A quality metric for homology modeling: the H-factor

Drug Discovery Targeting Nuclear Receptor Binding SET Domain Protein 2 (NSD2)

Nuclear receptor binding SET domain proteins (NSDs) catalyze the mono- or dimethylation of histone 3 lysine 36 (H3K36me1 and H3K36me2), using S-adenosyl-l-methionine (SAM) as a methyl donor. As a key member of the NSD family of proteins, NSD2 plays an important role in the pathogenesis and progression of various diseases such as cancers, inflammations, and infectious diseases, serving as a promising drug target. Developing potent and specific NSD2 inhibitors may provide potential novel therapeutics. Several NSD2 inhibitors and degraders have been discovered while remaining in the early stage of drug development. Excitingly, KTX-1001, a selective NSD2 inhibitor, has entered clinical trials. In this Perspective, the structures and functions of NSD2, its roles in various human diseases, and the recent advances in drug discovery strategies targeting NSD2 have been summarized. The challenges, opportunities, and future directions for developing NSD2 inhibitors and degraders are also discussed.

Evaluation of the Antioxidant Properties of Carvacrol as a Prospective Replacement for Crude Essential Oils and Synthetic Antioxidants in Food Storage

The phenolic structural analogues of synthetic antioxidants such as butylated hydroxytoluene (BHT) in essential oils have been reported to exhibit antioxidant properties. Additionally, their lipophilicity makes them suitable for use in lipid-rich foods. This study evaluated the antioxidant capacity of carvacrol, a monoterpenoid antioxidant compound in the Monodora myristica (Gaertn.) seed essential oil, compared to the seed essential oil and BHT. In vitro studies (ferric reducing antioxidant power (FRAP), metal chelating activity (MCA), and nitric oxide scavenging activity (NOSA)) were conducted to ascertain if the antioxidant capacity of carvacrol was comparable to that of the seed essential oil. The potential binding affinity and molecular interactions between carvacrol and lipoxygenase (LOX) and its homologous model were investigated in silico. The molecular docking was performed using Autodock Vina, and the best poses were subjected to molecular dynamics simulation. The IC₅₀ for MCA and NOSA were: carvacrol 50.29 µL/mL, seed essential oil (SEO) 71.06 µL/mL; and carvacrol 127.61 µL/mL, SEO 165.18 µL/mL, respectively. The LOX model was Ramachandran favoured (97.75%) and the overall quality factor in the ERRAT plot was 95.392. The results of the molecular docking and molecular dynamics simulations revealed that lipoxygenase has a higher affinity (-22.79 kcal/mol) for carvacrol compared to BHT. In the LOX-BHT and LOX-carvacrol complexes, the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and the radius of gyration (RoG) were not significantly different, indicating similar molecular interactions. The results obtained from this study suggest that carvacrol exhibits an antioxidant capacity that may be explored as an alternative for crude essential oils and synthetic compounds during the storage of lipid-rich foods.

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Molecular modelling of coat protein of the Groundnut bud necrosis tospovirus and its binding with Squalene as an antiviral agent: In vitro and in silico docking investigations

Bud blight disease caused by groundnut bud necrosis virus (GBNV) is a serious constraint in the cultivation of agricultural crops such as legumes, tomato, chilies, potato, cotton etc. Owing to the significant damage caused by GBNV, an attempt was made to identify suitable organic antiviral agents through molecular modelling of the nucleocapsid Coat Protein of GBNV; molecular docking and molecular dynamics that disclosed the interaction of the ligands viz., Squalene and Ganoderic acid-A with coat protein of GBNV. Invitro inhibitory effect of Squalene and Ganoderic acid-A was examined in comparison with different concentrations, against GBNV in cowpea plants under glasshouse condition. The different concentrations of Squalene (50, 100, 150, 250 and 500 ppm) tested in vitro resulted in reduction of lesion numbers (1.69 cm²) as well as reduced virus titre in co-inoculation spray. The present study suggests the antiviral activity of Squalene by effectively fitting into binding site of coat protein of GBNV with favourable hydrophilic as well as strong hydrophobic interactions thereby challenging and blocking the binding of viral replication RNA with coat protein and propagation. The present organic antiviral molecules will be helpful in development of suitable eco-friendly formulations to mitigate GBNV infection disease in plants.

Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors

The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or "anchors" as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.

Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation

Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.

PRIMO: An Interactive Homology Modeling Pipeline

The development of automated servers to predict the three-dimensional structure of proteins has seen much progress over the years. These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling. During each stage of the modeling process, the site provides suggestions for novice users to improve the quality of their models. PRIMO provides functionality that allows users to also model ligands and ions in complex with their protein targets. Herein, we assess the accuracy of the fully automated capabilities of the server, including a comparative analysis of the available alignment programs, as well as of the refinement levels used during modeling. The tests presented here demonstrate the reliability of the PRIMO server when producing a large number of protein models. While PRIMO does focus on user involvement in the homology modeling process, the results indicate that in the presence of suitable templates, good quality models can be produced even without user intervention. This gives an idea of the base level accuracy of PRIMO, which users can improve upon by adjusting parameters in their modeling runs. The accuracy of PRIMO’s automated scripts is being continuously evaluated by the CAMEO (Continuous Automated Model EvaluatiOn) project. The PRIMO site is free for non-commercial use and can be accessed at https://primo.rubi.ru.ac.za/.

Inhibition of Nuclear Receptor Binding SET Domain 2/Multiple Myeloma SET Domain by LEM-06 Implication for Epigenetic Cancer Therapies

Background:

Multiple myeloma SET domain (MMSET)/nuclear receptor binding SET domain 2 (NSD2) is a lysine histone methyltransferase (HMTase) and bona fide oncoprotein found aberrantly expressed in several cancers, suggesting potential role for novel therapeutic strategies. In particular, MMSET/NSD2 is emerging as a target for therapeutic interventions against multiple myeloma, especially t(4;14) myeloma that is associated with a significantly worse prognosis than other biological subgroups. Multiple myeloma is the second most common hematological malignancy in the United States, after non-Hodgkin lymphoma and remains an incurable malignancy. Thus, effective therapeutic strategies are greatly needed. HMTases inhibitors are scarce and no NSDs inhibitors have been isolated.

Methods:

We used homology modeling, molecular modeling simulations, virtual ligand screening, computational chemistry software for structure-activity relationship and performed in vitro H3K36 histone lysine methylation inhibitory assay using recombinant human NSD2-SET and human H3.1 histone.

Results:

Here, we report the discovery of LEM-06, a hit small molecule inhibitor of NSD2, with an IC₅₀ of 0.8 mM against H3K36 methylation in vitro.

Conclusions:

We propose LEM-06 as a hit inhibitor that is useful to further optimize for exploring the biology of NSD2. LEM-06 derivatives may pave the way to specific NSD2 inhibitors suitable for therapeutic efforts against malignancies.

The H-factor as a novel quality metric for homology modeling

Background

Drug discovery typically starts with the identification of a potential target that is then tested and validated either through high-throughput screening against a library of drug compounds or by rational drug design. When the putative target is a protein, the latter approach requires the knowledge of its structure. Finding the structure of a protein is however a difficult task. Significant progress has come from high-resolution techniques such as X-ray crystallography and NMR; there are many proteins however whose structure have not yet been solved. Computational techniques for structure prediction are viable alternatives to experimental techniques for these cases. However, the proper validation of the structural models they generate remains an issue.

Findings

In this report, we focus on homology modeling techniques and introduce the H-factor, a new indicator for assessing the quality of protein structure models generated with these techniques. The H-factor is meant to mimic the R-factor used in X-ray crystallography. The method for computing the H-factor is fully described with a demonstration of its effectiveness on a test set of target proteins.

Conclusions

We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor.

Applications and limitations of in silico models in drug discovery

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.