of normal physiological functions of the mammary glands, particularly for growth and differentiation. Approximately, two-thirds of breast cancers are positive for estrogen receptor (ERs), which is a predisposing factor for the growth of breast cancer cells. As such, ERα represents a lucrative therapeutic target for breast cancer that has attracted wide interest in the search for inhibitory agents. However, the conventional laboratory processes are cost- and time-consuming. Thus, it is highly desirable to develop alternative methods such as quantitative structure-activity relationship (QSAR) models for predicting ER-mediated endocrine agitation as to simplify their prioritization for future screening. In this study, we compiled and curated a large, non-redundant data set of 1231 compounds with ERα inhibitory activity (pIC50). Using comprehensive validation tests, it was clearly observed that the model utilizing the substructure count as descriptors, performed well considering two objectives: using less descriptors for model development and achieving high predictive performance (R Tr 2 = 0.94, Q CV 2 = 0.73, and Q Ext 2 = 0.73). It is anticipated that our proposed QSAR model may become a useful high-throughput tool for identifying novel inhibitors against ERα.

Human single chain-transbodies that bound to domain-I of non-structural protein 5A (NS5A) of hepatitis C virus.

A safe and broadly effective direct acting anti-hepatitis C virus (HCV) agent that can withstand the viral mutation is needed. In this study, human single chain antibody variable fragments (HuscFvs) to conserved non-structural protein-5A (NS5A) of HCV were produced by phage display technology. Recombinant NS5A was used as bait for fishing-out the protein bound-phages from the HuscFv-phage display library. NS5A-bound HuscFvs produced by five phage transfected-E. coli clones were linked molecularly to nonaarginine (R9) for making them cell penetrable (become transbodies). The human monoclonal transbodies inhibited HCV replication in the HCVcc infected human hepatic cells and also rescued the cellular antiviral immune response from the viral suppression. Computerized simulation verified by immunoassays indicated that the transbodies used several residues in their multiple complementarity determining regions (CDRs) to form contact interface with many residues of the NS5A domain-I which is important for HCV replication complex formation and RNA binding as well as for interacting with several host proteins for viral immune evasion and regulation of cellular physiology. The human monoclonal transbodies have high potential for testing further as a new ramification of direct acting anti-HCV agent, either alone or in combination with their cognates that target other HCV proteins.

Molecularly imprinted polymer for human viral pathogen detection.

Molecular imprinting has become an attractive synthetic approach for the fabrication of novel functional polymers with pre-designed molecular target selectivity. Such molecularly imprinted polymers (MIPs) have been applied in wide range of areas such as chemical and biological sensors, solid phase extraction and drug assays owing to their inherent robustness, reusability and reproducibility. Furthermore, MIPs can also be used as tools for studies concerning antibody/receptor binding site mimicry as well as being used as antibody substitutes for biomedical applications. Viral detection is a rapidly growing field owing to its increasing prevalence and ongoing evolution of viral variants and drug resistance. Therefore, this calls for effective detection, surveillance and control. Herein, we highlight and summarize the literature on the utilization of MIPs for human virus detection. Particularly, MIPs afford great potential for rapid virus detection as well as other recognition-based viral studies.

Exploring the epigenetic drug discovery landscape.

INTRODUCTION: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.

Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.

HemoPred: a web server for predicting the hemolytic activity of peptides.

AIM: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates. MATERIALS & METHODS: This study describes a sequence-based predictor based on a random forest classifier using amino acid composition, dipeptide composition and physicochemical descriptors (named HemoPred). RESULTS: This approach could outperform previously reported method and typical classification methods (e.g., support vector machine and decision tree) verified by fivefold cross-validation and external validation with accuracy and Matthews correlation coefficient in excess of 95% and 0.91, respectively. Results revealed the importance of hydrophobic and Cys residues on α-helix and ß-sheet, respectively, on the hemolytic activity. CONCLUSION: A sequence-based predictor which is publicly available as the web service of HemoPred, is proposed to predict and analyze the hemolytic activity of peptides.