Study of SHMT2 Inhibitors and Their Binding Mechanism by Computational Alanine Scanning

Upregulated Expression of SHMT2 Predicts Poor Survival of Lung Adenocarcinoma

Backgrounds: Serine hydroxy methyltransferase 2 (SHMT2) exerts an essential function in the cellular serine/glycine biosynthesis and one-carbon metabolism. Accumulative evidence revealed that SHMT2 was involved in cancer initiation and development in several types of carcinomas such as glioma, intrahepatic cholangiocarcinoma and colorectal cancer. However, expression and role of SHMT2 in lung adenocarcinoma (LUAD) had not been fully investigated. Methods: Transcriptional information of SHMT2 was retrieved from TCGA database. mRNA and protein expression of SHMT2 were analyzed in LUAD tissues alongside adjacent normal lung tissues using quantitative RT-PCR and immunohistochemical staining. The prognostic significance of SHMT2 in LUAD was assessed through both univariate and multivariate statistical analyses. Results: SHMT2 was higher in LUAD tissues than that in adjacent lung tissues on transcriptional level, mRNA level, and protein level. Elevated SHMT2 protein levels were associated with increased tumor size, positive lymph node metastasis, and more advanced TNM stages. LUAD patients with high SHMT2 level had worse prognosis. Conclusions: Our research indicated that elevated SHMT2 expression is strongly linked to adverse clinical characteristics and poor prognosis in LUAD patients. Consequently, SHMT2 may represent a novel prognosis marker and a promising therapeutic target regarding the treatment of LUAD.

Mechanistic Insight into the Enantioselective Degradation of Esterase QeH to (R)/(S)-Quizalofop-Ethyl with Molecular Dynamics Simulation Using a Residue-Specific Force Field

The enantioselective mechanism of the esterase QeH against the two enantiomers of quizalofop-ethyl (QE) has been primitively studied using computational and experimental approaches. However, it is still unclear how the esterase QeH adjusts its conformation to adapt to substrate binding and promote enzyme-substrate interactions in the catalytic kinetics. The equilibrium processes of enzyme-substrate interactions and catalytic dynamics were reproduced by performing independent molecular dynamics (MD) runs on the QeH-(R)/(S)-QE complexes with a newly developed residue-specific force field (RSFF2C). Our results indicated that the benzene ring of the (R)-QE structure can simultaneously form anion-π and cation-π interactions with the side-chain group of Glu328 and Arg384 in the binding cavity of the QeH-(R)-QE complex, resulting in (R)-QE being closer to its catalytic triplet system (Ser78-Lys81-Tyr189) with the distances measured for the hydroxyl oxygen atom of the catalytic Ser78 of QeH and the carbonyl carbon atom of (R)-QE of 7.39 Å, compared to the 8.87 Å for (S)-QE, whereas the (S)-QE structure can only form an anion-π interaction with the side chain of Glu328 in the QeH-(S)-QE complex, being less close to its catalytic site. The computational alanine scanning mutation (CAS) calculations further demonstrated that the π-π stacking interaction between the indole ring of Trp351 and the benzene ring of (R)/(S)-QE contributed a lot to the binding stability of the enzyme-substrate (QeH-(R)/(S)-QE). These results facilitate the understanding of their catalytic processes and provide new theoretical guidance for the directional design of other key enzymes for the initial degradation of aryloxyphenoxypropionate (AOPP) herbicides with higher catalytic efficiencies.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

DPP-IV Inhibitory Peptides from Coix Seed Prolamins: Release, Identification, and Analysis of the Interaction between Key Residues and Enzyme Domains

Dipeptidyl peptidase IV (DPP-IV) inhibitory peptides can regulate type 2 diabetes by inhibiting the cleavage of glucagon-like peptide-1 and prolonging its half-life. The development of DPP-IV inhibitory peptides is still a hot topic. The primary structure of coix seed prolamins contains peptide sequence fragments that potentially inhibit DPP-IV; however, limited information is available regarding the extraction of peptides from coix seeds and the analysis of their conformational relationships. In this study, novel coix seed prolamin-derived peptides were obtained through single hydrolysis and double-enzyme stepwise hydrolysis. The inhibitory activity of these peptides against DPP-IV was evaluated to explore new functional properties of coix seeds. The results evidenced that the step-by-step enzymolysis (papain and alcalase) compared to single enzymolysis promoted the secondary structure disruption of the hydrolysates, enhanced the β-turn structure, significantly increased the content of peptides below 1 kDa, and exhibited a substantial increase in DPP-IV inhibitory activity (97% inhibition). Three nontoxic DPP-IV inhibitory peptides, namely, LPFYPN, TFFPQ, and ATFFPQ (IC50 = 70.24, 176.87, 268.31 μM), were isolated and identified. All three peptides exhibited strong interactions with DPP-IV (all KA values >103). LPFYPN exhibited competitive inhibition, while TFFPQ and ATFFPQ demonstrated mixed competitive-noncompetitive inhibition. Hydrogen bonding and hydrophobic interactions were the main contributors to the coix seed prolamin peptides binding to DPP-IV. The central residue was a key amino acid in the parent peptide sequence, forming a more stable π-π stacking with residues in the active pocket, which may facilitate peptide activity. This study provides theoretical support for the development of coix seed-derived hypoglycemic peptides.

Combined Antibodies Evusheld against the SARS-CoV-2 Omicron Variants BA.1.1 and BA.5: Immune Escape Mechanism from Molecular Simulation

The Omicron lineage of SARS-CoV-2, which was first reported in November 2021, has spread globally and become dominant, splitting into several sublineages. Experiments have shown that Omicron lineage has escaped or reduced the activity of existing monoclonal antibodies, but the origin of escape mechanism caused by mutation is still unknown. This work uses molecular dynamics and umbrella sampling methods to reveal the escape mechanism of BA.1.1 to monoclonal antibody (mAb) Tixagevimab (AZD1061) and BA.5 to mAb Cilgavimab (AZD8895), both mAbs were combined to form antibody cocktail, Evusheld (AZD7442). The binding free energy of BA.1.1-AZD1061 and BA.5-AZD8895 has been severely reduced due to multiple-site mutated Omicron variants. Our results show that the two Omicron variants, which introduce a substantial number of positively charged residues, can weaken the electrostatic attraction between the receptor binding domain (RBD) and AZD7442, thus leading to a decrease in affinity. Additionally, using umbrella sampling along dissociation pathway, we found that the two Omicron variants severely impaired the interaction between the RBD of SARS-CoV-2's spike glycoprotein (S protein) and complementary determining regions (CDRs) of mAbs, especially in CDR3H. Although mAbs AZD8895 and AZD1061 are knocked out by BA.5 and BA.1.1, respectively, our results confirm that the antibody cocktail AZD7442 retains activity against BA.1.1 and BA.5 because another antibody is still on guard. The study provides theoretical insights for mAbs interacting with BA.1.1 and BA.5 from both energetic and dynamic perspectives, and we hope this will help in developing new monoclonals and combinations to protect those unable to mount adequate vaccine responses.

Experimental and computational approaches to characterize a novel amidase that initiates the biodegradation of the herbicide propanil in Bosea sp. P5

The herbicide propanil and its major metabolite 3,4-dichloroaniline (3,4-DCA) are difficult to biodegrade and pose great health and environmental risks. However, studies on the sole or synergistic mineralization of propanil by pure cultured strains are limited. A two-strain consortium (Comamonas sp. SWP-3 and Alicycliphilus sp. PH-34), obtained from a swep-mineralizing enrichment culture that can synergistically mineralize propanil, has been previously reported. Here, another propanil degradation strain, Bosea sp. P5, was successfully isolated from the same enrichment culture. A novel amidase, PsaA, responsible for initial propanil degradation, was identified from strain P5. PsaA shared low sequence identity (24.0-39.7 %) with other biochemically characterized amidases. PsaA exhibited optimal activity at 30 °C and pH 7.5 and had k_cat and K_m values of 5.7 s^-1 and 125 μM, respectively. PsaA could convert the herbicide propanil to 3,4-DCA but exhibited no activity toward other herbicide structural analogs. This catalytic specificity was explained by using propanil and swep as substrates and then analyzed by molecular docking, molecular dynamics simulation and thermodynamic calculations, which revealed that Tyr138 is the key residue that affects the substrate spectrum of PsaA. This is the first propanil amidase with a narrow substrate spectrum identified, providing new insights into the catalytic mechanism of amidase in propanil hydrolysis.

Vital role of SHMT2 in diverse disease

One-carbon metabolism is essential for our human cells to carry out nucleotide synthesis, methylation, and reductive metabolism through one-carbon units, and these pathways ensure the high proliferation rate of cancer cells. Serine hydroxymethyltransferase 2 (SHMT2) is a key enzyme in one-carbon metabolism. This enzyme can convert serine into a one-carbon unit bound to tetrahydrofolate and glycine, ultimately supporting the synthesis of thymidine and purines and promoting the growth of cancer cells. Due to SHMT2's crucial role in the one-carbon cycle, it is ubiquitous in human cells and even in all organisms and highly conserved. Here, we summarize the impact of SHMT2 on the progression of various cancers to highlight its potential use in the development of cancer treatments.

Mutational Effect of Some Major COVID-19 Variants on Binding of the S Protein to ACE2

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has many variants that accelerated the spread of the virus. In this study, we investigated the quantitative effect of some major mutants of the spike protein of SARS-CoV-2 binding to the human angiotensin-converting enzyme 2 (ACE2). These mutations are directly related to the Variant of Concern (VOC) including Alpha, Beta, Gamma, Delta and Omicron. Our calculations show that five major mutations (N501Y, E484K, L452R, T478K and K417N), first reported in Alpha, Beta, Gamma and Delta variants, all increase the binding of the S protein to ACE2 (except K417N), consistent with the experimental findings. We also studied an additional eight mutations of the Omicron variant that are located on the interface of the receptor binding domain (RDB) and have not been reported in other VOCs. Our study showed that most of these mutations (except Y505H and G446S) enhance the binding of the S protein to ACE2. The computational predictions helped explain why the Omicron variant quickly became dominant worldwide. Finally, comparison of several different computational methods for binding free energy calculation of these mutants was made. The alanine scanning method used in the current calculation helped to elucidate the residue-specific interactions responsible for the enhanced binding affinities of the mutants. The results show that the ASGB (alanine scanning with generalized Born) method is an efficient and reliable method for these binding free energy calculations due to mutations.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Therapeutic targeting of the hypoxic tumour microenvironment

Hypoxia is prevalent in human tumours and contributes to microenvironments that shape cancer evolution and adversely affect therapeutic outcomes. Historically, two different tumour microenvironment (TME) research communities have been discernible. One has focused on physicochemical gradients of oxygen, pH and nutrients in the tumour interstitium, motivated in part by the barrier that hypoxia poses to effective radiotherapy. The other has focused on cellular interactions involving tumour and non-tumour cells within the TME. Over the past decade, strong links have been established between these two themes, providing new insights into fundamental aspects of tumour biology and presenting new strategies for addressing the effects of hypoxia and other microenvironmental features that arise from the inefficient microvascular system in solid tumours. This Review provides a perspective on advances at the interface between these two aspects of the TME, with a focus on translational therapeutic opportunities relating to the elimination and/or exploitation of tumour hypoxia.

Roles of Mitochondrial Serine Hydroxymethyltransferase 2 (SHMT2) in Human Carcinogenesis

In the last few years, cellular metabolic reprogramming has been acknowledged as a hallmark of human cancer and evaluated for its crucial role in supporting the proliferation and survival of human cancer cells. In a variety of human tumours, including hepatocellular carcinoma (HCC), breast cancer and non-small-cell lung cancer (NSCLC), a large amount of carbon is reused in serine/glycine biosynthesis, accompanied by higher expression of the key glycine synthetic enzyme mitochondrial serine hydroxymethyltransferase 2 (SHMT2). This enzyme can convert serine into glycine and a tetrahydrofolate-bound one-carbon unit, ultimately supporting thymidine synthesis and purine synthesis and promoting tumour growth. In tumour samples, elevated expression of SHMT2 was found to be associated with poor prognosis. In this review, the pivotal roles of SHMT2 in human carcinogenesis are described, highlighting the underlying regulatory mechanisms through promotion of tumour progression. In conclusion, SHMT2 may serve as a prognostic marker and a target for anticancer therapies.

On the Use of Interaction Entropy and Related Methods to Estimate Binding Entropies

Molecular mechanics combined with Poisson-Boltzmann or generalized Born and solvent-accessible area solvation energies (MM/PBSA and MM/GBSA) are popular methods to estimate the free energy for the binding of small molecules to biomacromolecules. However, the estimation of the entropy has been problematic and time-consuming. Traditionally, normal-mode analysis has been used to estimate the entropy, but more recently, alternative approaches have been suggested. In particular, it has been suggested that exponential averaging of the electrostatic and Lennard-Jones interaction energies may provide much faster and more accurate entropies, the interaction entropy (IE) approach. In this study, we show that this exponential averaging is extremely poorly conditioned. Using stochastic simulations, assuming that the interaction energies follow a Gaussian distribution, we show that if the standard deviation of the interaction energies (σIE) is larger than 15 kJ/mol, it becomes practically impossible to converge the interaction entropies (more than 10 million energies are needed, and the number increases exponentially). A cumulant approximation to the second order of the exponential average shows a better convergence, but for σIE > 25 kJ/mol, it gives entropies that are unrealistically large. Moreover, in practical applications, both methods show a steady increase in the entropy with the number of energies considered.

Quantitative analysis of ACE2 binding to coronavirus spike proteins: SARS-CoV-2 vs. SARS-CoV and RaTG13

The global outbreak of the COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bat virus RaTG13 and SARS-CoV are also members of the coronavirus family and SARS-CoV caused a world-wide pandemic in 2003. SARS-CoV-2, SARS-CoV and RaTG13 bind to angiotensin-converting enzyme 2 (ACE2) through their receptor-binding domain (RBD) of the spike protein. SARS-CoV-2 binds ACE2 with a higher binding affinity than SARS-CoV and RaTG13. Here we performed molecular dynamics simulation of these binding complexes and calculated their binding free energies using a computational alanine scanning method. Our MD simulation and hotspot residue analysis showed that the lower binding affinity of SARS-CoV to ACE2 vs. SARS-CoV-2 to ACE2 can be explained by different hotspot interactions in these two systems. We also found that the lower binding affinity of RaTG13 to ACE2 is mainly due to a mutated residue (D501) which resulted in a less favorable complex formation for binding. We also calculated an important mutation of N501Y in SARS-CoV-2 using both alanine scanning calculation and a thermodynamic integration (TI) method. Both calculations confirmed a significant increase of the binding affinity of the N501Y mutant to ACE2 and explained its molecular mechanism. The present work provides an important theoretical basis for understanding the molecular mechanism in coronavirus spike protein binding to human ACE2.

Overexpression of SHMT2 Predicts a Poor Prognosis and Promotes Tumor Cell Growth in Bladder Cancer

SHMT2 was overexpressed in many tumors, however, the role of SHMT2 in bladder cancer (BLCA) remains unclear. We first analyzed the expression pattern of SHMT2 in BLCA using the TNMplot, Oncomine, the Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Next, the association between SHMT2 expression and overall survival (OS)/disease-free survival (DFS) in BLCA patients were analyzed using TCGA and PrognoScan database. The correlation between SHMT2 expression and clinicopathology was determined using TCGA database. Furthermore, the genes co-expressed with SHMT2 and their underlying molecular function in BLCA were explored based on the Oncomine database, Metascape and gene set enrichment analysis (GSEA). Finally, the effects of SHMT2 on cell proliferation, cell cycle, and apoptosis were assessed using in vitro experiments. As a results, SHMT2 was significantly overexpressed in BLCA tissues and cells compared to normal bladder tissues and cells. A high SHMT2 expression predicts a poor OS of BLCA patients. In addition, SHMT2 expression was higher in patients with a high tumor grade and in those who were older than 60 years. However, the expression of SHMT2 was not correlated with gender, tumor stage, lymph node stage, and distant metastasis stage. Finally, overexpression of SHMT2 promoted BLCA cell proliferation and suppressed apoptosis, the silencing of SHMT2 significantly inhibited BLCA cell proliferation by impairing the cell cycle, and promoting apoptosis. SHMT2 mediates BLCA cells growth by regulating STAT3 signaling. In summary, SHMT2 regulates the proliferation, cell cycle and apoptosis of BLCA cells, and may act as a candidate therapeutic target for BLCA.

Analysis of the binding modes of the first- and second-generation antiandrogens with respect to F876L mutation

Androgen receptor (AR) is an important target for the treatment of prostate cancer, and mutations in the AR have an important impact on the resistance of existing drugs. In this work, we performed molecular dynamics simulations of the existing marketed antiandrogens flutamide, nilutamide, bicalutamide, enzalutamide, apalutamide, darolutamide, and its main metabolite ORM15341 in complex with the wild-type and F876L mutant AR. We calculated the residue-specific binding free energy contribution of the wild-type and mutant ARs with the AS-IE method and analyzed the hotspot residues and the binding free energy contributions of specific residues before and after the mutation. In addition, we analyzed the total binding obtained by adding residue binding energy contributions and compared the results with experimental values. The obtained residue-specific binding information should be very helpful in understanding the mechanism of drug resistance with respect to specific mutations and in the design of new generation drugs against possible new mutations.

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands

Androgen receptor (AR) is an important therapeutic target for the treatment of diseases such as prostate cancer, hypogonadism, muscle wasting, etc. In this study, the complex structures of the AR ligand-binding domain (LBD) with fifteen ligands were analyzed by molecular dynamics simulations combined with the alanine-scanning-interaction-entropy method (ASIE). The quantitative free energy contributions of the pocket residues were obtained and hotspot residues are quantitatively identified. Our calculation shows that that these hotspot residues are predominantly hydrophobic and their interactions with binding ligands are mainly van der Waals interactions. The total binding free energies obtained by summing over binding contributions by individual residues are in good correlation with the experimental binding data. The current quantitative analysis of binding mechanism of AR to ligands provides important insight on the design of future inhibitors.

Computational and Experimental Approaches to Decipher the Binding Mechanism of General Odorant-Binding Protein 2 from Athetis lepigone to Chlorpyrifos and Phoxim

Insect resistance to insecticides is an increasingly serious problem, and the resistant mechanisms are complicated. The resistance research based on the chemosensory pathway is one of the hot problems at present, but the specific binding mechanism of chemosensory genes and insecticides remains elusive. The binding mechanism of AlepGOBP2 (belong to insect chemosensory gene) with two insecticides was investigated by computational and experimental approaches. Our calculation results indicated that four key residues (Phe12, Ile52, Ile94, and Phe118) could steadily interact with these two insecticides and be assigned as hotspot sites responsible for their binding affinities. The significant alkyl-π and hydrophobic interactions involved by these four hotspot residues were found to be the driving forces for their binding affinities, especially for two residues (Phe12 and Ile94) that significantly contribute to the binding of chlorpyrifos, which were also validated by our binding assay results. Furthermore, we also found that the AlepGOBP2-chlorpyrifos/phoxim complexes can be more efficiently converged in the residue-specific force field-(RSFF2C) and its higher accuracy and repeatability in protein dynamics simulation, per-residue free energy decomposition, and computational alanine scanning calculations have also been achieved in this paper. These findings provided useful insights for efficient and reliable calculation of the binding mechanism of relevant AlepGOBPs with other insecticides, facilitating to develop new and efficient insecticides targeting the key sites of AlepGOBP2.

Identification of three new compounds that directly target human serine hydroxymethyltransferase 2

Mitochondrial serine hydroxymethyltransferase 2 (SHMT2) is an important drug target in the one-carbon metabolic pathway, since its activity is critical for purine and pyrimidine biosynthesis. Additionally, it plays a prominent role during metabolic reprogramming of cancer cells, and SHMT2 inhibitors have proven useful as anticancer drugs. Compared to drugs targeting one-carbon metabolic enzymes (mainly dihydrofolate reductase and thymidylate synthase) that have been used for clinical treatment of cancer, efficient SHMT2-specific inhibitors are lacking. Therefore, we established a direct system for virtual screening, protein expression, and identification of inhibitors targeting SHMT2. First, 27 compounds qualifying as potential SHMT2 inhibitors were selected for biological activity verification through virtual screening of the 210 thousand compounds registered in the Specs database. Second, these 27 hits were subjected to quick screening by an in vitro non-competitive kinetic assay of SHMT2 single-enzyme catalysis. This allowed us to identify three compounds featuring medium-strength and non-competitive inhibition of SHMT2: AM-807/42004511 (IC₅₀  = 14.52 ± 4.1665 μM), AM-807/40675298 (IC₅₀  = 12.74 ± 5.8991 μM), and AM-807/42004633 (IC₅₀  = 9.43 ± 0.5646 μM). We describe a quick screening method for the identification of inhibitors targeting SHMT2, providing a basis for subsequent identification and screening of new inhibitors.

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.